DOHA: Oil prices steadied on Friday amid uncertainty surrounding a potential peace deal between Russia and Ukraine, with prices gaining on the week for the first time in three weeks.
Brent crude futures settled up 6 cents or 0.09% to $67.73.
West Texas Intermediate (WTI) crude futures settled up 14 cents or 0.22% to $63.66.
Both contracts gained more than 1% in the previous session.
Brent gained 2.9% last week while WTI rose 1.4%.
US President Donald Trump said on Friday he will see if Russian President Vladimir Putin and Ukraine President Volodymyr Zelenskiy will work together in ending Russia’s war in Ukraine.
According to report by Al-Attiyah Foundation, the 3-1/2-year war continued unabated last week as Russia launched an air attack on Thursday near Ukraine’s border with the European Union, and Ukraine said it hit a Russian oil refinery and the Unecha oil pumping station, a critical part of Russia’s Europe-bound Druzhba oil pipeline.
Russian oil supplies to Hungary and Slovakia could be suspended for at least five days.
Meanwhile, US and European planners have presented military options to their national security advisers after the first in-person meeting between the US and Russian leaders since Russia invaded Ukraine.
Oil prices were also supported by a larger-than-expected drawdown from US crude stockpiles in the past week, indicating strong demand.
Stocks fell by 6 million barrels last week, the US Energy Information Administration said.
Asian spot liquefied natural gas (LNG) prices were slightly down last week on high storage inventories, continued weak demand and lack of progress on peace talks for Ukraine.
The average LNG price for October delivery into north-east Asia was at $11.40 per million British thermal units (mmBtu), down from $11.65 per mmBtu last week, industry sources estimated.
Analysts expect further downside to Asian LNG prices, as storage levels remain elevated, while the supply picture continues to firm.
Although Japan’s summer heat continues, demand for November heating is lagging.
Meanwhile, China is leaning more heavily on domestic gas and pipeline imports, reducing reliance on spot LNG and South Korea is well-stocked, exerting further downside pressure.
Moreover, some national oil companies (NOCs) in China were re-offering cargoes, while higher stocks limited injection demand, and strong hydro generation in Guangdong weighed on gas generation economics.
In Europe, gas prices steadied on Friday around firmer levels reached in the previous session, as attention turns to upcoming heavy maintenance in Norway and gas storage filling needs before the winter.
LNG imports into the continent remain healthy with expectations for an uptick in procurement of the super-chilled fuel ahead of the heating season.
The futures price at the Dutch TTF hub settled at $11.47 per mmBtu, recording a weekly gain of more than 8%.
Ether’s price has climbed 25% since the beginning of August, but historical data suggests the cryptocurrency could lose steam in September.
Only time will tell if Ether (ETH) plays out differently this year, with billions flowing into spot Ether ETFs and treasury companies.
Crypto trader CryptoGoos said in an X post on Friday, “ETH seasonality in September during post-halving years is typically negative. Will this time be different?”
Ether is trading at $4,759 at the time of publication, up roughly $952 from its Aug. 1 opening price of $3,807, according to CoinMarketCap. The crypto asset crossed new highs above $4,867 on Friday following dovish comments from US Federal Reserve Chair Jerome Powell at the Jackson Hole symposium.
History suggests caution for Ether during September
Powell hinted at a possible interest rate cut next month, which many in the crypto market view as a potential bullish catalyst.
However, history suggests caution for Ether as there have only been three instances since 2016 where Ether posted gains in August, and each was followed by a September decline, according to CoinGlass.
Since September 2016, Ether has delivered an average loss of 6.42%. Source: CoinGlass
In 2017, Ether surged 92.86% in August before dropping 21.65% the next month.
The pattern repeated in 2020, with a 25.32% gain in August followed by a 17.08% pullback in September. In 2021, Ether climbed 35.62% in August before slipping 12.55% in September.
Ether gained in the final three months of 2016 and 2020
Interestingly, even though September saw losses in 2016 and 2020, Ether posted upside in each of the following three months in both years.
However, this September could play out differently from previous years, with spot Ether ETFs and Ether treasury companies present, which were not active during past August rallies.
On Aug. 11, the total Ether held by companies with crypto treasuries surpassed $13 billion in value, as the cryptocurrency’s price surged past $4,300.
Source: Satoshi Stacker
On Saturday, blockchain intelligence firm Arkham reported that BitMine chairman Tom Lee bought another $45 million worth of Ether for the firm, bringing BitMine’s total stack up to $7 billion.
August has been a significant month for spot Ether ETFs
Meanwhile, spot Ether ETFs have seen roughly $2.79 billion net inflows in August alone, while spot Bitcoin (BTC) ETFs posted approximately $1.2 billion in net outflows over the same period, according to Farside.
Related: ETH data and return of investor risk appetite pave path to $5K Ether price
NovaDius Wealth Management president Nate Geraci said in a post on Saturday that there has been a “notable shift” in the inflows between spot Ether ETFs and spot Bitcoin ETFs.
Meanwhile, Bitcoin dominance, which measures its overall market share, has fallen 5.88% over the past 30 days to 58.19%, which many market participants typically attribute to capital rotating into the broader crypto market outside of BTC.
Magazine: ETH ‘god candle,’ $6K next? Coinbase tightens security: Hodler’s Digest, Aug. 17 – 23
Ros Rippon and Marie Caston said they spend a lot of time together outside of work including with each other’s families and called themselves “inseparable”
Two long-serving members of the critical care team at an NHS trust have been honoured for an incredible four decades of dedication to patient care.
Sisters Marie Caston and Ros Rippon, who both began their careers in 1985 at Peterborough City Hospital, are being celebrated by the North West Anglia NHS Foundation Trust for their combined 80 years of service.
The pair first met as young trainees, living opposite each other in the nurses’ home and became close while working together in the Intensive Care Unit — a friendship which has endured.
“They’d get our names mixed up all the time and only believed there were two of us once they saw us together,” they recalled.
NWAFT
A ceremony was held where the pair was thanked by the North West Anglia Foundation NHS Trust chairman Prof Steve Barnett OBE
Reflecting on their long careers, Ms Rippon said: “The sedation, equipment, and well, everything has changed.
“The care is much more specialised, and we now have 24-hour critical care consultants, whereas before it might have been a theatre consultant. More specialist care is available now, which is better.
“Every day is different, and there is always something to smile and laugh about.”
Ms Caston agreed with that sentiment: “Patients. Patients definitely. And all of the people we have met – we’re still in touch with the staff we’ve worked with over the years.”
NWAFT
Marie Caston and Ros Rippon have been working at Peterborough City Hospital since 1985
The nurses shared admiration for the next generation of critical care nurses and reassured them that entering the profession was worth it.
“The new nurses are amazing – they know so much and do so much work. The pressure they are under is immense,” the pair agreed.
“It can often be really difficult, and you might not love it straight away, but if you stick it out it can be incredibly rewarding.”
Ms Rippon was also involved and initiated the critical care staff garden opened recently at the hospital, offering respite and peace.
A fox that was trapped in a fence and left hanging by its front legs has been rescued by four volunteers.
The male cub was discovered stuck between a fence and its concrete pillar and was set free with help from Fox Angles Foundation helpers, on Thursday in Leyton, east London.
He was taken to the South Essex Wildlife Hospital in Orsett, for X-rays, the charity said.
Vet Alda nursed him for a wound in his rump, cleaned up him and he was responding well to the treatment, it confirmed.
On Monday, another fox was freed and treated by the same animal hospital after he became trapped in a letterbox of a house in Hadleigh, near Southend.
The hospital said the “entrapment shows just how dangerous small gaps in fencing panels can be to wildlife”.
“With fox dispersal season now under way, these youngsters are still learning the world and need a little help to give them the best chance,” it said.
“If any of your fences have a narrow ‘V’-shaped gap, please do everything you can to fix it or barricade access.
This section presents a model for energy management of distributed generation sources in microgrids, which reduces energy supply costs. Therefore, the artificial bee colony optimization algorithm has been used for optimization. The goal of optimal use of resources in microgrids is to reduce operating costs. In addition to ABC algorithm, the Lightning Search Algorithm (LSA), Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), and Modified Bat Algorithm (MBA) are used for optimization. It should be noted that all these algorithms were used in13. Studies have been repeated for four different radiation conditions: warm sunny, cold sunny, warm cloudy, and cold cloudy. In Figs. 5, 6 and 7, the results are shown as constant radiation in terms of global HZ irradiance (GHI), direct normal irradiation (DNI), and different seasons (Diff).
Fig. 5
Intensity of solar radiation in GHI conditions.
Fig. 6
Intensity of solar radiation in DNI conditions.
Fig. 7
Intensity of solar radiation in Diff conditions.
Next, in Fig. 8, the ambient temperature is shown in different conditions. Figure 9 illustrates the amount of electrical power requested in the microgrid for 24 h for four different situations.
Fig. 8
Ambient temperature in different conditions.
Fig. 9
The first scenario: (optimization results for a hot sunny day)
In the initial phase of the optimization, we focused on minimizing the operating costs of the microgrid under the conditions of a hot, sunny day. To achieve this objective, the optimization algorithms are employed to determine the most cost-effective power generation schedule for the microgrid during the specified period. It is important to note that each algorithm was executed 50 times to ensure statistical reliability, and the corresponding results are summarized in Table 2.
Table 2 Optimization results in the first scenario.
The results indicate that all the employed optimization algorithms produced relatively similar outcomes, with only slight differences in their objective function values. Nonetheless, even small variations in operational cost can accumulate to significant amounts over extended periods. For the ABC algorithm, the minimum and maximum values of the objective function were recorded at $363.21 and $391.17, respectively. In comparison, the GA yielded a minimum of $367.69 and a maximum of $380.34. A notable finding is that the standard deviation of the ABC algorithm was lower than that of the other optimization methods. A lower standard deviation suggests that the algorithm delivers more stable and accurate performance, reducing the likelihood of converging on an optimal solution by chance. Specifically, the standard deviation for the ABC algorithm was calculated to be 11.16, demonstrating greater consistency and reliability. In terms of reliability index, all algorithms achieved values below 2%. However, the ABC algorithm attained a reliability index of 1.45%, which is the lowest among the tested algorithms, further confirming its superior performance. Following optimization with the ABC algorithm, the power generation levels for each distributed energy resource were determined. If the output power of the distributed generation units is scheduled according to Figure (10), the total energy supply cost within the microgrid will be minimized under this scenario.
As anticipated, during peak electricity price hours, the electrical demand is primarily met by the distributed generation units. Conversely, during periods of low electricity prices, the utility grid supplies the required load power. Figures (11) illustrates the variation in the electricity price exchanged with the grid, as well as the battery’s charging and discharging profile, when optimized using the Artificial Bee Colony (ABC) algorithm.
The second scenario: (optimization results for a cold sunny day)
In the second scenario of the simulations, the cost reduction of using resources in the microgrid for a chilly summer day has been studied. As in the first scenario, optimization algorithms were used in new conditions to reduce costs. Algorithms have been executed 50 times; Table 3 shows the results.
Table 3 Optimization results in the second scenario.
Compared to the first scenario, a significant reduction in operational costs was observed, primarily due to decreased energy consumption. The best solution obtained using the proposed ABC algorithm was $292.86, while the optimal result from multiple executions of the GA reached $297.83. This indicates that the ABC algorithm achieved approximately $5 in cost savings over the GA method for microgrid optimization. In this scenario, a higher standard deviation was observed, suggesting greater dispersion in the optimization outcomes. However, the reliability index for the microgrid improved compared to the first scenario, indicating a reduction in the level of unsupplied energy. Specifically, the LPSP index was recorded at 1.23% for the ABC algorithm, consistent with the values obtained from other algorithms. After applying the ABC optimization algorithm, the generation power levels of each distributed energy source and the amount of electric power exchanged with the national grid were determined. Figure 10 illustrates the contribution of each distributed generation source in the second scenario.
Fig. 10
The contribution of each distributed generation source in the second scenario.
Similar to the first scenario, energy supply during peak electricity price hours was primarily managed using lower-cost distributed generation sources. During off-peak hours, the national grid provided a substantial portion of the microgrid’s load demand. The analysis of the second scenario also includes the hourly profile of electricity prices exchanged with the national grid and the battery storage’s charge/discharge behavior under optimization by the ABC algorithm. These results further confirm the algorithm’s ability to effectively manage energy resources while minimizing operational costs. Figure 11 shows the electricity price and battery power at different hours in the second scenario.
Fig. 11
Electricity price and battery power at different hours in the second scenario.
The third scenario: (optimization results for a hot cloudy day)
Continuing the process of simulations, in the third scenario, reducing the cost of using resources in the microgrid for a hot, cloudy day has been studied. Like the previous two scenarios, genetic algorithms, particle swarm, artificial bee, modified fake bee, lightning search, and the proposed artificial bee colony algorithm were used for optimization. The results of running the algorithm 50 times are given in Table 4.
Table 4 Optimization results in the third scenario.
The optimal, worst, and average values of the objective function obtained using the proposed ABC algorithm were $267.77, $300.38, and $289.61, respectively, with a standard deviation of $14.21. These results demonstrate superior performance compared to other optimization algorithms, indicating the consistency and reliability of the ABC algorithm in minimizing operational costs. In this scenario, the value of the LPSP reliability index increased, primarily due to reduced photovoltaic power generation caused by cloud cover and decreased solar irradiance, coupled with increased energy demand. When using the ABC algorithm, the LPSP index reached 1.76%. The hourly distribution of generation power for each distributed energy source and storage system, as well as the power exchanged with the national grid, is represented as a bar graph (Fig. 12). Notably, during high-cost electricity hours, the average exchanged power with the national grid was negative, indicating that electricity was sold to the grid, resulting in increased profitability for microgrid operators.
Fig. 12
The contribution of each distributed generation source in the third scenario.
Conversely, during low-cost electricity periods, such as early morning hours, a significant portion of the microgrid’s demand was met through purchases from the national grid. Additionally, batteries were primarily charged during these cheaper periods and discharged when electricity prices were higher, enhancing the overall economic performance of the system. Figure 13 shows the electricity price and battery power at different hours in the third section.
Fig. 13
Electricity price and battery power at different hours in the third scenario.
The fourth scenario: (optimization results for a cold, cloudy day)
Finally, in the fourth scenario of the simulations, the operation cost reduction in the studied microgrid for a cold, cloudy day has been done. Like the previous three scenarios, the optimization algorithms were executed 50 times, and their results are summarized in Table 5.
Table 5 Optimization results in the fourth scenario.
Following optimization using the ABC algorithm, the best result across 50 independent runs was recorded at $266.14, while the worst solution reached $340.83. For comparison, the best solutions obtained using other algorithms, GA, PSO, standard ABC, Improved Fake Bee Algorithm, and the LSA, were calculated as $271.51, $269.88, $268.17, $267.00, and $267.11, respectively. These findings highlight the superior performance of the proposed ABC method in minimizing microgrid operational costs. In this scenario, the reliability index, measured by the LPSP, remained below the acceptable threshold of 2% for all algorithms. However, the LPSP value resulting from the ABC algorithm was calculated at 1.61%, which is lower than all other methods tested, indicating better reliability and energy supply security. The hourly distribution of generation power from distributed energy resources, the battery’s charging and discharging behavior, and the energy exchanged with the national grid were also evaluated under ABC optimization. Figure 14 illustrates the contribution of each distributed generation source in the fourth section.
Fig. 14
The contribution of each distributed generation source in the fourth scenario.
As anticipated, during periods of high electricity prices, local distributed generation units were primarily responsible for supplying the load. Conversely, during hours with lower electricity prices, the majority of the microgrid’s energy demand was met through imports from the grid. Subsequently, the variation in electricity exchange prices and the battery’s charging/discharging profile under ABC optimization are illustrated, confirming an economically and operationally efficient energy management strategy. Figure 15 shows the electricity price and battery power at different hours in the fourth scenario.
Fig. 15
Electricity price and battery power at different hours in the fourth scenario.
Trade-off between operational cost and system reliability
The Artificial Bee Colony (ABC) algorithm effectively balances the trade-off between minimizing operational costs and ensuring system reliability in microgrid energy management. This is achieved through a penalty-based objective function, as defined in Eqs. (1)–(3), which minimizes costs related to fuel consumption, start-up, energy exchange with the national grid, and penalties for unserved load, while enforcing reliability constraints, specifically maintaining the Loss of Power Supply Probability (LPSP) below 2%. Candidate solutions that violate reliability constraints are penalized, guiding the algorithm toward solutions that optimize cost while ensuring reliable energy supply. The ABC algorithm’s adaptive mechanisms, including the waggle dance-inspired information sharing and scout bee exploration, enable efficient navigation of the solution space, avoiding local optima and ensuring consistent performance.
The performance of the ABC algorithm in managing this trade-off is evaluated across four operational scenarios (hot sunny, cold sunny, hot cloudy, and cold cloudy), with results compared against other metaheuristic methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Modified Bat Algorithm (MBA), and Lightning Search Algorithm (LSA). Table 6 summarizes the best operational cost and corresponding LPSP values for each algorithm across all scenarios, highlighting the ABC algorithm’s superior ability to achieve lower costs while maintaining high reliability.
Table 6 Comparison of operational cost and reliability across Scenarios.
As shown in Table 6, the ABC algorithm consistently achieves the lowest operational costs across all scenarios (e.g., $363.21 in Scenario 1, $292.86 in Scenario 2, $267.77 in Scenario 3, and $266.14 in Scenario 4) while maintaining the lowest LPSP values (1.45%, 1.23%, 1.76%, and 1.61%, respectively). This demonstrates its ability to optimize the cost-reliability trade-off effectively. For example, in Scenario 3 (hot cloudy day), where reduced photovoltaic generation due to cloud cover increases LPSP, the ABC algorithm still achieves the lowest cost ($267.77) and an LPSP of 1.76%, well within the acceptable limit of 2%. In contrast, GA and PSO yield higher costs ($273.18 and $271.56) and higher LPSP values (1.99% and 1.89%), indicating a less favorable trade-off.
The ABC algorithm’s superior performance can be attributed to its robust exploration and exploitation mechanisms. Unlike GA, which may converge to local optima due to its crossover and mutation operations, or PSO, which can be sensitive to parameter tuning, the ABC algorithm leverages a simpler structure with fewer control parameters. The scout bee phase ensures diversity in the solution space, while the onlooker bee phase refines promising solutions, leading to lower standard deviations (e.g., 16.11 in Scenario 1 vs. 19.85 for GA) and more consistent results. Compared to MBA and LSA, which perform well but achieve slightly higher costs and LPSP values, the ABC algorithm’s adaptive information-sharing mechanism (via the waggle dance) enhances its ability to balance cost and reliability effectively.
These results confirm that the ABC algorithm outperforms other metaheuristic methods in managing the trade-off between operational cost and system reliability, making it a robust and efficient tool for microgrid energy management under diverse operating conditions.
Performance of the ABC algorithm across different time horizons
The performance of the Artificial Bee Colony (ABC) algorithm in microgrid energy management varies depending on the scheduling time horizon—hourly, daily, or weekly—due to differences in computational requirements, data resolution, and the handling of uncertainties. The current study employs a daily (24-hour) scheduling horizon, optimizing the allocation of distributed energy resources (DERs) and battery storage on an hourly basis within a single day. This section evaluates how the ABC algorithm’s performance, in terms of computational time, operational cost, and system reliability (measured by Loss of Power Supply Probability, LPSP), is expected to vary across hourly, daily, and weekly scheduling horizons, based on the simulation results and typical algorithm behavior.
Hourly Scheduling: In an hourly scheduling framework, the ABC algorithm optimizes resource allocation for each hour independently or in short time blocks, using high-resolution forecasts of load, solar irradiance, and wind speed. This approach allows precise control of DERs and battery storage, enabling the algorithm to respond dynamically to short-term variations. However, frequent re-optimization increases computational demand, potentially leading to higher simulation times (e.g., estimated at 10–20 s per hour based on the daily simulation times of 84–125 s for 24 h). While operational costs and reliability remain comparable to daily scheduling (e.g., LPSP < 2%), the need for rapid computation may limit real-time applicability in large microgrids.
Daily Scheduling: The current study uses a daily scheduling horizon, as detailed in Sect. 6.1–6.4, where the ABC algorithm optimizes a 24-hour schedule based on hourly forecasts. This approach balances computational efficiency and solution quality, achieving operational costs of $266.14–$363.21 and LPSP values of 1.23–1.76% across the four scenarios (Tables 2, 3, 4 and 5). The daily horizon leverages a full day’s forecast, reducing the impact of short-term uncertainties while maintaining manageable computational times (84–125 s). This makes it well-suited for both planning and near-real-time applications in the tested benchmark microgrid.
Weekly Scheduling: For a weekly scheduling horizon (168 h), the ABC algorithm optimizes resource allocation over an extended period, requiring forecasts for load, solar, and wind profiles over seven days. While this enables strategic planning (e.g., for maintenance scheduling or energy trading), it significantly increases the solution space and computational complexity, potentially leading to simulation times in the range of 600–1000 s. Additionally, longer-term forecasts are less accurate, which may increase LPSP (e.g., estimated at 1.8–2.5%) and operational costs due to suboptimal decisions under uncertainty. However, weekly scheduling can reduce overall costs by optimizing resource utilization over a longer period, particularly in scenarios with stable load patterns.
Table 7 summarizes the estimated performance of the ABC algorithm across these time horizons, based on the current study’s results and reasonable extrapolations for hourly and weekly scheduling.
Table 7 Estimated performance of the ABC algorithm across different time Horizons.
The ABC algorithm’s performance across these horizons highlights its flexibility but also its trade-offs. Hourly scheduling offers high precision but may be computationally intensive for real-time applications in large microgrids. Daily scheduling, as implemented in this study, provides an optimal balance for the benchmark microgrid, achieving low costs and high reliability with reasonable computational effort. Weekly scheduling, while beneficial for long-term planning, faces challenges due to increased uncertainty and computational time, which may reduce its effectiveness unless enhanced with robust forecasting or parallel computing techniques.
To improve the ABC algorithm’s performance across different time horizons, future work could explore adaptive time-step optimization, where the algorithm dynamically adjusts the scheduling resolution based on operational needs. Additionally, integrating stochastic forecasting models or parallelized computation could mitigate the challenges of weekly scheduling and enhance real-time applicability for hourly scheduling in complex microgrids.
Discussion
The simulation results presented in Sect. 6.1–6.6 highlight the effectiveness of the Artificial Bee Colony (ABC) algorithm in optimizing energy management for a benchmark microgrid under diverse load and solar irradiance conditions. Across four operational scenarios (hot sunny, cold sunny, hot cloudy, and cold cloudy), the ABC algorithm consistently achieves the lowest operational costs, ranging from $266.14 to $363.21, while maintaining high system reliability, with Loss of Power Supply Probability (LPSP) values between 1.23% and 1.76%, well below the 2% threshold (Tables 2, 3, 4 and 5). These findings, particularly those in Subsection 6.5, demonstrate the algorithm’s ability to effectively balance the trade-off between cost minimization and reliability. The ABC algorithm outperforms other metaheuristic methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Modified Bat Algorithm (MBA), and Lightning Search Algorithm (LSA), as evidenced by its lower costs (e.g., $266.14 vs. $271.51 for GA in Scenario 4) and lower LPSP values (e.g., 1.61% vs. 1.88% for GA in Scenario 4). The algorithm’s lower standard deviation (e.g., 16.11 in Scenario 1 compared to 19.85 for GA) further underscores its consistency and robustness, making it a reliable tool for microgrid energy management.
The relevance of these results is significant in the context of modern microgrid systems, where cost efficiency and reliability are paramount for sustainable energy solutions. As discussed in Subsection 6.5, the ABC algorithm’s ability to navigate the cost-reliability trade-off is particularly valuable in scenarios with variable renewable generation, such as cloudy conditions (Scenarios 3 and 4), where reduced solar output increases reliance on battery storage and grid imports. By optimizing the scheduling of distributed energy resources (DERs) and battery storage, the algorithm minimizes costly grid imports during peak price hours, as shown in Figs. 16, 17, 10, 11, 12, 13, 14 and 15. This aligns with the global shift toward decentralized energy systems, which aim to enhance energy security and reduce environmental impact through renewable integration1,13. The performance analysis across different time horizons in Subsection 6.6 further highlights the algorithm’s flexibility, with daily scheduling offering a balance of computational efficiency and solution quality (costs of $266.14–$363.21, LPSP of 1.23–1.76%), while hourly scheduling provides precision at higher computational costs, and weekly scheduling supports strategic planning but faces challenges due to increased uncertainty.
Fig. 16
The contribution of each distributed generation source in the first scenario.
Fig. 17
Electricity price and battery power at different hours in the first scenario.
Comparisons with existing literature contextualize the ABC algorithm’s contributions. For instance14] and [15, report cost reductions of 3–5% using PSO-based approaches, while the ABC algorithm achieves comparable or better savings (e.g., 5% lower costs than GA in Scenario 2). The integration of battery storage optimization, as explored in23, complements the current study’s focus on efficient resource utilization. However, Subsection 6.6 notes limitations in real-time implementation for larger microgrids, such as high computational times (84–125 s for daily scheduling), which could be mitigated by parallel computing or adaptive time-step optimization. These findings align with46, which emphasizes the role of adaptive controllers in enhancing microgrid performance, suggesting that combining ABC with such controllers could further improve real-time applicability.
The practical implications of these results are significant for microgrid operators seeking to minimize operational costs while ensuring reliable power supply. The ABC algorithm’s simplicity, requiring fewer control parameters than GA or PSO, makes it an attractive option for practical deployment. However, challenges such as computational complexity in large-scale systems and sensitivity to forecasting errors, as discussed in related literature48, must be addressed to fully realize its potential. Future research could explore integrating stochastic optimization or advanced forecasting techniques to enhance robustness, as well as incorporating demand response strategies45 to further reduce costs by 2–5%. These enhancements would strengthen the ABC algorithm’s role as a versatile and effective solution for next-generation microgrid energy management, contributing to the global transition toward sustainable and resilient energy systems.
Meta CEO Mark Zuckerberg’s aggressive AI talent poaching has been the talk of the tech circles lately. Now it appears that the Meta CEO is embracing a new and radical strategy by placing his biggest AI bet on the smallest teams at his company. As reported by Business Insider, during the recent earnings call Zuckerberg described Meta’s new Superintelligence Labs as “a bit of a different setup,” which contrasts it with the company’s workforce which has more than 70,000 employees. At the heart of this new transformation is the TBD Lab. The lab is a secretive group which consists of elite AI researchers led by Alexandr Wang. This new lab has task of creating Meta’s most advanced AI models — what Zuckerberg calls “personal superintelligence.”
Small team with big goals
“I’ve just gotten a little bit more convinced around the ability for small, talent-dense teams to be the optimal configuration for driving frontier research,” Zuckerberg said. Meta CEO feels that breakthroughs in the filed of AI are best achieved by small and compact groups which can hold the complete problem space ‘in their head’ rather than depending on the massive engineering teams like the ones that manage Facebook’s newsfeed.This philosophy mirrors a growing trend across Silicon Valley, where lean teams are seen as faster, more agile, and more innovative. Startups like Hightouch, which has raised over $132 million with just 55 engineers, exemplify the model. On the other hand, Nat Friedman, former GitHub CEO and now part of Meta’s AI product integration team, has championed the idea that most tech companies are “two to ten times overstaffed.”
Internal tensions and structural challenges
Meta’s pivot towards the ‘startup mode’ has created a bit of a friction within the company. As reported by Business Insider, the creation of Superintelligence Labs has led to resentment and resignation threats among legacy researchers, who have been sidelined after coming of the new hires.On the other hand, experts also suggest that small teams inside large companies often struggle to deliver transformational change. “They can produce useful products and efficiency gains,” said Elliott Parker, CEO of Alloy Partners, “but rarely the kind of fundamental shift that reshapes the parent company.”Lately, Meta has reorganised its AI division. The company has dissolved its two major AI units and is now dealing with the risk of overlap between micro teams. The Superintelligence LabsDespite the risks, Zuckerberg remains bullish. He sees small, elite teams as the key to staying competitive in the AI race, especially as breakthroughs like the 2017 “Attention Is All You Need” paper—authored by just eight researchers—continue to shape the field.“For the leading research on superintelligence,” Zuckerberg said, “you really want the smallest group that can hold the whole thing in their head.”
Figure 1 illustrates the circuit of the proposed structure, which consists of a single DC source, 10 unidirectional power electronic switches, 3 capacitors, and a diode. Switches S3 and S5 lack anti-parallel diodes, and their simultaneous activation enables the simultaneous charging of capacitors C2 and C3. Additionally, capacitor C1 is charged through the activation of switch S8. Using this simple algorithm, an 11-level output voltage is achieved. The switched capacitors stabilize at the desired voltage levels without the need for auxiliary circuits or sensors. The voltage level of capacitor C1 equals the input source voltage, while the voltage levels of capacitors C2 and C3 are half the input voltage. In Fig. 1, the maximum blocking voltage (MBV) of the switches and diode is indicated. Despite the output voltage boost factor being 2.5 times the input voltage, the maximum voltage stress on all components is lower than this value, which is a significant advantage of the proposed structure. The switching method for the proposed structure is presented in Table 1. In this table, the value “1” indicates that the corresponding switch is ON during the generation of the specified voltage level, while “0” indicates that it is OFF. Besides, “C” means charging, “D” means discharging, and “-” means no change in capacitor voltage.
Fig. 1
Proposed inverter structure.
Table 1 Switching States of the proposed 11-Level Inverter.
Operating States
Figure 2 illustrates the operating states and current paths during the positive and negative half-cycles. For each voltage level, only 4 of the 10 switches in the structure are turned ON, which is a significant advantage as it reduces conduction losses. The switching states for the positive half-cycle are described as follows:
State A: When switches S2, S4, S7, and S9 are ON, capacitors C1 and C3 discharge, generating the voltage level 2.5Vdc.
State B: Current flows through switches S2, S3, S7, and S9, and with the discharge of capacitor C1, the sum of the input source voltage and capacitor C1 generates the output voltage.
State C: In this state, switches S2, S4, S8, and S9 are turned ON, capacitor C1 charges, and capacitor C3 discharges to generate the voltage level 1.5Vdc.
State D: Switches S2, S3, S5, and S9 are turned ON, and capacitors C2 and C3 are simultaneously charged to 0.5Vdc. In this state, the voltage level Vdc is generated by the DC source.
State E: Current flows through switches S2, S6, and S9, generating the voltage level 0.5Vdc with the discharge of capacitor C3. In this state, capacitor C1 charges through switch S8, which is connected in parallel with the DC source.
State O: To generate the zero voltage level, switches S2, S3, and S10 are turned ON. In this state, capacitors C2 and C3 are simultaneously charged through switch S5.
Fig. 2
Current paths for load current and capacitor charging current in the proposed structure.
Switching method
Various methods have been introduced for controlling and generating pulse signals for switches in multi-level switched capacitor inverters. PWM, NLM, SVM, and SHE are among the most important switching methods, and they have been widely used over the years, each with its own advantages and disadvantages20,21,22,23. The SHE and NLM techniques fall under the category of low-frequency switching methods, while the SVM and PWM techniques are classified as high-frequency switching methods. Each of these methods can be implemented in the proposed structure. The proposed structure uses sinusoidal PWM modulation to shift the switching level, and the components of this modulation are shown in Figure 3. As illustrated in Figure 3-a, to generate an 11-level output voltage, 10 carrier signals (Vcar) with identical amplitude and switching frequency are needed, along with one reference signal (Vref). By comparing the reference signal with the carrier signals, the switching pulses corresponding to each voltage level are generated. The modulation index can be defined by the following relation:
Modulation technique (LS-PWM), a PWM signal generation scheme, b) Logical circuit for pulse generation.
Capacitor design
One of the key parameters in designing a multi-level switched capacitor inverter is selecting the appropriate capacitor size for the structure being used. If the capacitor size is less than the correct and suitable value, the voltage ripple across the capacitor will increase. This not only reduces the quality of the output voltage but also increases the capacitor charging current and the associated ripple losses. Additionally, if the capacitor size is greater than the correct and appropriate value, the cost and size of the inverter will increase. Therefore, in order to improve the inverter’s efficiency, the design and calculation of the appropriate capacitor size is of high importance. To select the capacitor size, the maximum voltage ripple across the capacitor during the switching process is calculated. For this purpose, the range in which each capacitor experiences the maximum discharge duration (MDD) is considered. To identify these time intervals, Figure 4, which illustrates the charging and discharging process of capacitors at different output voltage levels, can be used.
Fig. 4
Charging and discharging of capacitors based on output voltage levels.
Based on Figure 4, the maximum discharge duration (MDD) for capacitor C1 occurs during one quarter of the period from time t4 to T/2-t4. Similarly, the maximum discharge duration (MDD) for capacitors C2 and C3 occurs during one quarter of the period from time t5 to T/2-t5. Based on the symmetry in the output voltage waveform, the discharge time intervals for the capacitors are described for one-quarter of the period. To calculate the discharge energy and account for the full discharge duration, a factor of two is used. The moments indicated in Figure 4 are determined using Eq. (2), where Nl represents the number of output voltage levels. Using this equation, the moments t1 through t5 can be written as Eq. (3)24:
Since the capacitors are placed in the load path to generate output voltage levels and discharge, the current passing through the capacitors can be considered as the load current (Io). Therefore, the amount of charge discharged from the capacitors during specific time intervals can be expressed by the following equations:
$$Delta {Q_{{C_1}}}=2int_{{{t_4}}}^{{T/2 – {t_4}}} {{I_o}} sin left( {omega t – varphi } right)dt$$
(4)
$$Delta {Q_{{C_2}}}=Delta {Q_{C3}}=2int_{{{t_5}}}^{{T/2 – {t_5}}} {{I_o}} sin left( {omega t – varphi } right)dt$$
(5)
If the voltage ripple percentage of the capacitors (ΔVripple) is within the standard range and less than 10%, the minimum required capacitance can be expressed using relations (6) and (7):
$${C_1} geqslant frac{{2{I_o}}}{{2pi f times {V_{dc}} times {V_{ripple}}}}left( {cos left( {0.7754 – varphi } right) – sin varphi } right)$$
(6)
$${C_2},{C_3} geqslant frac{{2{I_o}}}{{2pi f times left( {0.5{V_{dc}}} right) times {V_{ripple}}}}left( {cos left( {1.12 – varphi } right) – sin varphi } right)$$
(7)
The selected capacitor capacitance must be higher than the calculated capacitance, and thus, the first standard capacitor with a value greater than the calculated capacitance (according to the capacitor’s datasheet) is chosen.
Capacitor charging current
One of the main challenges in the design and use of multilevel switched capacitor inverters is the capacitor charging current. This current is generated during the charging of the capacitors through the input voltage source, and at the moment of inverter startup, it is very high. If the capacitor charging current is not controlled, it can lead to damage to the circuit components, reduced capacitor lifespan, and decreased converter efficiency. Various methods have been proposed for controlling the capacitor charging current, among which the most important are the use of current-limiting resistors25 and the use of current-limiting inductors26, which play a significant role in reducing and controlling the capacitor charging current. In this paper, as shown in Figure 5, an inductor (Lr) in parallel with a reverse-parallel diode (Dr) is used to limit the capacitor charging current. The current-limiting inductor is connected in series with the input voltage source, preventing the capacitor from drawing a large instantaneous charging current. The size of the inductor must be properly chosen, as if the inductor is larger than the required value, it may cause disturbance in the dynamic response of the inverter. For proper operation, the condition of Eq. (8) must be satisfied, and the switching frequency (fsw) must be greater than the resonance frequency (fr) resulting from the current-limiting inductor and capacitor. The size of the current-limiting inductor can be calculated using Eq. (9):
Equivalent circuit of soft charging of capacitors with a current-limiting inductor, (a) Soft charging of capacitor C1, (b) Soft charging of capacitors C2 and C3.
Petrowsky, H. et al. Modern therapeutic approaches for the treatment of malignant liver tumours. Nat. Rev. Gastroenterol. Hepatol.17, 755–772 (2020).
PubMed
Google Scholar
Huang, M., Lu, J. J. & Ding, J. Natural products in cancer therapy: Past, present and future. Nat. Prod. Bioprospect. 11, 5–13 (2021).
PubMed
PubMed Central
Google Scholar
Attia, A. A. et al. Amygdalin potentiates the anti-cancer effect of Sorafenib on Ehrlich Ascites carcinoma and ameliorates the associated liver damage. Sci. Rep.12, 1–9 (2022).
Google Scholar
Chi, H. et al. Characterization of a novel and glutaminase-free type II L-asparaginase from Corynebacterium glutamicum and its acrylamide alleviation efficiency in potato chips. Int. J. Biol. Macromol.221, 1384–1393 (2022).
PubMed
Google Scholar
Zhou, Y. et al. Rational engineering and insight for a L-glutaminase activity reduced type II L-asparaginase from Bacillus licheniformis and its antileukemic activity in vitro. Int. J. Biol. Macromol.257, 128690 (2024).
PubMed
Google Scholar
Barzkar, N., Sohail, M., Tamadoni Jahromi, S., Nahavandi, R. & Khodadadi, M. Marine microbial L-glutaminase: from pharmaceutical to food industry. Appl. Microbiol. Biotechnol.105, 4453–4466 (2021).
PubMed
Google Scholar
Debnath, T., Kujur, R. R. A., Mitra, R. & Das, S. K. Diversity of microbes in hot springs and their sustainable use. Microb. Divers. Ecosyst. Sustain. Biotechnol. Appl. Vol. 1. Microb. Divers. Norm. Extrem. Environ. 159–186 (2019).
Haki, G. D. & Rakshit, S. K. Developments in industrially important thermostable enzymes: A review. Bioresour Technol.89, 17–34 (2003).
PubMed
Google Scholar
Feller, G. Protein stability and enzyme activity at extreme biological temperatures. J. Phys. Condens. Matter. 22, 323101 (2010).
PubMed
Google Scholar
Nigam, P. S. Microbial enzymes with special characteristics for biotechnological applications. Biomolecules3, 597–611 (2013).
PubMed
PubMed Central
Google Scholar
Sanchez, S. & Demain, A. L. Enzymes and bioconversions of industrial, pharmaceutical, and biotechnological significance. Org. Process. Res. Dev.15, 224–230 (2011).
Google Scholar
Kumar, A., Mukhia, S. & Kumar, R. Industrial applications of cold-adapted enzymes: Challenges, innovations and future perspective. 3 Biotech.11, 426 (2021).
PubMed
PubMed Central
Google Scholar
Lukey, M. J., Wilson, K. F. & Cerione, R. A. Therapeutic strategies impacting cancer cell glutamine metabolism. Future Med. Chem.5, 1685–1700 (2013).
PubMed
Google Scholar
Nguyen, H. A., Su, Y. & Lavie, A. Design and characterization of erwinia chrysanthemi L-asparaginase variants with diminished L-glutaminase activity. J. Biol. Chem.291, 17664–17676 (2016).
PubMed
PubMed Central
Google Scholar
Orabi, H., El-Fakharany, E., Abdelkhalek, E. & Sidkey, N. Production, optimization, purification, characterization, and anti-cancer application of extracellular L-glutaminase produced from the marine bacterial isolate. Prep Biochem. Biotechnol.50, 408–418 (2020).
PubMed
Google Scholar
Maurya, D. K., Kumar, A., Chaurasiya, U., Hussain, T. & Singh, S. K. Modern era of microbial biotechnology: opportunities and future prospects. in Microbiomes and plant health 317–343Elsevier, (2021).
Fidelito, G. et al. Multi-substrate metabolic tracing reveals marked heterogeneity and dependency on fatty acid metabolism in human prostate cancer. Mol. Cancer Res.21, 359–373 (2023).
PubMed
Google Scholar
Pandian, S. R. K., Deepak, V., Nellaiah, H. & Sundar, K. PEG–PHB-glutaminase nanoparticle inhibits cancer cell proliferation in vitro through glutamine deprivation. Vitr Cell. Dev. Biol.51, 372–380 (2015).
Google Scholar
Kuo, M. T., Chen, H. H. W., Feun, L. G. & Savaraj, N. Targeting the proline–glutamine–asparagine–arginine metabolic axis in amino acid starvation cancer therapy. Pharmaceuticals14, 72 (2021).
PubMed
PubMed Central
Google Scholar
Carneiro, B. A. & El-Deiry, W. S. Targeting apoptosis in cancer therapy. Nat. Rev. Clin. Oncol.17, 395–417 (2020).
PubMed
PubMed Central
Google Scholar
Elmetwalli, A. et al. Diarylheptanoids/sorafenib as a potential anticancer combination against hepatocellular carcinoma: the p53/MMP9 axis of action. Naunyn Schmiedebergs Arch. Pharmacol 1–17 (2023).
Matés, J. M. et al. Metabolic reprogramming of cancer by chemicals that target glutaminase isoenzymes. Curr. Med. Chem.27, 5317–5339 (2020).
PubMed
Google Scholar
Kao, T. W., Chuang, Y. C., Lee, H. L., Kuo, C. C. & Shen, Y. A. Therapeutic targeting of Glutaminolysis as a novel strategy to combat cancer stem cells. Int. J. Mol. Sci.23, 15296 (2022).
PubMed
PubMed Central
Google Scholar
Kumar, M. et al. Identification of small molecule inhibitors of RAD52 for breast cancer therapy: in Silico approach. J. Biomol. Struct. Dyn.42, 4605–4618 (2024).
PubMed
Google Scholar
Gupta, D. et al. A comprehensive review on role of Aurora kinase inhibitors (AKIs) in cancer therapeutics. Int J. Biol. Macromol130913 (2024).
Gupta, D. et al. Identification of polypharmacological anticancerous molecules against Aurora kinase family of proteins. J. Cell. Biochem.123, 719–735 (2022).
PubMed
Google Scholar
Agu, P. C. et al. Molecular Docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep.13, 13398 (2023).
ADS
PubMed
PubMed Central
Google Scholar
Elmetwalli, A. et al. Evaluation of Bacillus Aryabhattai B8W22 peroxidase for phenol removal in waste water effluents. BMC Microbiol.23, 1–13 (2023).
Google Scholar
Hasan, S. F., Elsoud, M. M. A., Sidkey, N. M. & Elhateir, M. M. Production and characterization of polyhydroxybutyrate bioplastic precursor from Parageobacillus toebii using low-cost substrates and its potential antiviral activity. Int J. Biol. Macromol129915 (2024).
El-Hadedy, D. E., MH, S., HH, E. & Mm, S. Enhanced production of extracellular L-methioninase by entire cell immobilization of streptomyces MDMMH4 and examination of its utilization as an antioxidant agent. Pak J. Pharm. Sci36, (2023).
Abellan-Schneyder, I. et al. Primer, pipelines, parameters: Issues in 16S rRNA gene sequencing. Msphere6, 10–1128 (2021).
Google Scholar
Abdallah, N. A., Amer, S. K. & Habeeb, M. K. Production, purification and characterization of L-glutaminase enzyme from streptomyces avermitilis. Afr. J. Microbiol. Res.14, 1184–1190 (2013).
Google Scholar
Katikala, P. K., Bobbarala, V., Tadimalla, P. & Guntuku, G. S. Screening of L-glutaminase producing marine bacterial cultures for extracellular production of L-glutaminase. Int. J. ChemTech Res.1, 1232–1235 (2009).
Google Scholar
Jayabalan, R. et al. Extracellular L-glutaminase production by marine brevundimonas diminuta MTCC 8486. Int. J. Appl. Bioeng.4, 19–24 (2010).
Google Scholar
Reda, F. M. Kinetic properties of streptomyces Canarius L-Glutaminase and its anticancer efficiency. Brazilian J. Microbiol.46, 957–968 (2015).
Google Scholar
El-Sewedy, T. et al. Hepatocellular carcinoma cells: Activity of amygdalin and Sorafenib in targeting ampk/mtor and BCL-2 for anti-angiogenesis and apoptosis cell death. BMC Complement. Med. Ther.23, 1–17 (2023).
Google Scholar
Elmetwalli, A. et al. Probiotic-derived silver nanoparticles target mTOR/MMP-9/BCL-2/dependent AMPK activation for hepatic cancer treatment. Med. Oncol.41, 106 (2024).
PubMed
PubMed Central
Google Scholar
El-Shehawy, A. A. et al. Thymoquinone, piperine, and Sorafenib combinations attenuate liver and breast cancers progression: Epigenetic and molecular Docking approaches. BMC Complement. Med. Ther.23, 1–21 (2023).
Google Scholar
Al Balawi, A. N., Eldiasty, J. G., Mosallam, S. A. E. R., El-Alosey, A. R. & Elmetwalli, A. Assessing multi-target antiviral and antioxidant activities of natural compounds against SARS-CoV-2: an integrated in vitro and in Silico study. Bioresour Bioprocess.11, 108 (2024).
PubMed
PubMed Central
Google Scholar
Vilar, S., Cozza, G. & Moro, S. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular Docking to drug discovery. Curr. Top. Med. Chem.8, 1555–1572 (2008).
PubMed
Google Scholar
Bewick, V., Cheek, L. & Ball, J. Statistics review 9: One-way analysis of variance. Crit. Care. 8, 1–7 (2004).
Google Scholar
Rafeeq, H. et al. Esterases as emerging biocatalysts: mechanistic insights, genomic and metagenomic, immobilization, and biotechnological applications. Biotechnol. Appl. Biochem.69, 2176–2194 (2022).
PubMed
Google Scholar
Tandon, S., Sharma, A., Singh, S., Sharma, S. & Sarma, S. J. Therapeutic enzymes: Discoveries, production and applications. J. Drug Deliv Sci. Technol.63, 102455 (2021).
Google Scholar
Binod, P. et al. Recent developments in l-glutaminase production and applications–An overview. Bioresour Technol.245, 1766–1774 (2017).
PubMed
Google Scholar
Srinivasan, P. et al. Production and purification of laccase by Bacillus sp. using millet husks and its pesticide degradation application. 3 Biotech.9, 396 (2019).
PubMed
PubMed Central
Google Scholar
Gomaa, E. Z. Production, characterization, and antitumor efficiency of l-glutaminase from halophilic bacteria. Bull. Natl. Res. Cent.46, 10 (2022).
Google Scholar
Mostafa, Y. S. et al. L-glutaminase synthesis by marine Halomonas Meridiana isolated from the red sea and its efficiency against colorectal cancer cell lines. Molecules26, 1963 (2021).
PubMed
PubMed Central
Google Scholar
Mousumi, D. & Dayanand, A. Production and antioxidant attribute of L-glutaminase from streptomyces enissocaesilis DMQ-24. Int. J. Latest Res. Sci. Technol.2, 1–9 (2013).
Google Scholar
Gehlot, P., Kumar, M. & Pareek, N. Production and purification of glutaminase free L-asparaginase from Lysinibacillus fusiformis and its appraisal in acrylamide mitigation of starchy foods. Mater. Today Proc.69, 64–73 (2022).
Google Scholar
Abhini, K. N., Rajan, A. B., Zuhara, K. F. & Sebastian, D. Response surface methodological optimization of l-asparaginase production from the medicinal plant endophyte acinetobacter baumannii ZAS1. J. Genet. Eng. Biotechnol.20, 22 (2022).
PubMed
PubMed Central
Google Scholar
Arévalo-Tristancho, E., Díaz, L. E., Cortázar, J. E. & Valero, M. F. Production and characterization of L-Asparaginases of isolated from the Arauca riverbank (Colombia). Open Microbiol. J13, (2019).
Wang, B., Pei, J., Xu, S., Liu, J. & Yu, J. A glutamine tug-of-war between cancer and immune cells: Recent advances in unraveling the ongoing battle. J. Exp. Clin. Cancer Res.43, 74 (2024).
PubMed
PubMed Central
Google Scholar
Tang, X. et al. Selective enhanced cytotoxicity of amino acid deprivation for cancer therapy using thermozyme functionalized nanocatalyst. J. Nanobiotechnol.22, 53 (2024).
Google Scholar
Srivastava, N., Srivastava, M., Mishra, P. K., Ramteke, P. W. & Singh, R. L. New and Future Developments in Microbial Biotechnology and Bioengineering: from Cellulose To Cellulase: Strategies To Improve Biofuel Production (Elsevier, 2019).
Ariaeenejad, S. et al. Highly efficient computationally derived novel metagenome α-amylase with robust stability under extreme denaturing conditions. Front. Microbiol.12, 713125 (2021).
PubMed
PubMed Central
Google Scholar
Banerjee, S., Maiti, T. K. & Roy, R. N. Production, purification, and characterization of cellulase from acinetobacter Junii GAC 16.2, a novel cellulolytic gut isolate of Gryllotalpa africana, and its effects on cotton fiber and sawdust. Ann. Microbiol.70, 1–16 (2020).
Google Scholar
Guajardo, N. & Schrebler, R. A. Upstream and downstream bioprocessing in enzyme technology. Pharmaceutics16, 38 (2023).
PubMed
PubMed Central
Google Scholar
Amiri, S., Mokarram, R. R., Khiabani, M. S., Bari, M. R. & Khaledabad, M. A. Characterization of antimicrobial peptides produced by Lactobacillus acidophilus LA-5 and bifidobacterium lactis BB-12 and their inhibitory effect against foodborne pathogens. Lwt153, 112449 (2022).
Google Scholar
Priyadarshini, A., Sahoo, M. M., Raut, P. R., Mahanty, B. & Sahoo, N. K. Kinetic modelling and process engineering of phenolics microbial and enzymatic biodegradation: a current outlook and challenges. J. Water Process. Eng.44, 102421 (2021).
Google Scholar
Bhatt, P. et al. New insights into the degradation of synthetic pollutants in contaminated environments. Chemosphere268, 128827 (2021).
PubMed
Google Scholar
Qeshmi, F. I., Homaei, A., Fernandes, P. & Javadpour, S. Marine microbial L-asparaginase: Biochemistry, molecular approaches and applications in tumor therapy and in food industry. Microbiol. Res.208, 99–112 (2018).
Google Scholar
Saleem, R. & Ahmed, S. Characterization of a new L-Glutaminase produced by achromobacter xylosoxidans RSHG1, isolated from an expired hydrolyzed L-Glutamine sample. Catalysts11, 1262 (2021).
Google Scholar
Bagewadi, Z. K. et al. Molecular expression, purification and structural characterization of Recombinant L-Glutaminase from streptomyces Roseolus. Int. J. Biol. Macromol.273, 133142 (2024).
PubMed
Google Scholar
Mosallatpour, S., Aminzadeh, S., Shamsara, M. & Hajihosseini, R. Novel halo-and thermo-tolerant Cohnella sp. A01 L-glutaminase: Heterologous expression and biochemical characterization. Sci. Rep.9, 19062 (2019).
ADS
PubMed
PubMed Central
Google Scholar
Beckett, A. & Gervais, D. What makes a good new therapeutic L-asparaginase? World J. Microbiol. Biotechnol.35, 1–13 (2019).
Google Scholar
Akram, F. et al. Abridgement of microbial esterases and their eminent industrial endeavors. Mol Biotechnol 1–17 (2024).
Sodhi, A. S. et al. Insights on sustainable approaches for production and applications of value added products. Chemosphere286, 131623 (2022).
PubMed
Google Scholar
Diwan, D. et al. Elsevier,. Microbial cancer therapeutics: A promising approach. in Seminars in cancer biology vol. 86 931–950 (2022).
Sawant, S. S., Patil, S. M., Gupta, V. & Kunda, N. K. Microbes as medicines: Harnessing the power of bacteria in advancing cancer treatment. Int. J. Mol. Sci.21, 7575 (2020).
PubMed
PubMed Central
Google Scholar
Awad, M. F., El-Shenawy, F. S., El-Gendy, M. M. A. A. & El-Bondkly, E. A. M. Purification, characterization, and anticancer and antioxidant activities of l-glutaminase from Aspergillus versicolor Faesay4. Int. Microbiol.24, 169–181 (2021).
PubMed
Google Scholar
Abu-Tahon, M. A. & Isaac, G. S. Purification, characterization and anticancer efficiency of L-glutaminase from Aspergillus flavus. J. Gen. Appl. Microbiol.65, 284–292 (2019).
PubMed
Google Scholar
Matés, J. M., Campos-Sandoval, J. A. & de Los Santos-Jiménez, J. M rquez, J. Dysregulation of glutaminase and glutamine synthetase in cancer. Cancer Lett.467, 29–39 (2019).
PubMed
Google Scholar
Hassan, F. S. et al. Comprehensive insight into exploring the potential of microbial enzymes in cancer therapy: progress, challenges, and opportunities: A review. Int J. Biol. Macromol134535 (2024).
Crosby, H. A. & Miller, K. E. Evaluating the analgesic effect of the GLS inhibitor 6-Diazo-5-Oxo-L-Norleucine in vivo. Pharm Pharmacol. Int. J3, (2016).
Mendiratta, S. S., Sekulic, N., Lavie, A. & Colley, K. J. Specific amino acids in the first fibronectin type III repeat of the neural cell adhesion molecule play a role in its recognition and polysialylation by the polysialyltransferase ST8Sia IV/PST. J. Biol. Chem.280, 32340–32348 (2005).
PubMed
Google Scholar
pez-Gil, L., n, C. I., Téllez-Jurado, A., Velasco-Vel zquez, M. A. & Anducho-Reyes, M. A. Identification and analysis of anticancer therapeutic targets from the polysaccharide Krestin (PSK) and polysaccharopeptide (PSP) using inverse Docking. Molecules29, 5390 (2024).
Google Scholar
Mates, J. M. et al. Glutaminase isoenzymes as key regulators in metabolic and oxidative stress against cancer. Curr. Mol. Med.13, 514–534 (2013).
PubMed
Google Scholar
Yang, W. H., Qiu, Y., Stamatatos, O., Janowitz, T. & Lukey, M. J. Enhancing the efficacy of glutamine metabolism inhibitors in cancer therapy. Trends Cancer. 7, 790–804 (2021).
PubMed
PubMed Central
Google Scholar
Elmetwalli, A. et al. Ammonia scavenger and glutamine synthetase inhibitors cocktail in targeting mTOR/β-catenin and MMP-14 for nitrogen homeostasis and liver cancer. Med. Oncol.41, 38 (2023).
PubMed
Google Scholar
Youness, A., Kamel, R., Elkasabgy, R. A., Shao, N., Farag, A. & P. & Recent advances in Tannic acid (gallotannin) anticancer activities and drug delivery systems for efficacy improvement; a comprehensive review. Molecules26, 1486 (2021).
Google Scholar
Cai, Y. et al. Recent advances in anticancer activities and drug delivery systems of tannins. Med. Res. Rev.37, 665–701 (2017).
PubMed
Google Scholar
Ghasemian, M. et al. Recent progress in Tannic acid based approaches as a natural polyphenolic biomaterial for cancer therapy: A review. Biomed. Pharmacother. 166, 115328 (2023).
Performance characteristics of engine with and without coating
Brake thermal efficiency [BTE
The ratio of an engine’s output power to its chemical energy input from the fuel and air supply is known as its thermal efficiency. BTE also considers combustion efficiency, highlighting that not all the chemical energy of the fuel is transformed into heat energy during combustion. Figure 6(a–f) depict the variations in BTE at different CRs (16, 17.5, and 19 CR) for diesel and various blends. The analysis examines the increase in load for engines with both coated and uncoated components. The findings show that BTE progressively rises with increasing load for both diesel and SOME blends in Uncoated Engine (UCE) and Thermal Barrier Coated Engine (TBCE), due to increased fuel supply and decreased heat loss. At first, it was found that thermal efficiency remained similar to diesel when the amount of biodiesel in the fuel mixture was reduced. However, as the blend percentage of SOME exceeds 30% by volume, BTE consistently decreases under all load conditions compared to diesel. This trend is due to inadequate atomization, reduced vaporization, and suboptimal combustion caused by the higher density, viscosity, and lower volatility of SOME3. As illustrated in Fig. 6(a–c), increasing the engine’s compression ratio is associated with higher BTE for all tested fuels. This aligns with the principle that BTE tends to increase with compression ratio, as supported by previous research. When the CR increased from 16 to 17.5, the average BTE rise by 3.63%, 3.45%, 2.85%, 2.33%, 1.51%, and 3.66%, and when increased from 17.5 to 19, the average BTE increased by 6.38%, 6.67%, 4.01%, 3.49%, 3.45%, and 6.61% for 20%, 40%, 60%, 80%, and 100% of various biodiesel blend ratios and diesel, respectively, under operating load. Higher peak pressure, higher ID, and higher burning temperature are the causes of this improvement in BTE at higher CRs. At CR-19, the BTE for the B20 blend of SOME is the highest among all biodiesel blend samples, being only 0.307% less than pure diesel at maximum loading conditions55. Vijay Kumar et al. found that applying a 0.5 mm 3Al2O3–2SiO2 TBC in an LHR engine using Mahua Methyl Ester improved BTE by 13.65% at 25% load, enhanced SFC and BTE at full load, and reduced exhaust temperature, smoke, HC, and marginally CO emissions across all operating conditions13. Samuelraj et al. reported that Blend E, containing carbon black, n-pentanol, and soybean biodiesel, improved BTE by 4.90% and reduced BSFC by 25.31% at peak load in an LHR engine. CO, HC, NOx, and smoke emissions dropped significantly, while in-cylinder pressure and heat release rate increased by 4.52% and 8.87%, respectively, indicating enhanced combustion12. As shown in Fig. 6(d–f), BTE is higher for all test fuels in the mullite-coated engine compared to the uncoated engine at all investigated CRs. The mullite coating, with its low thermal conductivity, acts as an insulator, reducing heat loss to the coolant and surroundings, leading to more homogeneous combustion. As thermal energy accumulates in the combustion chamber, the temperature of the gas inside the cylinder and the cylinder walls rises, enhancing combustion efficiency and increasing BTE. Experimental results show that at maximum load, the BTE for the TBC-coated engine increases by 5.49%, 4.08%, 4.01%, 3.17%, 2.78%, and 6.4% at CR 17.5 and by 5.75%, 4%, 3.94%, 2.98%, 2.4%, and 7.51% at CR 19 for B20, B40, B60, B80, B100, and diesel, respectively, compared to the uncoated engine, as shown in Figure S-1. Various literature studies on biodiesel such as Rice Bran, Pongamia, Cashew Nutshell, Rubber Seed, Cotton Seed, Neem Kernel Oil, and Frying Oil used in TBC-coated engines reported an increase in BTE between 3.8% and 8.3%49.
Fig. 6
(a–f) The variation of the BTE at various loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Brake specific fuel consumption
During engine testing, fuel consumption is measured by the rate at which fuel is consumed, expressed as the mass of fuel per unit of time. BSFC measures the amount of fuel used (in g/h) to produce one unit of power (kW) and is inversely related to BTE. Figure 7(a–f) shows the variation of BSFC for both surface-treated and untreated diesel engines fueled with diesel and blends of SOME, under varying load conditions and CRs. BSFC decreases with an increase in load and CR for all samples in the uncoated engine. BSFC experiences a significant 60% reduction from no load to quarter load, with a slight decline noted at 25% load. Due to the reduced calorific value at higher blend percentages, biodiesel blends have slightly higher BSFC than pure diesel. Consequently, more fuel is required for combustion to produce 1 kW, leading to an increase in BSFC56,57. Biodiesel blends with proportions of 20%, 40%, 60%, 80%, and 100% in the uncoated engine showed an 8%, 7.69%, 7.41%, 3.44%, 6.66%, and 8% decrease in BSFC when the CR varied from 16 to 17.5, and a 4.17%, 4%, 8%, 11.53%, 7.14%, and 4.17% decrease in BSFC when the CR varied from 17.5 to 19, respectively (Fig. 7(a–c)). Increasing the CR leads to a decrease in BSFC for all samples, likely due to reduced ID and improved conversion of heat to mechanical work, resulting in smoother engine operation. The results indicate that biodiesel shows a better decrease in BSFC than diesel as the compression ratio increases, attributed to the lower volatility and higher cetane number of biodiesels, which enhance combustion at higher CRs compared to diesel45.
The variation of BSFC for the mullite-coated diesel engine at varying loads for all test samples and CRs is displayed in Fig. 7(d–f). For all test samples, mullite-coated engines had lower BSFC than the uncoated engine across all CRs and engine load. Improved heat retention in the combustion chamber raises in-cylinder gas and wall temperatures, which improves fuel burning due to heat insulation in the coated condition. The ID period lowers as LHR engine cylinder and wall temperatures rise, improving fuel atomization and air-fuel blending. Consequently, the improved combustion conditions favor reduced amounts of test fuel, positively affecting both physical and chemical delays. Comparing the coated engine to the conventional diesel engine, this causes a drop in BSFC for all test samples at different CRs. For B20, B40, B60, B80, B100, and diesel in the coated engine, BSFC decreases by 4.7%, 4%, 3.84%, 7.84%, 3.44%, and 8.69% at CR-17.5, and by 9.09%, 8.69%, 4.17%, 4%, 7.69%, and 14.28% at CR-19, respectively, compared to the uncoated engine, as depicted in Figure S-258. In this study, the BSFC reduction reached up to 9.09% for B20 blend and 14.28% for diesel in the TBC engine, which is consistent with the reductions reported by (6.5%)43. The observed BSFC improvement is notably higher than that reported by Soudagar et al.59(3.5–5.6%) and contrasts with Muralidharan et al.60, who observed an increase of 9–10% in BSFC with biodiesel blends. These improvements in the present study can be attributed to the synergistic effects of the optimized compression ratio, enhanced combustion due to the mullite thermal barrier coating, and the favorable fuel properties of scum oil methyl ester. Various studies have reported a significant improvement of around 4.8% in LHR engines using different biodiesels, which is consistent with the findings of the current study32.
Fig. 7
(a–f) The variation of the BSFC at different loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Brake specific energy consumption (BSEC)
BSEC is defined as the metric that quantifies the efficient energy produced from the combustion of fuel to generate unit brake power within a specific timeframe. The determination of BSEC is a more realistic parameter compared to BSFC for assessing the performance of a CI engine running on various test samples with different densities and lower heating values45. BSEC is typically calculated as the product of the fuel’s lower heating value and BSFC, as shown in Eq. (7).
$$BSEC=Low{text{ }}calorific{text{ }}value{text{ }} times {text{ }}BSFC{text{ }}$$
(7)
Figure 8(a-f) illustrates the variation in BSEC values for both uncoated and mullite-coated VCR engines fueled with diesel and SOME blends at different CRs under varying loads. Essentially, BSEC decreases with an increase in load and CR, mirroring the trends observed in the BSFC figures for all tested fuel samples. From Figs. 9(a-f), it is evident that conventional diesel fuel has a lower BSEC than all biodiesel blend samples across different engine loads. However, B100 shows the highest BSEC value among the biodiesel samples due to its higher BSFC, lower calorific value, longer ID, higher flash point temperature, and poorer atomization properties43. Under peak engine load conditions, the average decrease in BSEC was 8.05%, 7.87%, 7.44%, 3.48%, 6.69%, and 7.99% when CR increased from 16 to 17.5, and similarly, the average decrease in BSEC was 3.13%, 3.8%, 8.05%, 11.57%, 7.09%, and 4.22% when CR increased from 17.5 to 19 for B20, B40, B60, B80, B100, and diesel, respectively, in the uncoated diesel engine, as shown in the BSEC graphs. The results indicate that increasing the compression ratio (CR) leads to a consistent reduction in Brake Specific Energy Consumption (BSEC) across all fuel samples, primarily due to enhanced thermal efficiency and shorter ignition delay, contributing to smoother engine operation. Fuel consumption showed a notable dependency on CR under varying engine conditions, directly influencing BSEC values. CR 17.5 served as the reference operating condition, while a further increase to CR 19 resulted in lower fuel consumption, likely due to improved combustion efficiency and optimized in-cylinder thermodynamic conditions, despite slightly reduced air availability from altered cylinder volume. In contrast, at CR 16, higher volumes of both fuel and air were required to maintain the rated power output, indicating suboptimal combustion. Interestingly, as shown in Fig. 8(c), BSEC values for B20 closely matched those of diesel at higher CRs, attributed to reduced fuel usage driven by better Brake Thermal Efficiency (BTE) and superior fuel–air mixing characteristics61.
Figure 8(d-f) describes the variation in BSEC for the LHR diesel engine at different CRs and varying loads for various fuel samples. The graphs show that BSEC decreases for the mullite-coated engine compared to the uncoated engine as engine load and CRs increase for all test samples. This is attributed to higher combustion chamber temperatures, improved fuel atomization, reduced ID, enhanced vaporization rate, and lower fuel consumption exhibited by the thermally coated diesel engine. These factors further decrease BSEC at higher CRs for all fuel samples in the thermally coated engine compared to the uncoated CI engine9,61. At maximum engine load, BSEC values in the coated engine are 10.14, 10.51, 10.86, 11.22, 11.98, and 9.77 MJ/kWh at CR-17.5, and 9.29, 9.67, 10.03, 10.39, 10.74, and 8.92 MJ/kWh at CR-19 for B20, B40, B60, B80, B100, and diesel, respectively. At 100% engine load, a reduction in BSEC of 4.14%, 3.9%, 3.87%, 7.39%, 3.42%, and 8.8% at CR-17.5 and 10.22%, 8.79%, 4.09%, 3.94%, 7.73%, and 14.35% at CR-19 for B20, B40, B60, B80, B100, and diesel, respectively, was observed in the thermally coated engine compared to the uncoated diesel engine, as shown in Figure S-3. The use of the mullite-coated engine resulted in a significant reduction in BSEC values of 5.25% at CR-17.5 and 8.18% at CR-19 with SOME blends, consistent with the findings of Karthikeyan et al.45.
Fig. 8
(a–f) The variation of the BSEC at various loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Exhaust gas temperature
The exhaust gas temperature (EGT) is a significant measurement that can be used as proxy for combustion quality inside the cylinder and is directly related to the in-cylinder temperature at the time of combustion. From Fig. 9(a–f), it is observed that EGT always increases with load for all test fuels and CRs, whether it is in uncoated and LHR diesel engines. This increase is due to a higher load more fuel is injected per unit time, in which heat release is increased to maintain sustain torque output. Nevertheless, blends of higher quantities of SOME biodiesel presented systemically slightly lower EGT than diesel, this tendency was more accentuated at higher CR. This decrease is due to the lower heating value and the higher oxygen content of biodiesel which lead to more complete combustion though at lower peak flame temperatures. At the lowest CR (16:1), in particular, EGT values were higher for all fuels. At 17.5 and 19 CR, EGT values of biodiesel blends are less than those of diesel which gives better thermal efficiency with increased CR8. At CR-17.5 EGT for diesel is 368.93 °C, for B20: 363.67 °C and for B100: 344.68 °C. Similarly, at CR-19 EGT for diesel is 363.67 °C, for B20: 360.09 °C and for B100: 342.46 °C, at operating conditions. The decrease in EGT at greater CRs can be ascribed to various variables, such as the reduced calorific value, increased oxygen content, elevated flash point, and diminished end-of-compression temperature of certain mixed fuel in comparison to diesel. The combustion temperature is a critical factor in understanding the generation of NOx inside the engine cylinder. Performance may increase due to lower exhaust loss62. As the CR increases, the volume of EGT at peak load decreases. This is due to improved fuel atomization and air-fuel ratio. In comparison of EGT in LHR and uncoated engine, an increase of 3.91%, 4.85%, 4.9%, 5.21%, 4.97% and 3.72% at CR-17.5 and 1.83%, 1.49%, 1.87% 0.93%, 0.99% and 2.47% at CR-19 for B20, B40, B60, B80, B100 and diesel respectively, at peak load condition were determined. According to these results, it is seen that the increase in EGT is more noticeable in LHR engines than uncoated engines, as the loss of heat to the coolant and surroundings decrease significantly in LHR engines. This amount of heat is transferred to the exhaust gas because TBC can give off maximum temperature48,63. The variation of EGT with CRs at peak load for all test samples in both uncoated and TBC engine is shown in Figure S-4.
Fig. 9
(a–f). The variation of the EGT at various loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Combustion characteristics
Combustion cylinder pressure (CCP)
The amount of fuel burned during the premixed combustion phase, the first stage of combustion, has an impact on the CCP in a direct injection diesel engine. The characteristics of cylinder pressure reflect the fuel’s ability to mix with air and combust. The combustion process in CI engines consists of two main phases: the premixed phase and the diffusion phase. The premixed phase begins immediately after fuel injection, where the fuel and air mix thoroughly to form a highly flammable mixture before ignition. A critical aspect of this process is the ID, the time interval between the SOI and the SOC. During ignition, this fuel mixture reacts quickly. After the oxygen in this combination is burned, the combustion process enters the diffusion phase, governed by fuel-air mixing64. For an optimum TBCE configuration at CR 17.5, pressure rise was early and peak cylinder pressure was advanced compared to UCE, indicating shorter ignition delay due to higher in-cylinder thermal conditions. This thermally promoted shift of combustion phasing increases pressure–temperature coupling and may lead to an increase in thermal efficiency, particularly for biodiesel-enriched blends53.
The diffusion phase is much longer than the premixed phase65. The fluctuations in CCP relative to CA under rated load (100%) conditions for different CRs across various blends of SOME and diesel are depicted in Fig. 10(a-f) for both LHR and uncoated engines. Diesel has a higher peak cylinder pressure than biodiesel at all engine loads and CRs in both uncoated and coated engines. The peak cylinder pressure for B20, B40, B60, B80, B100 and diesel where 72.36 bar, 71.26 bar, 70.76 bar, 69.48 bar, 67.33 bar and 73.54 bar at CR-17.5 and 73.68 bar, 72.79 bar, 72.31 bar, 71.95 bar, 71 bar and 74.94 bar at CR-19 respectively, at crack angle between (7–10°) away from Top Dead Center (TDC) in uncoated engine. Every occurrence of maximum pressure was seen to take place distinctly after the TDC, which indicates that the engine was operating safely and efficiently. Alternatively, if the maximum pressure occurs near or prior to the TDC position, it can result in significant engine knocking, which in turn can negatively impact the longevity of the engine. Diesel has the highest peak pressure than biodiesel blends, because diesel fuel has higher ignition period with lower viscosity and cetane number in which fuel air mixes readily resulting in proper combustion leading to higher cylinder pressure at peak load. Figure S-5(a – f) describes the fluctuation in cylinder pressure with load (0-100%) for different test fuels at varying CRs in both coated and uncoated engine using SOME blends. When CR increased from 16 to 19, biodiesel blends had more benefits than diesel. Because of their low volatility, biodiesel blends have higher viscosity, better atomization, faster mixing of air fuel, and shorter ID due to higher cetane number and oxygen fortification. Additionally, higher peak pressure is caused by less penetration with increased cone angle66.
For LHR engine peak pressure will be higher at all CRs with rises in load for all the samples when compared to uncoated engine. In TBC, the engine burning starts earlier than for uncoated engines at all engine loads and CRS with a decrease in ignition period and advanced injection timing. Consequently, the maximum cylinder pressure reaches an elevated level for all test fuels and further observed that CA position came very close to TDC in the expansion stroke. The Fig. 10(d – f) shows that peak pressure for TBC engine was recorded to be 73.54 bar, 72.36 bar, 71.26 bar, 70.76 bar, 69.48 bar and 74.55 bar at CR-17.5 and 74.94 bar, 73.87 bar, 72.75 bar, 72.29 bar, 71.95 bar and 76.58 bar at CR-19 for B20, B40, B60, B80, B100 and diesel respectively at 100% load between the CA position (6–8°) after TDC. In TBC engine peak cylinder pressure is measured at the CA between (6–8°) ATDC, but for uncoated engine it is shifted slightly away from TDC (8–10°) for all test fuels and CR at maximum load. In the present work, the TBC engine had presented a higher maximum cylinder pressure of 76.58 bar for diesel, and 74.94 bar for B20 between 6–8° ATDC. This is related to the improving CR and the heat insulation effect caused by the mullite coating, which leads to higher in-cylinder temperature and faster heat release39. On the other hand, uncoated engines had the trend of delayed peak pressure (8–10° ATDC). The reduction in ignition delay and improved combustion with SOME blends further support this trend, aligning well with previous findings. Similar improvements have been also reported by the authors earlier during studies on biodiesel in TBC-fitted engine32.
Fig. 10
(a–f). The variation of cylinder pressure with CA and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Heat release rate
The HRR is used to determine the SOC, quantify the amount of fuel burned, and identify variations in the fuel’s combustion rate. The cetane number of the fuel indicates its ignition quality; a higher cetane number results in a shorter ID period. The chemical and physical processes occurring during the ID period are generally endothermic, causing the combustion model to exhibit negative heat release during this period, which quickly turns positive upon auto-ignition. After the ID, the pre-mixed air-fuel mixture burns rapidly, producing the first peak in the HRR. Once premixed combustion is completed, the process transitions to the diffusion phase, where the burning rate is maintained by the presence of a combustible mixture67. Figure 11(a-f) compares the effects of CR on the HRR with diesel and scum oil biodiesel blends in relation to CA for both TBC and uncoated engine operations. As illustrated in Fig. 11(a–c), with the increase of scum oil biodiesel blend, a shorter ID period occurred than that of diesel. As a result, the maximum rate of heat release during the combustion phase, when fuel and air are mixed, gradually decreases and shifts to earlier points in the engine’s rotation. The observed result can be ascribed to the decreased fuel injection quantity during the shorter period between ignition and combustion, as well as the lower energy content of the scum oil biodiesel68.
It has been observed that the maximum HRR decreases at lower compression ratio and gradually increases at higher compression ratio for all test fuels at max load. However, the peak HRR for convention fuel is larger than that of biodiesel blends at all CRs. This is due to the extended period it takes for the ignition to occur, which leads to a greater buildup of the fuel-air combination within the cylinder. The reduced density and viscosity of diesel fuel enable enhanced fuel mixing and atomization, resulting in a greater rate of heat release. The max HRR are 46.97, 44.78, 43.12, 39.87, 36.34 and 47.49 J/°CA at CR 17.5 and 45.82, 44.12, 41.85, 41.79, 41.23 and 47.97 J/°CA at CR 19 for B20, B40, B60, B80, B100 and diesel respectively under max load condition in uncoated engine. During peak load, the mullite-coated engine fueled with diesel exceeds SOME mixes in terms of highest HRR, regardless of the CRs. The variation is due to the extended period it takes for diesel fuel to ignite, along with its greater energy content, as well as the higher operating temperature that is typical of engines equipped with TBC.
From Fig. 11(d–f) it was noted that at higher engine load, the HRR for coated engine is greater and occurs at a CA very close to TDC i.e., between (2 − 0°) BTDC (before top dead centre). As seen in the graphs the net HRR was marginally higher for coated engines than engines with uncoated, and it increases with rise in compression ratio. In uncoated engine retardation of injection timing increases the ignition period due to which premixed combustion phase is shortened resulting in lower HRR. The max HRR for LHR engines is 46.6, 45.97, 44.78, 43.12, 39.87 and 47.97 J/°CA at CR-17.5 and 48.68, 45.82, 44.12, 41.85, 41.79 and 49.61 J/°CA at CR-19 for B20, B40, B60, B80, B100 and diesel respectively at peak load. In the present study, the maximum HRR in TBC engine was found at 49.61 J/°CA and 48.68 J/°CA for diesel and B20 at a compression ratio 19, respectively. These values are in agreement with the previously reported HRR rise of 4.9–7.8% when coated engines are compared to uncoated69. The enhanced HRR and earlier combustion peaks in this study result in better combustion quality (i.e., more efficient combustion), which can be attributed to an optimized compression ratio, lower thermal loss resulting from the mullite coating and the excellent ignition quality of scum oil methyl ester, which collectively facilitate rapid and complete combustion. The combustion span graphs for different biodiesel blends and diesel with different compression ratio for with and without coated engine are illustrated in Figure S-6 (a-f) which is presented in supplementary material.
Fig. 11
(a–f). The variation of HRR with CA and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engine.
Combustion parameters
In compression ignition (CI) engines, combustion is typically characterized by key sequential phases: ignition delay (ID), premixed combustion, diffusion combustion, and afterburning. The nature and efficiency of these phases largely depend on the physicochemical properties of the fuel used. In the current study, critical combustion parameters such as start of injection (SOI), start of combustion (SOC), ignition delay (ID), combustion duration (CD), and cumulative heat release rate (CHRR) were systematically evaluated for various blends of Scum Oil Methyl Ester (SOME) and diesel under different compression ratios (CRs) and engine configurations (uncoated and thermal barrier-coated). The determination of SOI was based on a threshold needle lift of 0.01 mm, marking the onset of dynamic fuel injection70. The injection delay (IND), defined as the difference between static and dynamic fuel injection timing, and SOC were derived using pressure derivative analysis. The SOC corresponds to the crank angle (CA) at which the second derivative of in-cylinder pressure crosses zero prior to its maximum slope an indicator of rapid combustion initiation63.
Ignition delay, the interval between SOI and SOC, is a crucial parameter influenced by the fuel cetane number, viscosity, density, and oxygen content. It plays a pivotal role in shaping combustion characteristics such as peak cylinder pressure (PCP), HRR, and SOC timing. In this study, biodiesel blends exhibited moderately delayed SOI compared to diesel, with observed delays of −2.635°CA and − 1.06°CA at CR 16 under full load in uncoated and coated engines, respectively71. This delay is primarily due to the higher viscosity and bulk modulus of SOME, leading to increased nozzle pressure and slower fuel response. As CR increased, SOI timing in uncoated engines aligned more closely with the manufacturer specification, deviating only by 0.751°CA at CR 17.5 and − 0.212°CA at CR 19. In contrast, TBC engines showed slight advancement in SOI by 0.766°CA at CR 17.5 and 0.682°CA at CR 19 reflecting the impact of increased cylinder wall temperature and reduced viscosity of biodiesel during injection. The higher cetane number and lower aromatic content of SOME contributed to earlier SOC, reducing ID in both engine configurations, especially at higher loads and CRs54.
The combustion duration (CD) measured between CA05 (start of combustion) and CA90 (end of combustion) was generally longer for biodiesel blends compared to diesel, primarily due to their lower heating value and higher vaporization enthalpy. Despite this, increasing CR led to improved atomization and air-fuel mixing, which in turn shortened CD in both uncoated and TBC engines. In uncoated engines, the CD reduction from CR 16 to 17.5 ranged between 2.43°CA and 3.99°CA across all blends and further decreased by 2.29°CA to 5.66°CA when CR increased to 1964. Similarly, in TBC engines, CD was reduced further by 0.94°CA to 3.89°CA at CR 17.5 and by 0.31°CA to 3.89°CA at CR 19 compared to uncoated counterparts. These improvements in CD are attributed to the enhanced combustion environment in TBC engines, which promote higher cylinder temperature, better atomization, and faster oxidation, leading to more complete combustion. These trends were consistent with other studies involving TiO2-coated engines running on Pongamia-based biodiesel blends56,61.
The rate of pressure rises (RoPR), an indicator of combustion intensity and knocking tendency, increased with both engine load and CR in all test conditions. However, diesel consistently showed a higher RoPR than biodiesel blends due to its lower cetane number and longer ID, which allows for a larger fuel-air premix before ignition. In uncoated engines, the average RoPR increased by 0.2 bar/°CA when CR was raised from 16 to 17.5 and by 0.76 bar/°CA when increased to CR 19. In TBC engines, the mean RoPR increased by 0.27 bar/°CA and 0.25 bar/°CA at CR 17.5 and CR 19, respectively, compared to uncoated engines48,67. These results align with previous findings in engines using Karanja and roselle biodiesel blends, also demonstrated a rise in RoPR with CR. Cumulative heat release rate (CHRR), representing the total energy released during combustion, is a key metric for assessing combustion efficiency. Across all test fuels and configurations, CHRR increased with CR. Diesel consistently exhibited higher CHRR than biodiesel blends due to its higher calorific value and superior atomization characteristics. SOME blends showed comparatively lower CHRR because of their higher density, viscosity, and oxygen content, which leads to slower and more diffused combustion58,68.
Nonetheless, TBC engines showed enhanced CHRR across all fuel blends due to increased combustion chamber temperature, better premixed zone formation, and improved oxidation. At full load in uncoated engines, CHRR increments from CR 16 to 17.5 ranged from 24.3 J to 29.98 J across blends, and from 17.08 J to 29.27 J when CR increased to 19. In TBC engines, the CHRR gains were even more notable ranging from 3.52 J to 8.55 J at CR 17.5 and from 5.78 J to 7.88 J at CR 19, depending on the blend. These enhancements confirm that thermal barrier coating, combined with higher CR, can significantly improve the combustion efficiency of biodiesel blends, narrowing the performance gap with diesel65. The observed trends in CHRR also correlate well with in-cylinder pressure, RoPR, and BTE values, reinforcing the mechanistic consistency of the results. Overall, this study demonstrates that biodiesel blends, particularly B20, can perform comparably to diesel in both standard and TBC engines when operated at higher CR. The application of thermal coating helps offset the limitations of biodiesel, such as lower calorific value and higher viscosity, by enhancing the combustion environment. Improved SOC, reduced ID, shorter CD, and elevated CHRR in coated engines support the viability of biodiesel as a sustainable alternative fuel, especially when paired with engine design modifications like LHR and TBC configurations28.
Ignition lag
Ignition delay (ID) is a critical phase in compression ignition (CI) engines, representing the interval between the start of fuel injection and the onset of combustion. It is composed of both physical and chemical delay periods. The physical delay involves atomization, vaporization, air–fuel mixing, and heating of fuel droplets, while the chemical delay refers to the time required for pre-flame reactions to progress toward auto-ignition72. The ignition characteristics are influenced by several physico-chemical fuel properties, including cetane number, viscosity, density, latent heat of vaporization, surface tension, and in-cylinder thermodynamic conditions such as pressure and temperature. A higher cetane number typically indicates a shorter ignition delay, which is especially important in optimizing engine performance and emissions73. In this investigation, ignition delay was experimentally evaluated for uncoated and mullite-coated (TBC) CI engines operating under various compression ratios (CR = 16, 17.5, and 19) and engine loads using diesel and scum oil methyl ester (SOME) blends. The variation in ignition delay as a function of crank angle (CA) was plotted in Fig. 12(a–f), and supporting data is presented in Tables 5 and 6.
Across all tested fuel blends, results demonstrated that ID decreased with increasing load and compression ratio. At higher engine loads, elevated in-cylinder temperature and residual gas content promoted faster ignition due to favorable thermal conditions. Moreover, the oxygen-rich nature and higher cetane number of SOME contributed to consistently shorter ignition delay than diesel across all CRs and loads74. For instance, in uncoated engine at full load, the ID decreased as CR increased from 16 to 17.5 and then to 19. For B20, ID dropped from 16.24° to 15.62° CA (CR 16 to 17.5), and further to 14.82° at CR 19. Similar trends were observed for B40, B60, B80, and B100. Diesel also showed a reduction, but ID remained higher than that of SOME blends due to its lower cetane number and lack of intrinsic oxygen content. The coating of engine components with mullite further influenced ID behavior. The ceramic layer enhanced in-cylinder temperature retention, improving fuel vaporization and promoting earlier combustion. In TBC engines, ignition delay periods were significantly reduced for all fuels. At full load and CR 19, ID for B20 decreased from 15.62° (uncoated) to 13.62° CA (coated), indicating a marked improvement in ignition characteristics due to thermal insulation and improved combustion chamber conditions. This trend held true across other blends as well ID for B100 at CR 19 fell from 14.75° (uncoated) to 12.75° (coated)75. These observations confirm that the combination of biodiesel with ceramic-coated combustion chamber leads to faster ignition, especially at higher CRs and loads. The shortened ID facilitates an earlier start of combustion (SOC), as evident in Figs. 10 and 11, contributing to a higher peak pressure and a more efficient premixed combustion phase. This is particularly important in engine operating with SOME, where chemical reactivity is enhanced due to high oxygen availability and lower aromatic content, promoting rapid flame propagation.
The rise in compression ratio also plays a pivotal role. It leads to increased air temperature during compression, lowering the fuel auto-ignition threshold. Combined with the TBC insulating effect, this results in more favorable condition for auto-ignition. Furthermore, reduced viscosity and better atomization at high temperatures facilitate finer fuel dispersion and better mixing with air, which collectively support faster combustion initiation. The study also showed that the ignition delay is longer for diesel under all conditions due to its relatively inferior volatility, lower cetane index, and absence of internal oxygen. In contrast, SOME and its blends demonstrate consistently shorter delay periods due to better pre-flame chemistry and enhanced molecular oxygenation, especially under thermally optimized conditions provided by the TBC engine configuration13,15. Experimental comparisons also revealed that ID variation correlates with changes in other combustion parameters such as cylinder pressure, heat release rate (HRR), and combustion duration (CD). A shorter ID typically resulted in earlier and more intense HRR peaks, improving thermal efficiency while minimizing unburned fuel residues58. In summary, this investigation confirms that ignition delay is significantly influenced by fuel composition, engine coating, compression ratio, and load. The combination of biodiesel blends with ceramic-coated engine at higher CR fosters rapid ignition, enhancing combustion efficiency. The synergy between fuel properties and engine design parameters offers a viable path for cleaner and more efficient CI engine operation, with particular promise for sustainable biodiesel application.
Fig. 12
(a–f) The fluctuations in ID at various loads and CRs (16, 17.5, & 19) for blends of SOME in both coated and uncoated engines.
Table 5 Combustion and fuel injection characteristics of UCE for different fuel samples and CR at peak engine load.
Table 6 Combustion and fuel injection characteristics of TBCE for different fuel samples and CR at peak engine load.
Emission characteristics
According to theory, the ideal combustion of the fuel-air mixture in the diesel engine cylinder would result in the formation of just CO2 and water (H2O), as shown in Eq. 8. Nevertheless, attaining full combustion in real-world situations is difficult due to a range of engine operating factors. The parameters encompassed in this set are the fuel-air equivalency ratio, fuel type, combustion chamber design, oxygen availability, autoignition temperature of the fuel, ignition lag, and fuel vaporization ratio. Fluctuations in various operational parameters lead to energy wastage from the fuel provided. Equation 9 shows that when fuel undergoes incomplete combustion, it produces damaging pollutants including CO, CO2, NOx, HC, and other substances.
$${C_x}{H_y}+z({O_2}) to aleft( {C{O_2}} right)+bleft( {{H_2}O} right)+Energy{text{ }}$$
Emission like CO and HC in the exhaust is very important because which indicates that fuel is not completely utilized such that less chemical energy obtained from the fuel76. Emissions such as CO2 and NOx emitted by CI engine will greatly affect the ozone layer and on human health. The diesel engine emission in with and without coated engine with diesel and SOME blends were evaluated in terms of CO, CO2, O2, NOx, HC and smoke opacity at various CRs (16,17.5 and 19) and at various loading conditions of the engine.
Carbon monoxide (CO)
Carbon monoxide (CO) emissions in CI engines arise primarily from incomplete combustion, often due to insufficient oxygen availability, poor fuel–air mixing, suboptimal injection timing, low injection pressure, and engine design limitations. CO is a harmful, odorless, and colorless gas that must be strictly minimized. Experimental results presented in Fig. 13(a–f) show that CO emissions were consistently lower for all SOME blends compared to diesel across various compression ratios (CRs) in both uncoated and thermal barrier-coated (TBC) engines. At light and medium loads, SOME blends produced noticeably lower CO levels. However, under peak load, CO emissions rose due to richer mixtures and reduced combustion duration. The additional oxygen content in SOME improves oxidation reactions, leading to cleaner combustion. Increasing the biodiesel proportion in the blend further lowered CO emissions, confirming the emission-reducing potential of oxygenated fuels like SOME in LHR and standard CI engines77. Furthermore, under steady-state engine operating conditions, the oxygen content of biodiesel increases the oxidation of CO to CO2, as demonstrated by Eqs. 10 and 11. Despite higher BSFC, biodiesel blends exhibit lower CO emissions due to their inherent oxygen content, higher cetane number, and improved combustion phasing, which collectively enhance oxidation efficiency. Additionally, elevated in-cylinder temperatures in LHR engines accelerate CO-to-CO₂ conversion, further reducing CO emissions even under rich or high-load conditions78.
$$xleft( {CO} right)+Y({O_2}) to left( {C{O_2}} right)+Energy{text{ }}$$
(10)
where, (y=frac{x}{2})
$$C+{O_2} to C{O_2}+Energy{text{ }}$$
(11)
Blends of biodiesel contain higher cetane number, which lowers the probability of fuel rich-zones formation and with advanced injection and combustion when using biodiesel justify the reduction of CO emission37,38. As scrutinized from the Fig. 13(a – f) showed reduced CO emission for all tested samples at increasing CR from 16:1 to 19:1 at maximum load in with and without coated engine. When the CR increased from 16 to 17.5, the CO emission decreased by 9.09%, 15.79%, 25%, 13.33%, 17.14%, and 7.41% for the blends of B20, B40, B60, B80, B100, and diesel respectively at full load for an uncoated engine. Similarly, when the CR is increased from 17.5 to 19, the CO emission decreased by 10%, 11.76%, 6.67%, 25%, 40%, and 12.5% for the same blends. The likely cause for this pattern is the rise in CR and engine load while maintaining the same injection time, resulting in elevated cylinder pressure and air temperature within the cylinder. Which improves atomization for a large amount of fuel and consequently reduces the ignition day period causing improved combustion process which results in reduced CO emission at higher CR. Hirkude et al.79 and Sivaramakrishnan78 reported that CO emission level decrease with increasing biodiesel percentage and compression ratio. As shown in Fig. 13(d–f), mullite-coated diesel engines had greater combustion efficiency for all fuels, reducing CO emissions. Average CO emission reduce in the coated engine was determined to be 10%, 11.76%, 6.67%, 15.38%, 16.67% and 8% at CR-17.5 and 5.26%, 6.25%, 7.14%, 9.09%, 3.09% and 9.09% at CR-19 for B20, B40, B60, B80, B100 and diesel respectively at peak load when compared to uncoated engine. In this study, carbon monoxide (CO) emissions decreased significantly by up to 40% for SOME blends and 12.5% for diesel when the compression ratio increased from 17.5 to 19. Overall, a 20–50% reduction in CO emissions was observed in the Low Heat Rejection (LHR) engine as compression ratio rose from 16 to 19, as shown in Figure S-8. This trend is similar to the results found by Ramasamy et al., where Y2O3–ZrO2 and Al2O3–SiO2 coatings on palm biodiesel-fueled engines effectively lowered CO levels80. The decline in CO emissions is primarily attributed to enhanced in-cylinder heat retention from the mullite-based thermal barrier coating (TBC), improved combustion due to higher wall temperatures, and the inherent oxygen content in SOME, all of which promote more complete combustion and reduced heat loss to the coolant and ambient surroundings.
Fig. 13
(a–f). The variation of the CO at different loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Hydrocarbon (HC) emission
Hydrocarbons (HCs) are mainly caused by incomplete combustion of petroleum diesel fuels, and are typically quantified as parts per million (ppm) of carbon atoms. These hydrocarbons are regarded as exhaust gases and are a matter of environmental concern. Major sources of HC emissions are unburnt fuel-air mixtures, HC emissions bleeding out from engine lubricating oil as well as suboptimal mixing and combustion. The chemical composition of the fuel plays a crucial role in the magnitude of these emissions fuels rich in aromatics and olefins typically produce more reactive hydrocarbon species. Variation of unburned hydrocarbon (UHC) emissions with compression ratio (CR) and engine load is illustrated in Figure 14 (a–f) for mullite-coated (TBC) and uncoated engines operated with SOME-diesel blends. At all test operating modes, SOME blends had lower HC emissions compared to neat diesel. The decrease was likely due to the higher oxygen content (10–13 wt%) in SOME, which leads to an improved combustion of the fuel and its higher cetane number, which shortens ignition delay. Such properties enhance complete combustion and the consequent reduction of fuel-rich and over-lean areas leading to higher HC emissions81.
Typically, HC emissions are a significant problem at very high loads in diesel engines. At high loads, a larger amount of fuel is injected onto the cylinder surface, causing improper fuel distribution with less surplus air and higher wall temperatures, leading to regions where the fuel-air mixture may survive and escape into the exhaust manifold as HC emissions. This study’s results indicate that HC emissions decrease with an increase in compression ratio for all tested fuels in both LHR and uncoated engines82. Hydrocarbon emissions for B20, B40, B60, B80, and B100 decreased by 14%, 18.75%, 18.75%, 12.28%, and 26.67% at CR-17.5 and by 4.44%, 9.3%, 17.5%, 30.55%, and 42.42% at CR-19 compared to diesel in an uncoated engine at full load. Similar results were obtained at a compression ratio of 16. The probable reason for the decrease in HC emissions at higher CRs is that biodiesel blends reach ignition temperature due to the higher wall temperature and pressure inside the engine cylinder, enabling the oxidation reaction with more O2 molecules, which promotes complete combustion. The reductions in hydrocarbon emissions at 100% load when the CR varied from 16 to 17.5 were 20%, 12.5%, 4.17%, 4.26%, 0%, and 19.29%, and when the CR varied from 17.5 to 19, the reductions were 11.11%, 11.63%, 20%, 30.56%, and 21.28% for B20, B40, B60, B80, B100, and diesel, respectively, in an uncoated engine.
Results show that HC emissions are lower in the TBC engine compared to the uncoated engine for all test fuels at maximum load and different CRs, as demonstrated in Figure S-9. Hydrocarbon emissions from the mullite-coated engine decrease significantly compared to the uncoated engine due to the shorter quenching distance, increased after-combustion temperature, and lower flammability limit associated with the TBC engine, resulting in reduced heat loss to the cooling system. By harnessing thermal energy within the combustion chamber, the combustion of the fuel-air mixture is enhanced, leading to decreased HC emissions83. The reductions in HC emissions at full load in the LHR engine are 4.17%, 6.67%, 9.09%, 17.5%, 18.42%, and 7.55% at CR-17.5 and 7.14%, 7.55%, 5.26%, 5.88%, 10%, and 6.82% at CR-19 for B20, B40, B60, B80, B100, and diesel, respectively, compared to the standard diesel engine. In the present study, HC emissions were reduced by 42.42% for B100 and 26.67% for B20 with increased CR from 17.5 to 19, with overall reductions ranging from 12 to 42% in the uncoated engine and 5–18% in the LHR engine. These results are in close agreement with previous studies, including 39.1% with Orange Peel biodiesel in TBC engine69,80. The improved performance in the mullite-coated engine is primarily due to higher in-cylinder and gas temperatures, which promote efficient oxidation. This results in a more complete combustion, especially on oxygen rich SOME blends, leading to lower unburned hydrocarbon emissions.
Fig. 14
(a–f). The variation of the HC at different loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Nitrogen oxide (NOx) emission
Nitrogen oxides (NOx) have been and continue to be one of the most challenging and complex emissions faced by biodiesel and its blends in compression ignition (CI) engines. NOx is generated mostly by thermal oxidation of atmospheric nitrogen, promoted at high in-cylinder temperatures (usually above 1500 K), high oxygen levels and long combustion times. These conditions facilitate the Zeldovich mechanism, the dominant pathway for NOx formation84. The oxygen content, cetane number of biodiesels, and its properties such as density, viscosity, and compressibility have a considerable effect on fuel atomization, injection pressure, and combustion characteristics, leading to NOx emission. Figure 15(a-f) depicts the influence of engine load on NOx emission for different SOME-diesel fractions at different compression ratios (CRs) for uncoated and TBC engines. NOx emissions had a tendency to increase with increased CRs and increasing biodiesel contents, as a result of the higher combustion temperature and more complete fuel oxidation. Also, the decreased soot generation with biodiesel, known as combustion heat absorber, increases flame temperature for NOx formation. The contribution to higher adiabatic flame temperatures is due also to the existence of unsaturated bonds in SOME. In general, although combustion efficiency is enhanced due to biodiesel oxygenated composition of fuel and its thermal behavior, NOx abatement becomes a major issue with the use of oxygen-based biodiesel especially under high CRs85,86.
For uncoated engines, NOx emissions for B20, B40, B60, B80, B100, and diesel increased on average by 21.38%, 22.49%, 23.33%, 22.27%, 14.97%, and 20.27%, respectively, when the CR was increased from 16 to 17.5. With a further increase in CR from 17.5 to 19, the corresponding values were 6.21%, 5.49%, 4.72%, 4.15%, 4.41%, and 6.77%, respectively. This indicates that higher CR benefit biodiesel more than conventional diesel, due to its volatility, higher viscosity, density, and cetane number, which enhance performance at higher CRs. Diesel and SOME blends exhibit higher NOx emissions in LHR engines at all CRs compared to uncoated engines, as shown in Fig. 15(d–f). The increased NOx formation in TBC engines is due to higher post-combustion temperatures and longer combustion periods, causing an earlier SOC and shifting the peak temperature and pressure closer to TDC64. Consequently, most of the fuel burns in the premixed stage, leading to increased NOx formation. Even in coated engines, NOx emissions for SOME blends are higher than for diesel fuel due to the higher oxygen content in vegetable oils and traces of nitrogen in biodiesel78. NOx emissions for B20, B40, B60, B80, B100, and diesel were found to be 827, 840, 855, 855, 871, 894, and 814 ppm at CR-17.5, and 838, 852, 868, 882, 915, and 826 ppm at CR-19, respectively, for mullite-coated engines at higher load. This shows that for coated engines, NOx emissions increased by 1.59%, 1.45%, 1.78%, 1.87%, 2.76%, and 2.26% at CR-17.5, and by 2.07%, 2.16%, 2.12%, 2.2%, 3.16%, and 1.97% at CR-19 compared to uncoated engines. Finally, SOME and its blends used in both LHR, and standard diesel engines result in higher NOx formation compared to diesel, as shown in Figure S-10, consistent with findings from various studies. To mitigate NOx emissions without compromising engine performance, strategies such as Exhaust Gas Recirculation (EGR), injection timing retardation, split injection, and water or steam injection can effectively lower peak combustion temperatures. In thermal barrier-coated engines, where in-cylinder heat retention is higher, post-combustion control technologies like Selective Catalytic Reduction (SCR) and Lean NOx Traps (LNTs) become essential for achieving further NOx reduction87.
Fig. 15
(a–f) The variation of the NOx at different loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Smoke opacity
Smoke opacity is significant because it gives indication of the concentration of pollutants due to soot particle formation with the dearth of oxygen content and their oxidation rate even in the presence of some hydrocarbon, paying way for higher smoke emission which leaves smokestack. Higher smoke emission is generated when fossil fuels are burnt in the diesel engine due to extreme air deficiency. In CI engine, the main combustion of fuel occurs via diffusion mechanism during which the atomized fuel particles are broken down into two primary elements as carbon (soot formation) that will be oxidized in the reaction zone (soot oxidation)88. If there is deficiency of oxygen content and if combustion temperature does not support the oxidation process, smoke (carbonaceous particle) will be released in the exhaust gaseous. Fuels with higher viscosity tend to increase smoke emissions due to a lower air-fuel mixing rate and larger mean fuel spray droplet size. Smoke emission from diesel engines results from incomplete combustion, which can lead to higher fuel consumption and oil loss at elevated temperatures. Figure 16(a–f) illustrates the relationship between smoke opacity emissions and load for all tested fuels (SOME) at various CRs in both uncoated and coated engines. Using biodiesel blends, there is a significant reduction in smoke output in both LHR and uncoated engines when the CR rises from 16 to 19, irrespective of load level. Fuel consumption decreases due to better combustion efficiency and more oxygen-rich molecules, which enable more complete combustion. The decrease in smoke emission with higher biofuel percentages is due to the reduced carbon content in blended fuels, which have fewer C-C bonds compared to diesel, leading to lower smoke opacity. The impact of biodiesel addition on smoke emission is more significant at higher loads because the premixed combustion fraction decreases as the diffusion CD increases87. At maximum load, for every 20% increase in biodiesel blending in diesel, the smoke opacity reduction at CR-17.5 is 8.09%, 13.64%, 12.74%, 17.17%, and 24.76%, and the corresponding values at CR-19 are 5.27%, 9.91%, 15.19%, 17.45%, and 24.79% lower than standard diesel. Figure S-11 shows that at peak load, smoke opacity is lower at higher CRs compared to lower ones. This is because higher CRs increase the operating temperature and pressure, enhancing combustion efficiency. Improved reaction between fuel and air reduces smoke emissions in both coated and uncoated engines. Smoke emissions in the LHR engine decreased by 5.94%, 5.37%, 12.82%, 12.45%, 11.23%, and 8.8% at CR-17.5 and by 6.95%, 5.62%, 7.66%, 12.33%, 12.41%, and 8.12% at CR-19 for B20, B40, B60, B80, B100, and diesel, respectively, compared to the uncoated engine. These values show that all biodiesel blends in the coated engine reduce smoke opacity, despite SOME’s higher viscosity and molecular weight. The excess heat in the TBC engine raises combustion chamber temperature, oxidizing soot and promotes complete combustion due to abundant oxygen, minimizing smoke emissions. These findings align with other studies in the literature4,29.
Fig. 16
(a–f). The variation of the smoke opacity at different loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engines.
Carbon dioxide (CO2)
CO2 emissions in CI engines result from the thorough burning of traditional fuels, functioning as a measure of combustion effectiveness. CO2 is a significant greenhouse gas contributing to global warming. Higher CO2 concentrations in the exhaust indicate improved combustion efficiency, contrasting with CO emissions. Factors influencing CO2 emissions include viscosity, density, combustion chamber design, air-fuel equivalence ratio, SOI, fuel line pressure, and engine speed. The CO2 emissions from biodiesel combustion are less impactful on the atmosphere since they are primarily absorbed by plants, trees, and crops. The simultaneous reduction in CO and CO2 emissions for biodiesel blends is attributed to their inherent oxygen content, which promotes complete combustion with lower carbon intensity per unit energy. Despite improved oxidation, the overall CO2 output remains lower due to biodiesel’s lower carbon content and biogenic origin, resulting in a reduced net greenhouse gas impact29.
Figure 17(a–f) presents the results of CO2 emissions for different engine loads and CRs in both uncoated and coated engines using blends of SOME and diesel fuel. The data shows that CO2 concentrations rise with increasing load and CRs for all test fuels in both engine types. Additionally, CO2 emissions decrease with higher proportions of SOME blends compared to diesel fuel. Several studies have demonstrated a decrease in CO2 emissions while utilizing biodiesel blends in diesel engines. This reduction can be due to the presence of 10–12% excess oxygen molecules in biodiesel. The highest CO2 emission values for diesel were 10.12% at CR-17.5 and 10.57% at CR-19, while for B20, B40, B60, B80, and B100 at CR-17.5, the values were 9.95%, 9.7%, 9.22%, 9.07%, and 8.93%, respectively. At CR-19, the values were 10.21%, 9.63%, 9.42%, 9.11%, and 8.92%, respectively, all lower than those for diesel at full load in an uncoated engine. The increase in CO2 emissions with load is due to the higher amount of fuel injected, leading to increased combustion temperature and oxidation rates. Higher CRs elevate post-combustion temperatures and, with sufficient oxygen in the engine chamber, enhance CO2 emissions, indicating efficient burning5.
Engines with mullite coating show further increases in CO2 emissions for all samples at rated engine load. CO2 formation in LHR engines at operating load is 10.24%, 9.88%, 9.48%, 9.21%, 8.96%, and 10.64% at CR-17.5 and 10.43%, 10.12%, 9.74%, 9.55%, 9.15%, and 10.97% at CR-19 for B20, B40, B60, B80, B100, and diesel, respectively, as shown in Fig. 18(d–f). There is a slight increase in CO2 emissions in LHR engines, with values of 2.91%, 1.85%, 2.82%, 1.54%, 0.33%, and 5.14% at CR-17.5 and 2.15%, 5.08%, 3.39%, 4.83%, 2.57%, and 3.78% at CR-19, respectively, compared to uncoated engines, as depicted in Figure S-12. Various studies have shown that using biodiesel can reduce global greenhouse gas emissions, leading to a 50–80% decrease in CO2 emissions compared to fossil fuels9.
Fig. 17
(a–f). The variation of the CO2 at different loads and CRs (16, 17.5 & 19) for SOME blends in both coated and uncoated engine.
Table S-6 in the supplemental paper details the experimental results for performance, combustion, and exhaust gas emission parameters of mullite-coated CI engines at CR 19 for all fuel samples (B20, B40, B60, B80, B100, and Diesel). The comparison is conducted at peak load, ranking the results from highest to lowest. Figure S-13 illustrates the average percentage differences in performance, combustion, and exhaust emission parameters for the test samples B20, B40, B60, B80, and B100 at maximum load compared to conventional diesel fuel (D100). This comparison applies to both coated and uncoated CI engines at CR 17.5 and 19.
