Engine Performance Research Articles

Experimental Investigation of Energy Analysis of Methanol-Gasoline Mixtures at Different Torque Values

Despite the increasing use of petroleum-based fuel as a result of ever-increasing energy demands, the search for alternative fuels remains important. In this study, in a Lombardini LGW 523 brand 2-cylinder 4-stroke water-cooled engine, different volumetric ratios of methanol and gasoline fuel mixtures (M10, M20, M30, M40) were used at 4 different torque values (5, 10, 15, 20 Nm) at a constant 3000 engine speed. It is discussed. As a result of the study, engine performance, emission and energy analysis examination was carried out experimentally. Keywords: Energy, exergy, internal combustion engine, methanol fuel

Predictive modeling of engine performance and emissions for castor oil ethyl ester biodiesel blends: A Gaussian process regression approach

Replacing fossil fuels with cleaner alternatives is essential. This study examines a biodiesel-diesel blend containing 8 % castor oil ethyl ester (COEE8) and its impact on engine performance, combustion characteristics, and exhaust emissions. A single-cylinder diesel engine was tested under consistent conditions: an engine speed of 1500 rpm, varying engine loads (25 %, 50 %, and 75 % of maximum torque), and compression ratios (16, 17, and 18). Engine-out emissions were measured with Testo flue gas analyzers. The results showed that COEE8 combustion significantly decreased HC, CO, and smoke emissions compared to diesel fuel but increased NOx emissions. Additionally, COEE8 exhibited comparable brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) to diesel fuel. The optimal engine operating parameters were determined using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a multi-objective optimization technique. Due to limited data availability, Gaussian Process Regression (GPR), a machine learning algorithm for small datasets, modeled the multi-objective functions with compression ratio and engine load as input variables. The GPR model demonstrated high prediction performance across all output parameters (BSFC, BTE, HC, smoke, NOx, and CO) for both diesel and COEE8 fuels, with average coefficients of determination (R2) of 0.9896 and 0.9953, respectively, indicating a strong correlation between predicted and actual values. The mean absolute percentage error (MAPE) was also low, averaging 3.11 % and 2.26 %, respectively, demonstrating the model's accuracy. Using the GPR model, the NSGA-II identified the optimal trade-off between NOx and smoke index for both diesel and COEE8 fuels. The optimal compression ratio was 16 for both fuels, while the optimal engine load varied, ranging from 20 % to 30 % for COEE8 and around 30 % (almost 40 %) for diesel fuel.

Vehicle Engine Cooling System: Review Research

This study reveals that nano-refrigerants can improve the overall performance of these systems, particularly when used as nanoparticles in conjunction with a base refrigerant. The results show that nano refrigerants outperform base liquids in warm conductivity, and the size of the nanoparticles affects this conductivity. The thickness of nano-refrigerants shows a vertical pattern as the volume of particles increases, while it decreases with temperature increases. Traditional models, such as the Hamilton-Crosser and Einstein models, fail to accurately predict the warm conductivity and consistency of nanoliquids when temperature is considered. Even a small amount of nanoparticles can significantly improve the base liquid’s conductivity. The use of nano-fluids results in an improved convective intensity transfer coefficient for all volume concentrations of nanoparticles compared to water under different working conditions. This study delves into the characterization of nanofluids for vehicle engine cooling systems, focusing on their thermal properties and heat transfer capabilities. Through an analysis of thermal conductivity, heat transfer coefficients, viscosity, and nanoparticle size, the research aims to optimize the design and implementation of nanofluid-based cooling systems to enhance engine performance and fuel efficiency. By investigating the impact of different nanoparticles and concentrations on these properties, the study provides insights into the potential of nanofluids to improve cooling efficiency in automotive applications. The findings highlight the importance of understanding the relationship between nanoparticle characteristics and thermal properties for the effective utilization of nanofluids in vehicle cooling systems. By synthesizing findings from previous studies, this review aims to provide insights into the potential benefits of utilizing nanofluids in enhancing cooling efficiency and overall engine performance in automotive systems. The analysis underscores the importance of considering nanoparticle characteristics in optimizing nanofluid formulations for effective heat transfer in vehicle cooling systems.

Impact of Using n-Octanol/Diesel Blends on the Performance and Emissions of a Direct-Injection Diesel Engine

This study evaluates the viability of n-octanol as an alternative fuel in a direct-injection diesel engine, aiming to enhance sustainability and efficiency. Experiments fueled by different blends of n-octanol with pure diesel were conducted to analyze their impacts on engine performance and emissions. The methodology involved testing each blend in a single-cylinder engine, measuring engine performance parameters such as brake torque and brake power under full-load conditions across a range of engine speeds. Comparative assessments of performance and emission characteristics at a constant engine speed were also conducted with varying loads. The results indicated that while n-octanol blends consistently improved brake thermal efficiency, they also increased brake-specific fuel consumption due to the lower energy content of n-octanol. Consequently, while all n-octanol blends reduced nitrogen oxide emissions compared to pure diesel, they also significantly decreased carbon monoxide, hydrocarbons, and smoke opacity, presenting a comprehensive reduction in harmful emissions. However, the benefits came with complex trade-offs: notably, higher concentrations of n-octanol led to a relative increase in nitrogen oxide emissions as the n-octanol ratio increased. The study concludes that n-octanol significantly improves engine efficiency and reduces diesel dependence, but optimizing the blend ratio is crucial to balance performance improvements with comprehensive emission reductions.

Statistics of quantum heat in the Caldeira-Leggett model.

Nonequilibrium fluctuation relation lies at the heart of the quantum thermodynamics. Many previous studies have demonstrated that the heat exchange between a quantum system and a thermal bath initially prepared in their own Gibbs states at different temperatures obeys the famous Jarzynski-Wójcik fluctuation theorem. However, this conclusion is obtained under the assumption of Born-Markovian approximation. In this paper, going beyond the Born-Markovian limitation, we investigate the statistics of quantum heat in an exactly non-Markovian relaxation process described by the well-known Caldeira-Leggett model. It is revealed that the Jarzynski-Wójcik fluctuation theorem breaks down in the strongly non-Markovian regime. Moreover, we find the steady-state quantum heat within the non-Markovian framework can be widely tunable by using the quantum reservoir-engineering technique. These results are sharply contrary to the common Born-Markovian predictions. Our results presented in this paper may update the understanding of the quantum thermodynamics in strongly coupled and low-temperature systems. Moreover, the controllable heat may have some potential applications in improving the performance of a quantum heat engine.

Work and efficiency fluctuations in a quantum Otto cycle with idle levels.

We study the performance of a quantum Otto heat engine with two spins coupled by a Heisenberg interaction, taking into account not only the mean values of work and efficiency but also their fluctuations. We first show that, for this system, the output work and its fluctuations are directly related to the magnetization and magnetic susceptibility of the system at equilibrium with either heat bath. We analyze the regions where the work extraction can be done with low relative fluctuation for a given range of temperatures, while still achieving an efficiency higher than that of a single spin system heat engine. In particular, we find that, due to the presence of "idle" levels, an increase in the interspin coupling can either increase or decrease fluctuations, depending on the other parameters. In all cases, however, we find that the relative fluctuations in work or efficiency remain large, implying that this microscopic engine is not very reliable as a source of work.

Synergistic effect of hydrogen induction on the performance, combustion and emission phenomenon of dual-fuel CI engine with Chlorella vulgaris algae oil and its methyl ester

The present work aims to investigate the cumulative effect of hydrogen enrichment with the fuels of diesel, Neat chlorella vulgaris oil (NCVO), and Chlorella vulgaris methyl ester (CVME) in a mono-cylinder CI engine. The performance, combustion, and emission parameters of diesel, NCVO, and CVME are analyzed and compared to the findings obtained with hydrogen induction in the intake manifold under dual fuel configuration. With the effect of hydrogen infusion, the metrics of performance, combustion and emission parameters are improved. Under maximum load condition, the induction of hydrogen escalates the BTE from 32.74%, 28.01%, and 23.02%–35.39%, 31.40%, and 28.12% with diesel, CVME, and NCVO at maximal hydrogen share energy of 14.38%, 11.50% and 9.61% respectively. Alongside, the enrichment of hydrogen declines the carbon monoxide (CO) from 4.13, 10.38, and 19.24 g/kWh to 3.16, 8.02, and 15.26 g/kWh, unburned hydrocarbons (UHC) from 0.51, 0.71, and 2.02 g/kWh to 0.36, 0.50, and 1.64 g/kWh, smoke opacity from 55%, 75%, and 85%–43%, 67%, and 71% with diesel, CVME, and NCVO respectively. However, the hydrogen's significant flame velocity and immense calorific value confect to an elevated combustion temperature and subsequent rise in heat release rate (HRR) to, cylinder pressure, and emission of nitrogen oxide (NO). Among the tested fuels, diesel posses maximum HRR and cylinder pressure of 80.46 J/0CA and 88.67 bar respectively. Maximum specific NO emission was observed with CVME having 10.80 g/kWh, whereas diesel and NCVO has 9.49 g/kWh and 4.93 g/kWh respectively.

AI-Based Detection of Surge and Rotating Stall in Axial Compressors via Dynamic Model Parameter Estimation

Compressors are an essential component of aircraft engines. Their design and operation must be extremely reliable as engine safety and performance depend greatly on these elements. Axial compressors exhibit instabilities, such as surge or rotating stall, in a region close to the peak of their performance curves. These fluid dynamic instabilities can cause drops in efficiency, stress on the blades, fatigue, and even failures. Compressors are handled therefore by operating with a safety margin far from the surge line. Moreover, models able to predict onset instabilities and to reproduce them are of great interest. A dynamic system able to describe successfully both surge and rotating stall is the model presented by Moore and Greitzer That model has also been used for developing control laws of the compressor dynamics. The present work aims at developing an artificial neural network (ANN) approach able to predict either the permanence of the system in stable working condition or the onset instabilities from a time sequence of the compressor dynamics. Different solutions were tried to find the most suitable model for identifying the system, as well as the effects of the duration of the time sequence on the accuracy of the predicted compressor working conditions. The network was further tried for sequences with different initial values in order to perform a system analysis that included multiple variations from the initial database. The results show how it is possible to identify with high accuracy both rotating stall and surge with the ANN approach. Moreover, the presence of an underlying fluid dynamic model shares some similarities with physically informed AI procedures.

Numerical Simulation of Workflow for Evaluating Flame Tube Thermocyclic Durability

ABSTRACT This paper explores strategies for extending the operational lifespan of flame tubes in turbofan engines – a critical component for maintaining engine efficiency and reliability, in line with global trends aimed at maximizing the use of laid reserves of aircraft engine performance. Utilizing a combination of advanced computational simulations and empirical research, the study meticulously analyzes the internal processes within the flame tube of the AL-31F turbofan engine. A detailed geometric model and finite element grid were created and adapted to simulate various operating conditions and assess their impact on the flame tube’s performance. Special attention is given to understanding the thermal and mechanical stresses that influence its durability and serviceability. The results, compared against experimental data, validate the simulations and are crucial for identifying critical sections prone to damage, thereby facilitating enhanced decision-making regarding maintenance schedules and overhaul practices. This approach not only aims to minimize downtime and reduce maintenance costs but also extends the service intervals for critical engine components, thereby improving thermocyclic durability based on the damage mechanisms identified.

Control system optimisation of biodiesel-based gas turbine for ship propulsion

<p>Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.</p>

Investigation of the effect of antioxidant on carbon deposition of compression ignition engine for carbon emissions reduction

The study aimed to assess carbon deposition tendencies in mustard biodiesel with varying oxidation levels by examining residual carbon and existent gum content. Gas chromatography–mass spectrometry (GC-MS) and infrared spectrometry were used to analyze the chemical components of residual carbon and existent gum in mustard biodiesel and an antioxidant before and after oxidation. The research investigated carbon deposition on the exhaust valve of a compression ignition engine using mustard biodiesel blended with clove oil as an antioxidant. The engine ran for 100 h on each fuel sample: diesel (D100), biodiesel blended fuel (B30), and clove oil (3000 ppm). After completing 100 h on each sample, engine exhaust was analyzed for carbon deposition using Scanning Electron Microscopy (SEM) and Energy Dispersive Microscopy (EDX).The findings revealed increased carbon deposition when using biodiesel blended fuel compared to diesel fuel. However, the addition of an antioxidant to the biodiesel blend resulted in reduced carbon deposition. This suggests that the use of antioxidants effectively mitigated carbon buildup in the engine’s exhaust when using biodiesel blends. The aim of this study is to enhance engine performance, increase engine life, and reduce pollution in the environment.

Design of Variable Valve Timing and Electronic Control for Honda R18A

Abstract: This paper explores the Variable Valve Timing and Lift Electronic Control (VTEC) system developed by Honda, focusing on its mechanisms, design methodology, and impact on engine performance. VTEC optimizes engine efficiency and power by switching between cam profiles suited for low-RPM stability and high-RPM power output, controlled by the engine's computer based on operational conditions. We examine the intricate workings of the VTEC system, detailing its hydraulic actuation and the resulting enhancements in torque and horsepower. Our study centers on the Honda R18A engine, targeting increased maximum RPM and power output. Using theoretical analysis validated through Ricardo Wave software and MATLAB simulations, we determine optimal valve lift and timing to achieve the desired performance. Additionally, the research addresses the geometric parameters influencing airflow through the valves, providing a comprehensive understanding of the VTEC system's contribution to engine efficiency and driving experience. This investigation not only underscores the technological advancements in variable valve timing but also presents practical insights for automotive engineering applications.

Effect of Heat Leakage on Relativistic Quantum Lenoir Engine Performance with a Massless Boson as Working Substance in the Infinite Potential Box

A study on the effect of heat leakage on power output, thermal efficiency, and reversibility rate in a relativistic quantum Lenoir engine has been conducted. Initially, we analogize the quantum working substance of the engine, a massless boson trapped in an infinite potential box with a movable right wall, as an ideal gas confined in a pistoned cylinder. Then, the total work, heat input, and heat output of each engine cycle which consists of isochoric, adiabatic expansion, and isobaric compression are extracted by applying the concept of quantum thermodynamics. Finally, power output, thermal efficiency, and reversibility rate of the engine are calculated for different variations of the heat leakage constant. The results are the relationship between several parameters which are expressed in the graph of thermal efficiency vs. compression ratio, graph of efficiency/normal efficiency vs. compression ratio, power output vs. efficiency, and reversibility rate vs. compression ratio. The conclusion is that an increase in heat leakage has an effect on reducing the efficiency and reversibility rate of the engine but does not affect its power output. This work will provide a new chapter for further research related to the use of the boson particle as a working substance in the quantum heat engine, especially the study of the heat leakage effect on engine performance.

Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different loads

The transportation industry is increasingly focused on hydrogen based fuel as a promising alternative due to its potential for reduced emissions and enhanced performance. The purpose of this study is to improve efficiency and reduce emissions on low speed on different loads for heavy duty vehicles. This study can be impactful to train the electronic control unit (ECU) for heavy duty vehicles working on aforementioned conditions. This study investigates the effect of hydrogen ratios (0%–40 %) in HCNG, (0%–15 %) exhaust gas recirculation (EGR) ratios, and spark timing (8°-34o CA bTDC) at low and high loads (15 % & 75 %) under stoichiometric conditions at low speed (700 rpm). Performance, emissions and combustion parameters were thoroughly analysed across these conditions. Brake thermal efficiency increases by 20.7 % & 19.4 % by the addition of (0 %–40 %) hydrogen at low and high load at 14o CA bTDC running on 5 % and 0 % EGR respectively. NOx emissions reduces by 11.9 % & 17.9 % by the addition of (0 %–15 %) EGR and increases by 46.1 % & 46.4 % by increasing the amount of hydrogen in HCNG at low and high load at 14o CA bTDC at 0 % EGR respectively. Coefficient of variation reduces 13.8 % by (0 %–40 %) hydrogen addition at 11 % EGR at 16o CA bTDC. Optimized Gaussian process regression (GPR) and neural network (NN) machine learning techniques were applied to the dataset, and found GPR matern 5/2 is best one. The findings can be utilized in the development of HCNG engine.

Load Behaviour of Steel Beam with Opening using Finite Element Approach

Abstract: This project investigates the structural behaviour of castellated steel beams through a combination of Finite Element Analysis (FEA) and software investigation. Castellated beams, known for their web openings, offer improved structural performance with reduced weight. The study focuses on the deflection patterns of these beams under various loading conditions. ANSYS software was used to model and analyse the beams, both with and without stiffeners around the openings. The results indicate that the incorporation of stiffeners significantly enhances the beams' strength and reduces deflection, optimizing material usage without compromising structural integrity. The strategic placement of stiffeners led to a substantial reduction in steel volume, demonstrating an efficient design approach that balances weight reduction and strength. This work underscores the importance of FEA in optimizing structural designs, providing valuable insights into the behaviour of castellated beams and promoting innovative solutions for industrial applications. The findings contribute to the broader understanding of steel beam behaviour under different loading conditions, highlighting the effectiveness of stiffeners in enhancing performance and efficiency in structural engineering

Optimisation of engine performance and emissions fueled by biodiesel blends

Optimisation of engine performance and emissions fueled by biodiesel blends

Optimization of Injection Pressure and Fuel Temperature in a Diesel Engine Using Biodiesel B40

Biodiesel is an alternative fuel substitute for diesel engines produced from vegetable or animal oil through the transesterification reaction process between fatty acid, methanol, and catalyst. However, in its use in diesel engines, there is a decrease in engine performance. This is partly due to the higher viscosity value compared to diesel. Some ways to improve engine performance using biodiesel include adjusting injection pressure and increasing fuel inlet temperature. This study aimed to determine the effect of adding injection pressure and fuel inlet temperature on the performance of diesel engines using B40, such as power, thermal efficiency, sfc, and AFR. This study used a 1-cylinder diesel engine with constant rotation, using five variations of injection pressure 110-150 bar with a 10 bar interval, and five variations of fuel inlet temperature 30˚C-70˚C with a 10˚C intervals, and five loads from 5,000 kg/m2 to 25,000 kg/m2 with a 5000 kg/m2 interval. Testing and data processing were done using the Taguchi method. The results showed that the best diesel engine performance occurred at an injection pressure of 150 bar and a fuel temperature of 60˚C. The predicted performance value achieved under optimal conditions is a power of 2.9 kW at a load of 25000 kg/m2, thermal efficiency of 69.92% at a load of 25000 kg/m2, sfc of 3 x10-5 kg/kJ at a load of 25000 kg/m2, and AFR of 169.23 at a load of 5000 kg/m2. Temperature significantly affects engine performance power, sfc, thermal efficiency, and AFR compared to injection pressure.

Identifikasi Kenaikan Suhu Minyak Lumas Pada Motor Induk di MV. Manalagi Dasa

Lubrication is one aspect that must be paid attention to, remembering that if there is a delay in lubrication or imperfect lubrication, it will result in damage to the parts that rub together. Low lubricating oil pressure is one of the factors causing imperfect lubrication in the engine. The purpose of lubrication for the performance of the main engine is to reduce friction between other components of the main engine. This research was carried out at MV. Manalagi Dasa, the method used in this research was the Fishbone Diagram. The problem formulation of this research is what causes an increase in the temperature of the lubricating oil on the main motor and what is the impact of increasing the temperature of the lubricating oil on the main motor. Based on research that has been carried out, there are several factors that cause less than optimal lubrication of the oil cooler on the main engine, namely engine factors including damage to gaskets, human factors, namely the lack of understanding of the driver about the lo cooler, operational and maintenance method factors, namely incompatibility in implementing the PMS (Plan Maintenance System) as well as environmental factors, namely dirty sea chest filters. Efforts made include repairing or replacing gaskets on the lo cooler plate, the importance of the machinist's understanding of the lo cooler, implementing PMS (Plan Maintenance System), and cleaning the sea chest filter.

A comparative study of diesel engine fueled by Jatropha and Castor biodiesel: Performance, emissions, and sustainability assessment

The increasing scarcity of fossil fuels and environmental concerns have driven the search for sustainable alternatives, such as biodiesel. This study aims to evaluate the performance, emissions, and sustainability of biodiesel derived from Jatropha and Castor seeds when used in diesel engines. Biodiesel production was achieved through the transesterification process, addressing atomization challenges, and enhancing combustion efficiency. The research compared various blends of biodiesel and conventional diesel fuel (B20, B40, and B60) across different engine loads, from no load to full load. Key findings revealed that biodiesel blends exhibited comparable properties to conventional diesel. Jatropha biodiesel showed superior performance and lower emissions compared to Castor biodiesel. The B20 blend consistently demonstrated the best performance and reduced emissions across all engine loads. Specifically, biodiesel resulted in lower brake thermal efficiency (BTE), carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), and smoke emissions compared to conventional diesel, but higher exhaust gas temperature (EGT), brake-specific fuel consumption (BSFC), and nitrogen oxide (NOx) emissions. The increase in NOx emissions was linked to higher EGT, while other emissions were influenced by the biodiesel’s viscosity and cetane number. The study concluded that the B20 blend of Jatropha and Castor biodiesel offers a viable alternative to conventional diesel, reducing CO2 and CO emissions by 22–10 % and increasing NOx emissions by 8 %. These results suggest significant potential for biodiesel to contribute to sustainable transportation, highlighting the importance of optimizing biodiesel blends for improved engine performance and lower environmental impact. These findings highlight the promising potential of Jatropha and Castor biodiesel as substitutes for diesel fuel, indicating advancements in fuel technology.

Extraction and characterization of Cucumis melon seeds (Muskmelon seed oil) biodiesel and studying its blends impact on performance, combustion, and emission characteristics in an internal combustion engine

This study examines the performance, combustion, and emissions characteristics of a single-cylinder internal combustion diesel engine when fueled with a blend of diesel and biodiesel derived from muskmelon seeds. The kinematic viscosity of the extracted muskmelon seed oil was 6.1 cSt at 40 °C, which is higher than the kinematic viscosity of petroleum diesel of 2.6 cSt. Muskmelon biodiesel was further analyzed using thin-layer chromatography (TLC) and high-voltage separator tests. A comparison of the fuel properties of muskmelon biodiesel with conventional diesel fuel revealed that muskmelon biodiesel could be used alone or in a diesel–biodiesel blend to fuel compression diesel engines. In this study, muskmelon seed biodiesel was blended with diesel fuel at proportions of 10 %, 20 %, and 50 % (BD10, BD20, and BD50, respectively). At a relatively low rotational speed of 1200 rpm, the brake thermal efficiency (BTE) of the engine operated with BD10 and BD20 blends were 36.1 % and 36.0 %, respectively, while the brake-specific fuel consumption (BSFC) of the two blends were 0.260 kg/kWh, and 0.262 kg/kWh, respectively. These values closely resemble those typically observed in diesel fuel engines. Indeed, the average BTE of the BD20 blend was only 3.24 % less than the average BTE of diesel fuel. Diesel fuel generates less NOx and SO2 emissions compared to biodiesel blends: BD100 emitted the most NOx pollution of all fuels tested. In addition, BD10 released significantly more SO2 emissions compared to the other fuels tested. However, the BD20 blend outperformed all other blends in terms of CO, NOx, and SO2 emissions at high engine speeds. The only exception was H2S emissions, which were higher than BD50 and BD100. BD20 also exhibited significantly reduced CO emissions compared to diesel fuel, while BD10 emitted significantly more CO emissions than the other biodiesel blends. Our findings revealed that BD20 exhibited the best engine performance and lower emissions among all fuels tested. In other words, BD20 is the ideal fuel blend for use in diesel engines and does not require any alterations to the engine. Muskmelon waste seeds represent a non-edible waste stream that can be exploited in the production of biodiesel fuel, allowing for the upcycling of a potentially problematic thermochemical conversion feedstock. This potentially valuable use for waste muskmelon seeds in the energy sector could address the wastefulness associated with this particular waste stream.