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

Dey, Suman; Reang, Narath Moni; Das, Pankaj Kumar; Deb, Madhujit

doi:10.1016/j.fuel.2021.120356

Cited by 57 publications

(20 citation statements)

References 64 publications

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“…However, to the best of the authors' knowledge, there has not been any application of this type of methodology comparison in the study of microalgae carbohydrate accumulation. Several studies exhibited higher R 2 values from the ANN models, however, other researchers have reported otherwise 18,36‐40 . In this study, the ANN model had a higher R 2 value (0.99588) than the RSM models (0.9873).…”

Section: Resultscontrasting

confidence: 49%

“…Several studies exhibited higher R 2 values from the ANN models, however, other researchers have reported otherwise. 18,[36][37][38][39][40] In this study, the ANN model had a higher R 2 value (0.99588) than the RSM models (0.9873). However, it is worth noting that while the RSM model has a lower R 2 value between the two investigated models in this study, it still revealed high R 2 , R 2 adj , and R 2 pred values while requiring a lesser number of experimental runs using the CCD.…”

Section: Ann Modeling and Predictionmentioning

confidence: 55%

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Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation

Condor

Luna

Abarca

et al. 2022

Intl J of Energy Research

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Summary Microalgal biofuels have been long considered as potentially clean and sustainable alternatives to conventional fossil fuels. In this study, several parameters, including light intensity, initial nitrogen concentration, and nitrogen starvation duration were investigated for their effects on biomass production and carbohydrate accumulation of Chlorella vulgaris FSP‐E by using two well‐established modeling and optimization methods, namely response surface methodology (RSM) and artificial neural networks (ANN). RSM with central composite design (CCD) revealed that all investigated parameters and their interactions were significant (P < .01) to microalgal carbohydrate accumulation. Both RSM and ANN showed excellent performance in predicting the carbohydrate content of microalgae biomass with a high correlation coefficient (R2) of 0.9873 and 0.9959, respectively. Microalgal biomass with 59.53% carbohydrate content was obtained under optimized conditions. The carbohydrate‐rich biomass was further used as feedstock for bioethanol fermentation, achieving a maximum productivity of 7.44 g L−1 h−1.

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Section: Resultscontrasting

confidence: 49%

Section: Ann Modeling and Predictionmentioning

confidence: 55%

Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation

Condor

Luna

Abarca

et al. 2022

Intl J of Energy Research

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“…Furthermore, greater atomization of the fuel, advanced evaporation, and oxygen content in biodiesel enhanced the CP. 69 , 70 As a result of the various cetane index values, there is a significant difference in ignition delay between other blends and B20 fuels. As a consequence of CuO nanoparticle dissemination in B20, the CP was improved.…”

Section: Resultsmentioning

confidence: 99%

Experimental Investigation on the PCCI Engine Fueled by Algal Biodiesel Blend with CuO Nanocatalyst Additive and Optimization of Fuel Combination for Improved Performance and Reduced Emissions at Various Load Conditions by RSM Technique

et al. 2023

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Fossil fuel depletion and environmental pollution are paramount problems the world faces. Despite several measures, the transportation industry is still battling to manage these issues. A combined approach of fuel modification for low-temperature combustion with combustion enhancers could offer a breakthrough. Due to their properties and chemical structure, biodiesels have piqued the interest of scientists. Studies have asserted that microalgal biodiesel might be a viable alternative. Premixed charge compression ignition (PCCI) is an easily adoptable promising low-temperature combustion strategy in compression ignition engines. The objective of this study is to identify the optimal blend and catalyst measure for improved performance and reduced emissions. Microalgae biodiesel at various proportions (B10, B20, B30, and B40) was amalgamated with CuO nanocatalyst and tested to arrive at the right concoction of biodiesel with nanoparticles in a 5.2 kW CI engine for different load conditions. The PCCI function warrants that about 20% of the fuel supplied is vaporized for premixing. Finally, the interplay factors of the independent variables of the PCCI engine were then explored by response surface methodology (RSM) to determine the optimal level of desired dependent and independent variables. The RSM experiment findings suggest that the best biodiesel and nanoparticle concoctions at 20%, 40%, 60%, and 80% loads were B20CuO76, B20Cu60, B18CuO61, and B18CuO65, respectively. These findings were experimentally validated.

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“…Reang et al [37] found that blending D75B20RW5 with biodiesel shows 8% higher brake thermal efficiency and 3.33% lower BSFC compared to diesel. Whereas Dey et al [38] found that the D85B10E5 was the most optimal mix at 100% engine loading performance. Based on the gray-fuzzy approach with the Taguchi method, the most optimum engine parameter is found in the D85B10E5 mixture at 100% [39].…”

Section: Nomenclaturementioning

confidence: 93%

Engine Performance Using Blended Fuels of Biodiesel and Eco Diesel

Idris¹,

Husin²,

Hermawan³

et al. 2023

Energy Engineering

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Diesel engines is an internal combustion engine with high thermal efficiency, which also uses biodiesel fuel, an environmentally friendly, non-toxic, and low sulfur content. Biodiesel has been around for a long time due to its similar characteristics to diesel fuels which has limited availability. However, several disadvantages are associated with biodiesel, such as poor volatility and high viscosity, which reduces engine performance. Therefore, this study was carried out to improve the diesel engine performance by mixing biodiesel with ecodiesel (ED), an additive produced from natural ingredients that is dissolvable in biodiesel. The biodiesel fuel properties used are density 860 kg/m 3 , dynamic viscosity 4.50E-06 m 2 /s, cetane number 45, and flashpoint 52°C. The results showed that biodiesel-ED mixture could improve engine performance and the optimum performance was at a speed of 3000 rpm on 43.30 (kW), 124.93 (N.m) of the engine torque, and 2.45E -5 (kg/kW.s) of the specific fuel consumption. According to paired sample t-test, the difference in the engine performance is only experienced in the torque, which has a significant increase in the composition of the biodiesel+ED by 0.07 gr mixture.

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

Cited by 57 publications

References 64 publications

Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation

Optimization and modeling of carbohydrate production in microalgae for use as feedstock in bioethanol fermentation

Experimental Investigation on the PCCI Engine Fueled by Algal Biodiesel Blend with CuO Nanocatalyst Additive and Optimization of Fuel Combination for Improved Performance and Reduced Emissions at Various Load Conditions by RSM Technique

Engine Performance Using Blended Fuels of Biodiesel and Eco Diesel

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