Blend Percentage Research Articles

Developing Geopolymer Concrete by Using Ferronickel Slag and Ground-Granulated Blast-Furnace Slag

Geopolymer concrete is gaining recognition as an environmentally friendly alternative to traditional cement-based materials, offering potential solutions for reducing the carbon emissions of the construction industry. This study aims to develop GGBFS–FNS geopolymers utilising ferronickel slag (FNS) and ground-granulated blast-furnace slag (GGBFS). Ground FNS (GFNS) is a potential candidate for replacing fly ash in geopolymers. This research aims to develop for the first time a GGBFS–FNS alkali-activated concrete. Numerous trials were conducted including different GGBFS–FNS blend percentages, several chemical admixtures and varying activator concentrations to develop the optimal binder mix composition. The effects of different chemical admixtures on the properties of geopolymer pastes, mortars, and concretes were investigated. The study evaluated setting time, compressive strength, shrinkage, and physical and durability properties. The results indicate that conventional admixtures have limited impact on the setting time, while increasing the water/solid ratio and decreasing the GGBFS content could extend the initial and final setting times. The presence of FNS aggregate could improve the compressive strength of geopolymer mortars. The water absorber admixture was highly effective in reducing shrinkage and increasing chloride diffusion resistance. The geopolymer mix containing 50 wt.% GFNS and 50 wt.% GGBFS with the presence of the water absorber admixture presented high chloride diffusion resistance, non-reactivity to the alkali–silica reaction and high sulphate resistance. Overall, the GGBFS–FNS geopolymers exhibited promising potential for engineering applications as an environmentally friendly material, particularly in aggressive environments.

Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system

A thorough investigation of the thermodynamics and economicperformance of a cogeneration system based on solid oxide fuel cells that provides heat and power to homes has been carried out in this study. Additionally, different percentages of green hydrogen have been blended with natural gas to examine the techno-economic performance of the suggested cogeneration system. The energy and exergy efficiency of the system rises steadily as the hydrogen blending percentage rises from 0% to 20%, then slightly drops at 50% H2 blending, and then rises steadily again until 100% H2 supply. The system's minimal levelised cost of energy was calculated to be 4.64 £/kWh for 100% H2. Artificial Neural Network (ANN) model was also used to further train a sizable quantity of data that was received from the simulation model. Heat, power, and levelised cost of energy estimates using the ANN model were found to be extremely accurate, with coefficients of determination of 0.99918, 0.99999, and 0.99888, respectively.

Effect of bio-mineral oil blend quenchant on the mechanical properties of carburized mild-steel

In this study, the effect of bio-mineral oil blend quenchants on the mechanical properties of carburized mild steel was experimentally studied and reported. The tensile, hardness, impact, and microstructural test specimens were prepared in line with ASTM standards. Prepared specimens were then buried in a 50:50% ratio mixtures of eggshell/date-seed particulates as carburizing medium in a sealed packed cylindrical crucible. The carburization was then carried out in a muffle furnace at 950 oC for 3 h soaking time at 5 °C/min heating rate and thereafter quenched in different percentage blends of bio-mineral oils. Before the mechanical test and microstructural examination, samples were tempered at 200 oC for 1 h.Results from the experimental findings revealed that water and bio-mineral oil blend quenchants significantly influenced the mechanical properties and microstructure of carburized mild steel in varying degrees depending on the quenching media. Specimen quenched in 100% groundnut oil yielded the maximum yield tensile strength (805.43 MPa) and hardness at the surface edge (173.8 HV) equivalent to 106.7 and 87.66 percentage increment however, the best combination of mechanical properties (tensile strength 738.66 MPa, strain 17.12%, hardness 169.5 HV and impact strength 51.1 J) was obtained in the specimen quenched in 60/40% groundnut oil and SAE40 oil blends respectively. The enhancement in the mechanical property was due to the grain refinement in the microstructure of the bio-mineral oils quenched specimen. The 60/40 groundnut/SAE40 oil blend is therefore recommended for metallurgical heat treatment of mild steel for critical industrial applications.

Influence of modified operating parameters and thermal barrier coating on diesel engine performance using punnai oil mixture

The current investigation examined how the combined use of Modified Operating Parameter (MOP) and Thermal Barrier Coating (TBC) enhanced the performance, combustion, and emission characteristics of a diesel engine running on Punnai oil fuel. In this study, it was found that the Punnai oil blended with diesel could effectively increase engine efficiency and decrease emissions under MOP and TBC. Based on the results of the testing, the 60 P (where P denotes the blend percentage of Punnai oil in Diesel) fuel blend proved to be the most efficient fuel blend for the engine in terms of Cylinder Pressure, Heat Release Rate (HRR), and Brake Thermal Efficiency (BTE) in comparison with the 70 P and 80 P fuel blends. Furthermore, the 60 P blend demonstrated lower emissions of Carbon Dioxide (CO2) and Hydrocarbons (HC) than the other blends, although it produced higher levels of Nitrous Oxide (NOx) and Smoke Opacity. The study indicates that utilizing a 60 P Punnai oil fuel could result in improved engine performance and reduced emissions.

Knock-limited combustion of ethanol-, isobutanol-, and 2-methyl-3-buten-2-ol-gasoline blends in a direct-injected spark-ignition engine

Knock-limited combustion of alcohol-gasoline fuel blends was studied in a direct-injection spark-ignition engine. Ethanol, isobutanol (2-methyl-1-propanol), and methylbutenol (2-methyl-3-buten-2-ol) were splash-blended with a blendstock for oxygenate blending (BOB) gasoline. Ethanol was blended in fractions of 10%, 20%, and 30% by volume (E10, E20, and E30). Isobutanol and methylbutenol were blended to match the oxygen weight percentage of the ethanol blends, resulting in blends of 16%, 32%, and 49% (I16, I32, and I49) isobutanol by volume and 18%, 37%, and 56% (M18, M37, and M56) methylbutenol by volume. Neat BOB and gasoline primary reference fuels (PRF) with octane numbers (ON) of 87 (PRF87) and 100 (PRF100) were included for reference. The engine was operated at fixed speed, equivalence ratio, and injection timing while knock-limited spark advance was located across a range of intake pressures. Low-level alcohol blends E10 and M18 had similar knocking behavior and appeared to be slightly more knock-resistant than I16. For mid-level blends, E20 showed further improvement over I32 while M37 showed noticeably better knock resistance, able to match the knock-resistance of PRF100 for loads above 1000 kPa gross indicated mean effective pressure (GIMEP). For high-level blends, the difference between E30 and I49 was similar to their mid-level counterparts. However, M56 showed significant improvement over E30 and I49 throughout the entire range of loads tested. M56 exceeded the knock-resistance of PRF100 for loads above 700 kPa GIMEP. The improved knock-resistance combined with the increased volumetric energy density of methylbutenol resulted in improved fuel consumption and indicated efficiency. The overall results of this study indicate that the order of knock-resistance of these three alcohols, for direct-injection operation with injection during the intake stroke, is isobutanol < ethanol < methylbutenol when compared on an equal oxygen weight basis.

Optimization of concrete mixes using mixture approach for slip-formed concrete pavement incorporating blends of limestone aggregates

Incorporating blends of aggregates from different quarries to construct concrete pavements with acceptable properties has been always a concern in cold regions. In the present research study, a thorough analysis was carried out on two different crushed limestone aggregates (CLAs). A total of 19 concrete mixtures were fabricated with different blend percentages of coarse and fine CLAs (variables) within an appropriate grading range for slip-form concrete (SFC) paving. The relationship between variables and studied performance responses involving strength and durability was correlated using response surface mixture method to establish predictive models. The other components were kept constant to give the similar properties of cement paste and workability of concrete. The performance of aggregates was identified by petrographic and chemical analysis, wet attrition and freeze-thaw tests. Multi-objective optimization with the goals to improve the strength and durability of pavement concrete could obtain optimal blend in terms of maximum possible replacement of durable and non-durable CLAs using reliable predictive models with the desirability of 0.827.

Modelling using fuzzy logic: study of needle-punched filters

The tensile behaviour dependency on the process parameters of blended nonwoven fabric is highly non-linear. This paper explores the potentiality of fuzzy logic inference in such a nonlinear environment as predictive process modelling with minimal experimental data. A model based on fuzzy inference engine has been attempted using recycled polyester fibre blend percentage, needling density and depth of needle penetration as the input variables and tensile strength in both machine direction (MD) and cross direction (CD) as the output variables. Proposed model was built in MAT LAB/Simulink and has been verified by experimental sample data set. Mean relative error percentage and correlation coefficient between the fuzzy model predicted and actual experimental sample values were found to be around ±2.5% and up to 0.97 respectively. These results validate that the model may be applied with high accuracies for the prediction of tensile behaviour of the blended nonwoven fabric in the textile industries.

Comparative Assessment of Ethanol and Methanol–Ethanol Blends with Gasoline in SI Engine for Sustainable Development

Growing environmental concerns over global warming and depleting fossil fuel reserves are compelling researchers to investigate green fuels such as alcoholic fuels that not only show the concrete decrement in emissions but also enhance engine performance. The current study emphasizes the influence of different alcoholic fuel blends in gasoline on engine performance and emissions for an engine speed ranging from 1200 to 4400 rpm. The obtained performance results demonstrate that the brake power and brake thermal efficiency (BTE) increased with an incrementing blend percentage of ethanol and methanol in gasoline (EM). The minimum brake specific fuel consumption (BSFC) was ascertained using pure gasoline followed by E2 and then E5M5. The NOx and CO2 emissions can be described in the decreasing order of E, EM and gasoline due to same trend of exhaust gas temperature (EGT). CO results were in reverse order of CO2. HC emissions were found in the increasing order of E, EM and pure gasoline. E10 performed better among all blends in terms of less exhaust emissions and engine performance. However, EM blended with gasoline significantly reduced NOx. E5M5 produced 1.9% lower NOx emission compared to E10 owing to 1.2% lower EGT. Moreover, greenhouse gases such as CO2, which is mainly responsible for global warming reducing by 1.1% in case E5M5 as compared to E10.

New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study

Over the last few decades, the need to reduce and optimize patient medical radiation exposure has prompted the introduction of novel reconstruction algorithms in computed tomography (CT). Against this backdrop, the present study aimed to assess whether reduced radiation dose CT images reconstructed with the new-generation adaptive statistical iterative reconstruction (ASiR-V) maintain the same image quality as that of routine image reconstruction. In addition, the optimization of image quality parameters for the ASiR-V algorithm (e.g., an optimal combination of blending percentage and noise index (NI)) was investigated. An abdominal reference phantom was imaged using the routine clinical protocol (fixed noise index of 18 and 40% ASiR reconstruction). Reduced radiation dose CT scans were performed with varying NI (22, 24, and 30) and using the ASiR-V reconstruction algorithm. Quantitative and qualitative analyses of image noise, contrast, and resolution were performed against NI and reconstruction blending percentages. Our results confirm the ability of the ASiR-V algorithm to provide images of high diagnostic quality while reducing the patient dose. All the parameters were improved in ASiR-V images as compared to ASiR. Both quantitative and qualitative analyses showed that the best agreement was obtained for the images reconstructed using ASiR-V with NI24 and a high percentage of blending (70–100%). This preliminary study results show that ASiR-V allows for a significant reduction in patient dose (about 40%) while maintaining a good overall image quality when appropriate NI (i.e., 24) is used.

Data analysis of ethanol blended petrol programme of India

The ethanol blended petrol (EBP) programme of 2018 is a policy framework designed to pro- mote the availability of ethanol in the Indian market and to increase its blending percentage in petrol. The paper focuses on the features, current challenges and critically analyzes EBP 2018. It also compares EBP 2018 with the ethanol blending policies of leading nations and suggests remedial measures. EBP 2009 had proposed a 20% blending target for ethanol in petrol by 2017, while EBP 2018 has fixed a 10% ethanol blending target by 2022 and 20% by 2030. The ethanol blending target could not be achieved till now and the current blending rate stood at 2%. Taxes and inter-state controls on the trading and transportation of molasses and non-potable ethanol by various states in India coupled with their set aside allotment continue to derail the implementation of the EBP programme. EBP 2018 proposed differential pricing and incentives for 2G ethanol versus 1G ethanol which is regressive. The paper also analyses whether the ethanol blending and other policy measures had the desired impact on realization of EBP goals and on socio-economic development.

How Do Microalgae Biodiesel Blends Affect the Acoustic and Vibration Characteristics of the Direct Injection Diesel Engine: An Experimental Examination

Abstract The noise and vibration characteristics play a vital role in the effectiveness of engine operations and performance of internal combustion engines. Accumulation of the higher amplitude of both noise and vibration affects the comfort of the engine. So far, most of the research done on the performance, combustion, and emission characteristics only. Less importance is shown in the form engine vibration and sounds created by the engine operation. This paper presents and explores the importance and experimental results of noise and vibration by the compression ignition diesel engine with the fuels of diesel and microalgae biodiesel. The produced microalgae biodiesel blends were SMB10%, SMB20%, and SMB30%. The experimental results were conducted at different engine loads varying across 25%, 50%, 75%, and 100%. The inline, four-cylinder, water-cooled, and naturally aspirated DI diesel engine was used as an experimental setup. From the comparative results between the diesel and microalgae biodiesel, it is found that the use of microalgae blended biodiesel reduced the noise and vibration. The higher the percentage of blends, the greater the reduction in sound and vibration will be. Apart from possessing the performance and emission qualities, the microalgae biodiesel blends proved to be an efficient fuel in reduced vibration and noise qualities too. In three directions, the vibrations were measured as lateral, longitudinal, and vertical vibrations. The vibration in the lateral direction was significantly reduced. Compelling the results, it is understood that the use of the microalgae blends can be sustainable from the perspective of engine wear and tear.

Optimizing engine performance and reducing emissions of greenhouse gases through spirulina microalgae and nano-additive blends

The shift in focus towards biofuels has led to the attention towards fourth-generation fuels, particularly microalgae, due to its high oil productivity and simple cultivation processes. The current study aimed to examine the effects of spirulina microalgae blends in a naturally aspirated diesel engine by testing two blend percentages (15% and 30%) and incorporating Fe2O3 nanoparticles (75 ppm). A series of test conducted in a single-cylinder engine with an optimum compression ratio of 17.5. The fuels tested include 100% diesel (D0), diesel with Fe2O3 nanoparticles (DF), diesel with 15% microalgae blends (B15), diesel with 15% microalgae blends and Fe2O3 nanoparticles (B15F), diesel with 30% microalgae blends (B30), and diesel with 30% microalgae blends and Fe2O3 nanoparticles (B30F). The results showed that the addition of microalgae blends led to a marginal increase in engine performance, while the addition of Fe2O3 nanoparticles led to a significant increase in brake thermal efficiency and decreased fuel consumption. The emissions rate was also lower compared to diesel, but the addition of Fe2O3 nanoparticles increased the oxygen content in the fuel, thereby improving the combustion rates. By ensuring the complete combustion the formation of CO2, HC and smoke intensity was also found to be significantly lower compared to diesel fuel. On the contrary, NOx increased due to the cylinder temperatures. This research highlights the potential of using microalgae as a sustainable source of biofuel, and the positive effects of adding Fe2O3 nanoparticles to enhance the fuel's efficiency.

Performance and emission characteristics of diesel engine fuelled with cashew nut shell oil & optimizing the operating parameters using response surface methodology

Due to the stringent environmental changes and rosein pollution from automobiles and other industrial sources make researchers to find an alternative fuel for diesel substitute based engines. In this paper, direct injection CI engine characteristics is studied using cashew nut shell oil as a biodiesel. The work carried out in two parts, the first part consist of experimental work studies on 4 stroke single cylinder CRDI engine using CNSL as a biodiesel. CNSL Biodiesel is prepared by acid-base trans-esterification technique. The prepared biodiesel is then blended with diesel by the volume basis of [B20D80]. The Diesel engine operated at constant speed of 1500 rpm for the loading condition such as 0%, 25%, 50%, 75%, and 100%. The brake thermal efficiency, brake specific fuel consumption, injection pressure, blend percentage were noted. In terms of Emissions, the CO, CO2, NOX, HC were observed for all loading conditions and compression ratios. The second part of the work is to optimize the operating parameters of the diesel engine using response surface methodology optimizer. For the optimization the input values namely engine load, compression ratio, bending ratios of diesel and biodiesel is varied accordingly to observe the output parameters like BTE, BSFC and for emissions like CO, CO2, NOx, and HC. The optimization is carried out using statistical Minitab software version 18. The predicted and experimental values were compared for the optimal values of BTE, BSFC and emissions respectively.

CO2 REDUCTION IN BRAZILIAN ROAD AND RAIL TRANSPORT

For several years, the use of oil for driving vehicles, generating power and many other activities has caused the world to become highly dependent on it, which is running out. The paper purposes to clarify the Biodiesel stands out among these alternatives as a potential replacement for petroleum diesel, and it is obtained through the transesterification of vegetable oils or animal fat. As a methodology, the study opted for a case study using equations to calculate the final fuel consumption of buses urban transportation in Rio de Janeiro City. As findings, we obtained that to being renewable, biodiesel reduces Brazil's dependence on petroleum and emits less greenhouse gases such as CO2. The paper includes social and pratical implications: based on calculations performed considering petrodiesel consumption in Brazil, this study aims to quantifying CO2 emissions until 2025. The value is in to avoid emissions when methyl biodiesel produced from soybean oil is used as part of the fuel, considering the blend percentages determined by the Brazilian government for the established period.

Moisture management properties and drying behaviour of knitted fabrics for inner layer applications

The present research work is focused on moisture management properties and drying behaviour of various knitted fabrics for inner layer application. Nineteen knitted samples have been prepared by changing the blend percentage of wool/acrylic, number of filaments in polypropylene yarn and structure of knitted fabric. Yarns of different blend ratio of wool/acrylic fibre are used to knit fabrics of different structure. Polypropylene yarns having different number of filaments are also used to knit samples of different structure. These fabrics are tested for moisture management properties and drying behaviour. Wetting time, absorption rate, spreading speed, wetting radius, accumulative one-way transport index (AOWTI), overall moisture management capacity (OMMC), drying time and drying rate of the fabrics have been studied. It is observed that the overall performance of polypropylene knitted fabric and cotton knitted fabric are better as compared to the wool/acrylic having same structural parameters. The OMMC index of polypropylene plain knit, pile knit and cotton knit fabric is found as 0.70, 0.60 and 0.75 respectively. The polypropylene and cotton knitted fabrics shows better results than others in terms of the parameters, like maximum wetting radius, AOWTI, etc. Polypropylene knitted pile structure fabric shows minimum drying time (0.228 h) and zero residual moisture after wash at 90 min under standard conditions. So, it is found that the higher wool content in fabrics gives better moisture management and drying behaviour. Polypropylene fabrics having high number of filaments in the constituent yarn show better moisture management and drying behaviour than the wool/acrylic fabrics. Moisture management and drying behaviour of pile fabrics are better than the plain and rib fabrics.

To Study the Influence of Injection Timing, Injector Opening Pressure and Blend Percentage on Engine Performance and Emissions by the Integration of Taguchi and RSM for an Engine Fuelled with CAOME

Carbon neutral fuels must have a control over global warming. Even through passenger vehicle can be replaced with electrical and hybrid vehicles, but it is extremely difficult to replace goods transport vehicles which uses hydrocarbon fuels. Biofuels are one that can be obtained from various feedstock’s including grains and green matter with high starch and sugar content such as corn, sugar cane and sugar beets. The castor oil methyl esters, which is non-edible in nature fulfils the requirement of fuel for internal combustion engine. The conventional experimental scheme needs more time for optimization and substantial number of experiments need to perform as it is possible to vary a single operating variable at a time and is expensive. Mathematical models of Taguchi method using design of experiments (DOE) give superior results. By using DOE, Taguchi L9 orthogonal array is considered. Analysis of variance (ANOVA), The Regression Equation and signal-to-noise (S/N) ratio are obtained to predict the best parameters and to evaluate the influence of significant conditions on performance, emission and combustion characteristics. The mathematical model obtained by integration of Taguchi method and RSM is successfully validated with accuracy of 95%.

To Study the Influence of Ethanol and EGR on Engine Performance and Emissions by the Integration of Taguchi and RSM for an Engine Fuelled with CAOME.

With the rise in fossil fuel consumption rate, depletion in reserves and stringent pollution norms resulted in search of renewable and carbon neutral fuel. Ethanol is one such fuel that can be obtained from various feedstock’s including grains and green matter with high starch and sugar content such as corn, sugar cane and sugar beets. Even castor oil methyl esters which is non-edible in nature also fulfils the requirement of fuel for internal combustion engine. The traditional experimental scheme requires more time for optimization and extensive experiments need to perform as it is possible to vary only one parameter at a time. This also result in increase in cost and it doesn’t provide interactive effect among the chosen variables. Mathematical models of Taguchi method using design of experiments (DOE) provide good results. By using DOE, Taguchi L27 orthogonal array is considered. Analysis of variance (ANOVA), Regression Equation and signal-to-noise (S/ N) ratio are obtained to predict the optimal parameters and to evaluate the influence of significant conditions on performance, emission and combustion characteristics. It is observed from the experiments that ethanol blend percentage and EGR influences on the output parameters.

Modeling and optimization of performance and emission parameters of a diesel engine: A comparative evaluation between date seed oil biodiesel produced via three different heating systems

In this study, the performance of a diesel engine fueled with date seed oil (DSO) biodiesel, produced under microwave, ultrasonic and conventional heating system, is investigated by experimental and statistical methods. The multi-linear regressions (MLR) were developed using the percentage of biodiesel in fuel and the engine speeds (as independent variables) and the power, torque, and SFC and CO, CO2, HC, O2, and NOx emissions (as dependent variables). Then, the best-fitted regression model was used for analyzing the response surface of the performance parameters, and finding the optimal values of biodiesel blending percentages using genetic algorithm (GA) approaches at different engine speeds. Finally, the engine performance parameters were assessed when the engine is fueled with these optimal blending for three types of biodiesel. The results showed that technologies used in the biodiesel production would have a noticeable effect on the response of engine performance, however, the response surface of exhaust emission follows roughly the same trend with different slope and curvature. Moreover, optimization indicated that the optimal percentage of DSO biodiesel at different speeds was between 11%-%13, %13-%19, and %17-%20 for microwave, ultrasonic, and conventional production method, respectively. Subsequently, when the engine is fueled with the optimal blending of biodiesel at every engine speed, biodiesel produced via an alternative heating system can significantly improve the engine performance and reduce exhaust emissions, especially HC, CO, and NOx by microwave-assisted produced biodiesel.

Corrosion behavior of copper, aluminium, and stainless steel 316L in chicken fat oil based biodiesel-diesel blends

This study investigates the corrosion behavior of automotive materials in bio-based fuels. The Response Surface Methodology is employed to evaluate the corrosion rates of materials such as copper, aluminium, and stainless steel when they are exposed to chicken fat-based biodiesel. Copper, aluminium, and stainless steel showed minimum corrosion rate at a blend percentage of 5.86 % when they were immersed for 920 h and maximum corrosion rate at blend percentage 34.14 % when these were immersed for 920 h. Meanwhile, the maximum corrosion rate was observed at a blend percentage of 34.14 % corresponding to the same immersion period. Optimum values indicated by RSM for copper and aluminium were noted at a blend percentage of 10 % and an immersion period of 720 h. Similarly, for stainless steel 316 l, these were 10.91 % and 754.44 h, respectively. Additionally, trials using the B100 for 920 h were conducted on copper, aluminium, and stainless steel 316 l, and the results showed considerably higher corrosion rates than those previously found. The surface morphology of the materials was investigated by X-ray Diffraction and Scanning Electron Microscopy, and it was revealed that copper was the most corrosive material in chicken fat oil-based biodiesel followed by aluminium and stainless steel 316 l.

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach.

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.