Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology, neural network and neuro-fuzzy system

Ishola, Niyi B.; Okeleye, Adebisi A.; Osunleke, Ajiboye S.; Betiku, Eriola

doi:10.1007/s00521-018-03989-7

Cited by 46 publications

(21 citation statements)

References 44 publications

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“…Also, the high values confirmed good generalization and predictive capability of the ANN model since it was able to predict the outputs of validation and testing data that were not part of the training set used to develop the model (Sarve et al, 2015;Ishola et al, 2019). The developed ANN model was further evaluated using R 2 .…”

Section: Artificial Neural Network Model Descriptionmentioning

confidence: 80%

“…The predicted values from the ANN model are very close to the experimental data better than the values predicted by the RSM model. Predictions by ANN models were superior to that of RSM models in the esterification of palm kernel oil (Betiku et al, 2016), transesterification of sorrel oil (Ishola et al, 2019), and transesterification of sesame oil (Sarve et al, 2015). Figure 10 shows the results of the sensitivity analysis for the models.…”

Section: Comparison Of Performance Of the Developed Modelsmentioning

confidence: 99%

“…For the RSM investigation, Eq. 4 was used to determine the influence of each input variable on the model based on the sum of squares (Ishola et al, 2019;Betiku and Ishola, 2020). While for ANN, the contribution level of each input variable was determined by Garson (1991) method as described by Eq.…”

Section: Sensitivity Analysis Of Process Input Variablesmentioning

confidence: 99%

“…Statistics such as the correlation coefficient (R), coefficient of determination (R 2 ), Adjusted R 2 , MSE, root MSE (RMSE), mean average error, standard error of prediction, mean relative percent deviation (MRPD). The details of the equations for determining the parameters have been described in our previous works (Betiku et al, 2016;Ishola et al, 2019).…”

Section: Performance Assessment Of Modelsmentioning

confidence: 99%

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Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

et al. 2020

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High free fatty acids (FFA) content in oils poses challenges such as soap formation and difficulty in the separation of by-products in direct transesterification of oil to biodiesel, which is of environmental concern and also increases the cost of production. Thus, in this study, the ferric sulfate-catalyzed esterification of neem seed oil (NSO) with an FFA of 5.84% was investigated to reduce it to the recommended level of ≤1%. The esterification process for the NSO was modeled using response surface methodology (RSM) and artificial neural network (ANN). The effect of the pertinent process input variables viz. methanol/NSO molar ratio (10:1–30:1), ferric sulfate dosage (2–6 wt%), and reaction time (30–90 min) and their interactions on the reduction of the FFA of the NSO, were examined using Box Behnken design. The optimal condition for the process for reducing the FFA content of the oil was established using RSM and ANN-genetic algorithm (ANN-GA). The results showed that the models developed described the process accurately with the coefficient of determination (R2) of 0.9656 and 0.9908 and the mean relative percent deviation (MRPD) of 6.5 and 2.9% for RSM and ANN, respectively. The ANN-GA established the optimum reduction of FFA of 0.58% with methanol/NSO molar ratio of 18.51, ferric sulfate dosage of 6 wt%, and reaction time of 62.8 min as against the corresponding values of 0.62% FFA, 23.5, 5.03, and 75 min established by the RSM. Based on the statistics considered in the study, ANN and GA outperformed RSM in modeling and optimization of the NSO esterification process.

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Section: Artificial Neural Network Model Descriptionmentioning

confidence: 80%

Section: Comparison Of Performance Of the Developed Modelsmentioning

confidence: 99%

Section: Sensitivity Analysis Of Process Input Variablesmentioning

confidence: 99%

Section: Performance Assessment Of Modelsmentioning

confidence: 99%

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Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

et al. 2020

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“…This owes to its ease of use, and more accurate prediction of complex and non-linear systems with large inputs [15,18]. The predictive ability of both RSM and ANNs have been evaluated [14,[19][20][21][22][23], with the later giving better predictions. GA is an optimization solver, based on natural selection and biological evolution mechanisms such as mutation, selection, inheritance and crossover [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization studies of microalgae dewatering by utilizing bio-based alkali: a case study of response surface methodology (RSM) and genetic algorithm (GA)

Ajala

Alexander

2020

SN Appl. Sci.

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In the present study, the flocculating viability of bio-based alkali derived from cocoa pod husk ash was investigated by using a D-optimal design of the response surface methodology (RSM). The interactive effects of biomass concentration and flocculant dose on flocculation efficiency and concentration factors were examined. The generalization ability of modeling and predictive tools such as RSM and artificial neural network (ANN) was also examined. Moreso, the flocculation process was optimized using RSM and response surface methodology coupled with genetic algorithm (RSM-GA). The flocculation process results showed that the bio-based alkali effectively flocculated microalgae with more than 90% flocculation efficiency at the optimum condition predicted by RSM and RSM-GA. Both RSM and ANN have described the flocculation process with high accuracy, based on the values of statistical indices evaluated but ANN has demonstrated a higher generalization potential as compared to RSM. The results of elemental analysis of the bio-based alkali shown that the concentration of K + (51,489 ppm) was highest, followed by Ca 2+ (1450 ppm) and Na + (210 ppm). This undoubtedly showed the alkaline nature of the bio-based alkali obtained from cocoa pod husk ash that was employed as the flocculant for harvesting microalgae. This study confirms that ash derived alkali can be used to effectively and efficiently harvest microalgae.

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Machine learning techniques for the prediction of polymerization kinetics and polymer properties

Ishola,

McKenna

2023

Can J Chem Eng

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In the current study, the ability of two data‐driven machine learning tools, the extreme learning machine (ELM) and the adaptive neuro‐fuzzy inference system (ANFIS), to predict the polymerization rate and melt flow index of linear low‐density polyethylene produced in a gas phase process was investigated. The level of interaction between the input variables (ethylene, 1‐butene, isopentane pressures, and reaction temperature) on the outputs (melt flow index and activity) was also examined. It was found that both outputs are impacted by the presence of isopentane as an induced condensing agent. Various statistical indicators, including the coefficient of correlation (R2) and root mean square error (RMSE), were used to quantitatively evaluate both developed models. The ANFIS model outperformed the developed ELM model in terms of predicting the MFI and the catalyst activity. A sensitivity analysis of the ANFIS and ELM models showed that all the input variables under investigation had a sizable impact on the responses and none of them could have been discarded. The present study showed that machine learning tools could be employed to adequately develop empirical models to predict polymerization kinetics as well as the final polymer properties.

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Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology, neural network and neuro-fuzzy system

Cited by 46 publications

References 44 publications

Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

Multi-objective optimization studies of microalgae dewatering by utilizing bio-based alkali: a case study of response surface methodology (RSM) and genetic algorithm (GA)

Machine learning techniques for the prediction of polymerization kinetics and polymer properties

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