Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil

 Process Safety and Environmental Protection

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Những tác giả chính: Ishola, Niyi B., Adeyemi, Omowumi O., Adesina, Ayo Joshua, Odude, Victoria.O
Định dạng: Tạp chí
Ngôn ngữ:Tiếng Anh
Được phát hành:  Process Safety and Environmental Protection 2020
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Truy cập trực tuyến:https://ir.oauife.edu.ng/handle/123456789/5093
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author Ishola, Niyi B.
Adeyemi, Omowumi O.
Adesina, Ayo Joshua
Odude, Victoria.O
author_facet Ishola, Niyi B.
Adeyemi, Omowumi O.
Adesina, Ayo Joshua
Odude, Victoria.O
author_sort Ishola, Niyi B.
collection DSpace
description  Process Safety and Environmental Protection
format Journal
id oai:ir.oauife.edu.ng:123456789-5093
institution My University
language English
publishDate 2020
publisher  Process Safety and Environmental Protection
record_format dspace
spelling oai:ir.oauife.edu.ng:123456789-50932020-01-20T13:14:13Z Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil Ishola, Niyi B. Adeyemi, Omowumi O. Adesina, Ayo Joshua Odude, Victoria.O palm kernel inference genetic sulfate catalyzed adaptive neuro fuzzy inference system  Process Safety and Environmental Protection Ferric sulfate-catalyzed esterification process for palm kernel oil (PKO), which had an initial acid value (AV) of 22 ± 0.1 mg KOH/g oil, was modeled using response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS). The process parameters investigated in the AV reduction of the oil were methanol-to-oil ratio (2:1–3:1), catalyst loading (6–10 w/v) and reaction time (15–25 min) using Box Behnken design of RSM. The developed ANFIS and RSM models were both subjected to various statistical evaluation and they both showed high degree of accuracy based on the high values of coefficient of determination (R²) of 0.9662 and 0.9039 for ANFIS and RSM, respectively and low values of mean absolute error of prediction (MAE) 0.0506 and 0.1506, and average absolute deviation (AAD) of 2.3665 and 7.1179 for ANFIS and RSM, respectively. To minimize the AV for the PKO, the process parameters investigated were optimized using RSM and ANFIS coupled with genetic algorithm (GA). Optimum values of methanol-to-oil ratio of 2.96:1, catalyst amount of 6 w/v and reaction time of 15 min with a corresponding AV of 1.05 mg KOH/g oil (95.2% AV reduction) were established using ANFIS-GA, while the values obtained using RSM were methanol-to-oil ratio of 2:1, catalyst amount of 6 w/v and reaction time of 25 min with a corresponding AV of 1.54 mg KOH/g oil (93.0% AV reduction). Based on the statistical indicators employed for this work, ANFIS was a better prediction tool than RSM while GA outperformed RSM in the optimization of the esterification process. Ferric sulfate proved to be a good catalyst for PKO esterification 2020-01-20T13:14:10Z 2020-01-20T13:14:10Z 2015-07 Journal DOI: 10.1016/j.psep.2017.07.004 https://ir.oauife.edu.ng/handle/123456789/5093 en text/plain  Process Safety and Environmental Protection
spellingShingle palm kernel
inference
genetic
sulfate catalyzed
adaptive neuro fuzzy inference system
Ishola, Niyi B.
Adeyemi, Omowumi O.
Adesina, Ayo Joshua
Odude, Victoria.O
Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil
title Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil
title_full Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil
title_fullStr Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil
title_full_unstemmed Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil
title_short Adaptive neuro-fuzzy inference system-genetic algorithm vs . response surface methodology: a case of ferric sulfate-catalyzed esterification of palm kernel oil
title_sort adaptive neuro fuzzy inference system genetic algorithm vs response surface methodology a case of ferric sulfate catalyzed esterification of palm kernel oil
topic palm kernel
inference
genetic
sulfate catalyzed
adaptive neuro fuzzy inference system
url https://ir.oauife.edu.ng/handle/123456789/5093
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