Engine Performance Parameters Research Articles

Performance and ecological parameters of a diesel engine fueled with diesel and plastic pyrolyzed oil (PPO) at variable working parameters

Plastics are one of the major pollutants to the environment. An investigation is carried out in converting low density polyethylene (LDPE) to oil by pyrolysis method with the aid of catalyst for the use in internal combustion engines. Experiments were conducted at different speeds (1200, 1500 and 1800 rpm) and variable loads (low, medium and full) with constant compression ratio engine to evaluate the performance and ecological parameters. Plastic pyrolyzed oil (PPO) is blended with diesel fuel in 20:80 volume proportion to get D80PO20 blended mixture which is employed in engine in order to assess its characteristics. Encouraging outcomes have been obtained as cylinder pressure in combustion is of same order as that of diesel fueled engine. The results showed that increasing engine speeds resulted in higher cylinder pressure and brake thermal efficiency. Also, high nitrogen oxide (NOX) and low brake specific energy consumption (BSFC) has been identified. Lower smoke (BSN) and NOX emissions are noticed from engine tail pipe with D80PO20 blended mixture.

Investigating the Effects of Pentanol and Biodiesel Blends on the Performance and Emission Characteristic of Compression Ignition Engine

Emissions from use of fossil fuels have consistently posed significant threat to the environment and wellbeing of man. This has prompted several studies aimed at finding solution to the emissions and their effects. The aim of this research is to investigate the effect of adding pentanol and biodiesel from Moringa Oleifera seed to pure diesel content of 70% by volume. pentanol and biodiesel make up the remaining 30 percent by volume and were also varied, with pentanol percentage being increased from 2 percent to 8 percent so as to carefully monitored the effects of adding pentanol while the Moringa oleifera biodiesel made up the balance for the 30%. These blends were labelled according to the percentage composition of biodiesel and pentanol (B28P2, B26P4, B24P6, B24P6, and B22P8). The physicochemical properties of all blends such as viscosity, density, pour point, acid value and iodine value determined. From the result of the engine performance and emission tests, B24P6 blend has been noted to have the best engine performance parameters, haven shown the best engine brake power of 19.787 kW, and with the highest engine brake thermal efficiency of 19.78%.While in term of engine emission, B22P8 blend, have the lowest Nitrogen oxide (NOx) emission, with a value of 82.4994 ppm which is about 3% lower than that of pure diesel, while B26P4 have the lowest carbon monoxide (CO) emission among all the samples tested with value of 6.1556 ppm which is about 58% lower than that of Petro diesel. However, blends appeared to have good brake mean effective pressure except for B22P8 which have the lowest BMEP, that’s about 2.8% lower than that of Petro diesels. Conclusively, the addition of Moringa oleifera biodiesel with pentanol improved the diesel quality, yielding good improvement in engine performance and emission.
 Keywords: Diesel, biodiesel, pentanol, biofuel blends, Moringa oleifera seed, engine emissions

The effects of nanoadditives on the performance and emission characteristics of spark-ignition gasoline engines: A critical review with a focus on health impacts

Gasoline is currently the main fuel of choice in many cities worldwide, and hence, the consequent exposure to its emissions, i.e., CO, NOX, unreacted hydrocarbons, particulate matters, lead, sulfur dioxide, and ozone, is inevitable. Among the various solutions put forth to mitigate the toxic gasoline-related air pollution, while improving fuel performance at the same time, is the application of nanoparticles. Considering the above, the present paper aims to review and critically discuss the improvements made in engine performance and exhaust emission parameters by adding nanosized fuel additives in gasoline. Overall, the addition of fuel nanoadditives enhances the thermo-physical properties of the fuel and improves combustion characteristics. Moreover, the inclusion of the fuel nanoadditives generally results in early combustion and shortened ignition delay. In more detail, the optimum quantity of fuel nanoadditives is associated with improvements in ignition characteristics and reductions in exhaust emissions. The present work also scrutinizes the adverse health effects of various gasoline-related emissions. Finally, possible mechanisms underlying the improvements in gasoline properties and its combustion in the presence of nanoadditives are also reviewed and discussed.

Modelling and evaluation of combustion emission characteristics of COME biodiesel using RSM and ANN—a lead for pollution reduction

Nowadays, the emissions from the diesel engines are focused lot to minimise the environmental pollutions in accordance with the emission standards. In this regard, biodiesels are found to be efficient for the diesel engines due to their higher energy contents and low exhaust emissions. Use of biofuel in association with diesel will be an efficient way for the cost-effective performance of the diesel engines with reduced pollutions. The COME is an efficient combustible oil, in which the diesel is blended at different proportions to identify their suitability in the diesel engines. In this regard, the properties of the COME-Diesel blends are determined and analysed for their influence on the combustion characteristics. To understand the performance and emission characteristics of blends, experiments are carried out on the variable compression ratio (VCR) engine keeping the blend, compression ratio, load, and speed as variables. The response surface methodology (RSM) used as a tool for designing and conducting the experiments according to the predetermined variables. The experimental sets generated are performed to determine the NO and HC emissions (response functions). The adequacy of the models is verified through ANOVA and through the p and F tests. The experimental design matrix is also used in training the artificial neural network (ANN) to develop the empirical models. The models from RSM and ANN are experimented and the results obtained from both the models are compared for their accuracy levels. Once the hypothesis is developed for the biodiesel and engine setup, the emission models will be used for the optimising the engine operating parameters and blends to minimise the pollutions from engine for wide operating conditions.

Investigation of engine characteristics of a variable compression ratio diesel engine using biodiesel and alcohol blends

Increasing environmental pollution and reducing fossil fuels are major concerns of today’s age. To address both the above said issues, the present research focuses on fossil fuel compensation with Biofuels additives. The objective is to analyze combined effect of biodiesel, alcohols and other additives on the diesel engine performance and emission characteristics. The prime target of present research is to analyze the emission characteristics of the diesel engine fueled with Biodiesel (20%)-diesel, Biodiesel (20%)-ethanol (10%)-diesel and Biodiesel (20%)-butanol (10%)-diesel blends. For this purpose, various loads and compression ratios have been applied on each of the blends, to get the optimum result for better performance and reduction in emissions. Engine tests were conducted at variable loads, starting from no load (0 Kg) to full load (15 Kg) at rpm of 1500 on a single cylinder diesel engine. Two compression ratios CR16 and CR18 were used for the research. Engine performance parameters namely BSFC, BTE and engine emissions namely NOX, CO, HC and CO2 were measured. Performance and emission variations of the modified blends were compared with the reference fuel (pure diesel) for the quantitative assessment of the improvements.

Quantitative investigation on the impact of injection timing on soot formation in a GDI engine with a customized sectional method

Soot engine-out emissions are no longer a prerogative of Diesel engines. Emission regulations related to Gasoline units aim to curb the soot emissions along with other pollutants. In this scenario, Computational Fluid Dynamics (CFD) is a very promising research and development tool to explore the influence of engine design and operational parameters, as well as of the fuel chemical nature, on the particulate matter formation. Among the soot models, the Sectional Method is an advanced resource to provide information on Particle Number, Particulate Mass and Particle Size Distribution. In this study, the Sectional Method is applied in conjunction with a customized soot library, where the source terms governing the soot sections transport equations are stored. The library is computed via chemical kinetics simulation of a 0D constant pressure reactor, which provides fuel-related coefficients for each individual source term over the entire range of conditions experienced by the 3D-CFD model. 3D-CFD simulations are then carried out for three different injection timings without case-by-case tuning. Numerical results are then compared to the experimental dataset by using a consistent methodology. A satisfactory agreement between 3D-CFD results and experimental measurements is reached for soot mass and particle numbers, while the particle size distribution function is only partially reproduced. Soot-related quantities are thoroughly analyzed for each of the examined injection strategies to understand the mechanisms leading to soot formation and emissions.

Optimization of diesel engine performance and emission parameters of Karanja biodiesel-ethanol-diesel blends at optimized operating conditions

This study reviews Karanja biodiesel, ethanol and diesel blending in context of Indian subcontinent and also focuses on its effects on engine’s performance, emission and combustion responses. Experimentations carried out on four stroke single cylinder diesel engine at constant speed 1500 rpm and injection pressure 210 bar. The four input parameters as Injection angle, Compression ratio, Blend % and loads varied to obtain engine responses as Brake thermal efficiency, Brake specific fuel consumption, Exhaust gas temperature, Carbon dioxide, Carbon monoxide, Nitrogen Oxide and hydrocarbon. Taguchi method was applied to optimize the trials using L9 orthogonal array and to get main effects S/N ratio curves, for optimal combination of input parameters to response. The ANNOVA was used to establish the contribution of input parameters to responses. Further single combination of input parameters for all seven responses as Injection Angle 19°CA bTDC, Compression ratio 18, Krishna 30 (fuel blend) and 50% load was obtained by Gray Relational Analysis. Experimentation revealed marginal reduction of brake thermal efficiency about 2%, Brake specific fuel consumption higher by 3%, Exhaust gas temperature increases by 3%, Carbon dioxide increases by 0.86%, hydrocarbon decreases by 12 PPM, carbon monoxide and Nitrogen oxide reduced by 0.029% and 8% respectively when compared with diesel at full load. The combustion parameters as Cylinder pressure, Ignition delay, Combustion duration, Start of combustion, End of combustion, Rate of pressure rise, Mean effective pressure and Net heat release rate were observed at full load, also support optimal combination of input parameters.

Comparative study using RSM and ANN modelling for performance-emission prediction of CI engine fuelled with bio-diesohol blends: A fuzzy optimization approach

This present investigation focuses on prediction of engine responses of a single cylinder CI engine powered by bio-diesohol (diesel-palm biodiesel-ethanol) blends using RSM (response surface methodology) and ANN (artificial neural network) model. RSM combined with multi-level general full factorial design (FFD) is used for the prediction of brake thermal efficiency (BTE), brake specific fuel consumption (BSEC), and nitrogen oxides (NOx). The engine experimental data is trained in ANN model using Levenberg-Marquardt back propagation training algorithm with logistic-sigmoid activation function. Different statistical measures are calculated to quantify the errors and correlations of the predicted models. Comparatively lower prediction error and higher correlation have been observed from the ANN model compared to RSM. The range of overall mean square error (MSE) and correlation coefficient are found (0.0003–0.00059) & (0.99403–0.998) and (0.00019–0.00035) & (0.99943–0.99971) from RSM and ANN model respectively. The range of overall mean absolute percentage error (MAPE) from ANN model (3.13–4.55%) is found lower compared to RSM model (3.97–6.6%). Thereafter, RSM and ANN predicted responses are introduced in fuzzy logic system for the optimization of engine operating parameters. At 100% load, the D75B20E5 (75% diesel + 20% palm biodiesel + 5% ethanol) blend has been found best for the optimization of BTE, BSEC and NOx emission. Finally, after the confirmation test, it has been revealed that the performance of D75B20E5 blend is as comparable to diesel.

Exploring the efficiency and pollutant emission of a dual fuel CI engine using biodiesel and producer gas: An optimization approach using response surface methodology

The present study focuses on optimizing the engine operating parameters of a dual-fuel (DF) engine. Producer gas (PG) and Honge oil methyl ester (HOME) are used as primary fuel and pilot fuel respectively for the operation. An experimental design matrix of 20 different combinations was considered using Design of Experiments (DoE), based on the central composite design (CCD) of response surface methodology (RSM). The effects of these combinations were experimentally investigated to calculate the performance and emission characteristics of the engine. The objective of the work is to maximize the Brake thermal efficiency (BTE) and minimize the exhaust gas temperature (EGT), nitrogen oxide (NOx), hydrocarbon (HC), and carbon monoxide (CO) emissions. The RSM model is developed using the experimental data and further, the operating parameters were optimized using the desirability approach. The optimized combination of operating parameters was obtained at 61.10% engine load, compression ratio (CR) of 18, and injection timing (IT) of 23.30° before top dead center (BTDC). The optimum responses corresponding to these operating conditions were found as 14.23%, 354.29 °C, 52.18 ppm, 39.53 ppm, and 0.51% for BTE, EGT, NOx, HC, and CO respectively with an overall desirability of 0.962. The optimized responses were validated experimentally at optimum input conditions and found to be within acceptable error levels. Further, an economic analysis of the optimized DF system is also carried out.

An Experimental Investigation on the Effect of Ferrous Ferric Oxide Nano-Additive and Chicken Fat Methyl Ester on Performance and Emission Characteristics of Compression Ignition Engine

In recent years, industries have been investing to develop a potential alternative fuel to substitute the depleting fossil fuels which emit noxious emissions. Present work investigated the effect of ferrous ferric oxide nano-additive on performance and emission parameters of compression ignition engine fuelled with chicken fat methyl ester blends. The nano-additive was included with various methyl ester blends at different ppm of 50, 100, and 150 through the ultrasonication process. Probe sonicator was utilized for nano-fuel preparation to inhibit the formation of agglomeration of nanoparticles in base fuel. Experimental results revealed that the addition of 100 ppm dosage of ferrous ferric oxide nanoparticles in blends significantly improves the combustion performance and substantially decrease the pernicious emissions of the engine. It is also found from an experimental results analysis that brake thermal efficiency (BTE) improved by 4.84%, a reduction in brake specific fuel consumption (BSFC) by 10.44%, brake specific energy consumption (BSEC) by 9.44%, exhaust gas temperature (EGT) by 19.47%, carbon monoxides (CO) by 53.22%, unburned hydrocarbon (UHC) by 21.73%, nitrogen oxides (NOx) by 15.39%, and smoke by 14.73% for the nano-fuel B20FFO100 blend. By seeing of analysis, it is concluded that the doping of ferrous ferric oxide nano-additive in chicken fat methyl ester blends shows an overall development in engine characteristics.

Effect of n-Butanol Blends on Engine Performance and Exhaust Emission of Compression Ignition Engine Fuelled with Diesel-Palm Oil Methyl Ester (B20)

The study aims to evaluate the performance and emission characteristic in a compression ignition engine of conventional diesel fuel-biodiesel blends with different percentage of fuel additive n-butanol. The experiment was operated at a constant engine load (50% throttle condition) with different engine speed (2700, 3100, 3500 rpm). A blend of biodiesel and diesel fuel known as B20 (20% palm oil methyl ester and 80% diesel in volume) was prepared, and then n-butanol was added to B20 at a volume of 5ml, 10ml and 15ml (denote as B20+Bu5, B20+Bu10 and B20+Bu15, respectively) and the tested fuel samples were compared with diesel fuel and diesel-biodiesel (B20). The experimental results show that when the proportion of n-butanol was increased in B20 blends, kinematic viscosity was larger while calorific value was smaller than those of the neat diesel. Although n-butanol have some negative impacts on engine performance parameters, its generally positively affect exhaust emission parameters compared to diesel fuel. According to engine performance and exhaust emission test result of n-butanol fuel blends with B20 blends, average values of brake thermal efficiency (10.19%, 7.58% and 4.29%), carbon monoxide (21.75%, 17.06% and 11.28%), hydrocarbon (18.51%, 15.68% and 12.13%) are lower, while brake specific fuel consumption (BSFC) (27.48%, 45.37% and 59.20%) are higher and carbon dioxide (CO2) and oxides of nitrogen (NOx) are comparable than those of diesel fuel.

Ammonia Emissions in SI Engines Fueled with LPG

Ammonia is a toxic exhaust component emitted from internal combustion engines. Both pure ammonia and the products of its reaction with nitrogen and sulfur compounds, being the source of particulate matter (PM) emissions, are dangerous for human health and life. The aim of the article was to demonstrate that NH3 can be produced in exhaust gas after-treatment systems of spark-ignition (SI) engines used in light-duty vehicles. In some cases, NH3 occurs in high enough concentrations that can be harmful and dangerous. It would be reasonable to collect research data regarding this problem and consider the advisability of limiting these pollutant emissions in future regulations. The article presents the results of the spark-ignition engine testing on an engine test bench and discusses the impact of the air–fuel ratio regulation and some engine operating parameters on the concentration of NH3. It has been proven that in certain engine operating conditions and a combination of circumstances like the three-way catalytic reactor (TWC) temperature and periodic enrichment of the air–fuel mixture may lead to excessive NH3 emissions resulting from the NO conversion in the catalytic reactor. This is a clear disadvantage due to the lack of limitation of these pollutant emissions by the relevant type-approval regulations. This article should be a contribution to discussion among emissions researchers whether future emission regulations (e.g., Euro 7 or Euro VII) should include a provision to reduce NH3 emissions from all vehicles.

Performance and Emissions of a Microturbine and Turbofan Powered by Alternative Fuels

Alternative fuels containing biocomponents produced in various technologies are introduced in aviation to reduce its carbon footprint but there is little data describing their impact on the performance and emissions of engines. The purpose of the work is to compare the performance and gas emissions produced from two different jet engines—the GTM-140 microturbine and the full-size DGEN380 turbofan, powered by blends of Jet A-1 and one of two biocomponents: (1) Alcohol-to-Jet (ATJ) and (2) Hydroprocessed Esters and Fatty Acids (HEFA) produced from used cooking oil (UCO) in various concentrations. The acquired data will be used to develop an engine emissivity model to predict gas emissions. Blends of the mineral fuel with synthetic components were prepared in various concentrations, and their physicochemical parameters were examined in the laboratory. Measurements of emissions from both engines were carried out in selected operating points using the Semtech DS gaseous analyzer and the EEPS spectrometer. The impact of tested blends on engine operating parameters is limited, and their use does not carry the risk of a significant decrease in aircraft performance or increase in fuel consumption. Increasing the content of biocomponents causes a noticeable rise in the emission of CO and slight increase for some other gasses (HC and NOx), which should not, however, worsen the working conditions of the ground personnel. This implies that there are no contraindications against using tested blends for fuelling gas-turbine engines.

Optimal Control of a Spark Ignition Engine Including Cold Start Operations for Consumption/Emissions Compromises

This study presents a semi-empirical modeling approach based on an extensive parametric study using a spark-ignition port-injection engine. The experimental results are used to derive engine-out emission models for each regulated pollutant (CO, HC, NOx) as a function of engine operating parameters. Such parameters include engine speed, intake manifold pressure, equivalence ratio, and spark advance. The proposed models provide accurate predictions over a large range of engine operating conditions. The adequate accuracy and low computational burden of the models are promising in the context of optimal control theory. Dynamic programming is applied in order to find the best operating parameters that define trade-off between fuel consumption and emissions over driving cycles.

Multi-objective optimization of the engine performance and emissions for a hydrogen/gasoline dual-fuel engine equipped with the port water injection system

Hydrogen is one of the most promising options being considered as the fuel of future. However, injection of hydrogen into modern gasoline fueled engines can cause some issues such as power loss. This study, therefore, aims to address this challenge in a simulated hydrogen/gasoline dual-fueled engine by developing a novel and innovative approach without possible side effects such as NOx increment. To achieve this goal, the impacts of water injection and the start of the combustion (SOC) modification in a gasoline/hydrogen duel fueled engine have been rigorously investigated. In current methodology, an engine is simulated using AVL BOOST software and the model is validated against the experimental data. The Latin Hypercube design of experiments method was employed to determine the design points in 3-dimensional space. Due to the existing trade-off between NOx and BMEP, multi-objective optimization using genetic algorithm (GA) was implemented to determine the optimum values of water injection and SOC in various hydrogen energy shares and the effects of optimum design parameters on the main engine performance and emission parameters were investigated. The results showed that the proposed solution could recover the brake mean effective pressure (BMEP) and in some hydrogen energy shares even increase it above the level of single fueled gasoline engine with the added benefit of there being no increase in NOx compared to the original level. Furthermore, other emissions and engine performance parameters are improved including the engine equivalent Brake specific fuel consumption (BSFC) which was shown to increased up to 4.61%.

Modelling the performance and emissions of ethanol–gasoline blend on a gasoline engine using ANFIS

ABSTRACT This research investigates the importance of the adaptive neuro-fuzzy inference method (ANFIS) to model the gasoline engine's performance and exhaust emissions using various fuels. To collect data for the creation of the planned ANFIS model, an experiment was performed on a SI engine using gasoline-ethanol blends at varying engine loads. The ANFIS model was designed to provide an association between all parameters using specific gasoline-ethanol blends and different engine loads. Then, using experimental data, engine performance parameters, and emissions were forecasted by the ANFIS model. The model findings were then contrasted with experimental values to determine the accuracy of the ANFIS predictions. The maximum correlation coefficient (R) of 0.9900–0.9999 and 95.3594% accuracy for both performance and exhaust emissions values were generated by the ANFIS model. The Mean relative errors (MRE) ranged from 0.055 to 8.396% whereas the root mean square errors (RMSE) values were small. The findings confirmed that the ANFIS is competent enough to forecast the performance and emissions of gasoline engines very efficiently.

Effect of aspect ratio on the performance characteristics of free piston linear generator engine fueled by hydrogen

Free-piston linear generator (FPLG) engines currently gained great attention due to their capability to operate with variable fuel and compression ratio. This paper presents an experimental study on the effect of aspect ratio on the performance characteristics of the FPLG engine fueled by hydrogen. Three aspect ratios (i.e. 1.0, 1.5, and 2.0) are used to identify the engine combustion and performance parameters. The injection position is fixed in the middle of the stroke, while the equivalence ratio is kept at 1.0. The results indicate that the aspect ratio 2.0 produces the highest pressure, heat release, and shortest combustion duration. Whereas the aspect ratio 1.0 produces higher combustion efficiency and operating frequency. The piston speed decreases with the decrease in aspect ratio, which gives a negative effect on the indicated mean effective pressure and power output of the PFLG. Overall, the aspect ratio has a significant influence on engine performance characteristics.

Testing and evaluation of cold-start emissions from a gasoline engine in RDE test at two different ambient temperatures

Abstract In order to better reflect the actual ecological performance of vehicles in traffic conditions, both the emission standards and the applied emission tests are being developed, for example by considering exhaust emissions for a cold engine start. This article presents the research results on the impact of ambient temperature during the cold start of a gasoline engine in road emission tests. The Real Driving Emissions (RDE) tests apply to passenger cars that meet the Euro 6 emissions norm and they are complementary to their type approval tests. A portable emissions measurement system was used to record the engine and vehicle operating parameters, as well as to measure the exhaust emissions during tests. This allowed for parameters such as engine load, engine speed and vehicle speed to be monitored. The cold start conditions for two different temperatures (8°C and 25°C) were compared in detail. Moreover, the engine operating parameters, exhaust concentration values and road emissions for the 300 s time interval, were compared. The summary of the article presents the share of a passenger car’s cold start phase for each exhaust compound in the urban part of the test and in the entire Real Driving Emissions test depending on the ambient temperature.

Experimental Analysis between Performance Parameters an Internal Combustion and Engine Block Vibration with Gasoline and Ethanol Fuel

Ethanol and gasoline are widely used with fuels in Otto cycle engines. These fuels have different heating power and octane number and the engine behaves differently depending on the type of fuel used. The objective of this study is to measure, compare and investigate the factors that affect the block vibration of an internal combustion engine with fuel used ethanol or gasoline, as function shaft torque and shaft power engine, main effective pressure, advanced ignition, pressure combustion engine and other parameters. The experiment consisted of instrumenting the side of the engine block with a tri axil accelerometer to measure the level of vibration intensity of the engine running on a bench dynamometer varying engine speed at full load conditions. The results showed that the engine vibration level was influenced by the engine speed, load, type of fuel and performance parameters of engine. The highest level of vibration was achieved in the region of maximum torque and maximum pressure combustion. The combustion process is mainly responsible for the highest level of vibration achieved with ethanol. In all operating conditions the vibration level of the engine block was higher as ethanol. On average, the longitudinal, vertical and transverse engine vibration was 3%, 31% and 56% higher in the engine running on ethanol compared to the engine using gasoline. This research is important because it correlates the longitudinal, vertical and transverse vibration level of the block of an internal combustion engine as an engine combustion process fueled with ethanol and gasoline.

Optimization of Operating Parameters of Diesel Engine Powered with Jatropha Oil Diesel Blend by Employing Response Surface Methodology

Biodiesel is promoted as an appropriate alternative fuel for use in compression ignition (CI) engines as it is non-toxic, biodegradable, and sulphur-free and does not require any change in current engines design. The key objective of this experimental study is to evaluate the best operational parameters of engine referring to performance and emissions of Jatropha biodiesel powered CI engine by employing response surface methodology (RSM). In order to achieve maximum brake power (BP), brake thermal efficiency (BTE) and to reduce nitrogen oxide (NOx) and unburnt hydrocarbon (HC) emissions the optimization model is used. Effects of different factors such as fuel injection pressure (FIP), engine compression ratio (CR), and load on thermal performance have been studied in a single cylinder diesel engine. Experiments design was based on L20 orthogonal array Central composite design (CCD) method. RSM was employed to test the suitability of biodiesel in diesel engines and models were developed by using experimental results. Based on the optimization, the optimum engine parameters found were 18 CR, 180 bar FIP and 8.11 kg engine load. Under these settings, the optimum responses were found as 2.21 kW, 28.24%, 25.3 ppm and 174.6 ppm for BP, BTE, HC, and NOx, respectively. Meanwhile, R 2 (coefficient of determination) values were found as 99.96%, 99.93%, 98.5%, 99.14%, and 99.78%, for BP, BTE, net heat release rate (NHRR), HC, and NOx, respectively.