Multi-input multi-output (MIMO) ANN and Nelder-Mead’s simplex based modeling of engine performance and combustion emission characteristics of biodiesel-diesel blend in CI diesel engine

Esonye, Chizoo; Onukwuli, Okechukwu Dominic; Ofoefule, A. U.; Ogah, Ekechi Ogah

doi:10.1016/j.applthermaleng.2019.01.101

Cited by 65 publications

(35 citation statements)

References 34 publications

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“…DESO would possess high melting pointv due its high saturated fatty acid content and this would make it to be useful for many industrial applications such as surfactants and detergents. Also the amount of fatty acid present in DESO is found to compare well with others in oleic family ( Esonye et al., 2019b ). It will therefore found useful applications in nutrition and as non-drying oil upon slight modifications ( Uzoh and Onukwuli, 2016 ; Obiegu-Nwosu et al., 2020 ).…”

Section: Resultssupporting

confidence: 54%

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Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents

Esonye¹,

Onukwuli

Anadebe³

et al. 2021

Heliyon

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This research presents optimal factor evaluation for maximum Dyacrodes edulis seed oil (DESO) extraction by applying central composite design (CCD) based on Box-Behnken (BB) experimental design of response surface methodology (RSM) and Artificial neural network (ANN) on feed forward-back propagation (FFBP) of Levenberg Marquardt (LM) training algorithm. Polar solvents (ethanol and combination of methanol and chloroform (M/C)) and non-polar solvents (n-hexane) were used for the extraction. The RSM optimal predicted oil yields were 45.21%, 38.61% and 30.87% while experimental values were 46.01%, 40.71% and 32.45% for n-hexane, ethanol and M/C respectively. The RSM optimum conditions were particle size of 450.67, 451.19 and 450.22μm, extraction time of 55.57, 55.16 and 56.11min and solute/solvent ratio of 0.19, 0.16 and 0.18 g/ml for n-hexane, ethanol and M/C respectively. The ANN-GA optimized conditions showed 5.14, 5.81 and 2.12 % higher DESO yields at 1.10, 0.26 and 0.65% smaller particle sizes, 5.47, 0.30 and 0.62 % faster extraction rate, and 24, 11.11 and 10% more solute requirement, for n-hexane, ethanol and M/C solvents respectively. The particle size was found to be the most significant factor. ANN and RSM established good correlations with the experimental data but ANN showed higher predictive supremacy than RSM based on its higher values of R 2 and lower error indices. Also, ANN-GA provided more economical optimal DESO extraction route. The physico-chemical characteristics, functional groups and fatty acid compositions of the seed oil compared with literature values and suggest high commercial values for DESO. Therefore, the obtained results present a viable method to harness the useful and highly potential seed oil from dyacrodes edulis for many industrial applications.

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Section: Resultssupporting

confidence: 54%

“…Interestingly, the seed oil from dyacrodes edulis seed (DES) has been established as sustainable material for renewable energy with high commercial value and industrial potential ( Esonye et al, 2019b ). Unfortunately, its extraction process has not been optimized.…”

Section: Introductionmentioning

confidence: 99%

Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents

Esonye¹,

Onukwuli

Anadebe³

et al. 2021

Heliyon

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“…Also, the higher the amount of unsaturated fatty acid in a biodiesel sample, the better the cloud point but lower the oxidation stability which implies that the higher composition of unsaturated fatty acids in the methyl ester (52.42%) would therefore enhance its cold flow properties [27]. It is reported that the high viscous nature of waste frying oils is because of their high saturated and less unsaturated fatty acids and this could cause micro-crystal formations that are dangerous to engine fuel injection units [28,29]. Therefore, the application of biodiesel derived from the kernel seed of sea almond would possess no inherent viscosity problem.…”

Section: Gc-ms Characterization Resultsmentioning

confidence: 99%

Sea Almond as a Promising Feedstock for Green Diesel: Statistical Optimization and Power Rate Law Based Chemical Kinetics of Its Consecutive Irreversible Methanolysis

Esonye¹,

Onukwuli²,

Ofoefule³

et al. 2020

Prunus

Self Cite

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For successful industrial scale-up and effective cost analysis of transesterification process, presentation of complimentary research data from process optimization using statistical design techniques, chemical kinetics and thermodynamics are essential. Full factorial central composite design (FFCCD) was applied for the statistical optimization of base methanolysis of sea almond (Terminalia catappa) seed oil using response surface methodology (RSM) coupled with desirability function analysis on quadratic model. Reaction time had the most significant impact on the biodiesel yield. Optimum conditions for biodiesel yield of 93.09 wt% validated at 92.58 wt% were 50.03°C, 2.04 wt% catalyst concentration, 58.5 min and 4.66 methanol/oil molar ratio with overall desirability of 1.00. Ascertained fuel properties of the FAME were in compliance with international limits. GC–MS, FTIR and NMR characterizations confirmed unsaturation and good cold-flow qualities of the biodiesel. Based on power rate law, second-order kinetic model out-performed first-order kinetic model. Rate constants of the triglyceride (TG), diglycerides (DG) and monoglycerides (MG) hydrolysis were in the range of 0.00838–0.0409 wt%/min while activation energies were 12.76, 15.83 and 22.43 kcal/mol respectively. TG hydrolysis to DG was the rate determining step. The optimal conditions have minimal error and would serve as a springboard for industrial scale-up of biodiesel production from T. catappa seed oil.

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“…Esonye et al [ 113 ] evaluated the emissions and performance of the Perkins diesel engine with different blends of African pear seed biodiesel. Multi-input and multi output-ANN model designed to forecast the BSFC, BTE, HC, CO, and NOx emissions using the fuel blend, speed, and load as the input parameters.…”

Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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Multi-input multi-output (MIMO) ANN and Nelder-Mead’s simplex based modeling of engine performance and combustion emission characteristics of biodiesel-diesel blend in CI diesel engine

Cited by 65 publications

References 34 publications

Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents

Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents

Sea Almond as a Promising Feedstock for Green Diesel: Statistical Optimization and Power Rate Law Based Chemical Kinetics of Its Consecutive Irreversible Methanolysis

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

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