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Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology

Food waste with high carbohydrate content is considered as a suitable substrate for fermentation of methane gas. In this study, co-digestion of poultry manure (PM) and food waste (FW) was used. Response surface methodology (RSM) and artificial neural network (ANN) were applied to optimize parameters...

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Main Authors: Tuan Yusof, Tengku Roslina, Che Man, Hasfalina, Abdul Rahman, Nor'aini, Hafid, Halimatun Saadiah
Format: Article
Language:English
Published: Canadian Center of Science and Education 2014
Online Access:http://psasir.upm.edu.my/id/eprint/34774/1/Optimization%20of%20Methane%20Gas%20Production%20From%20Co-Digestion%20of%20Food.pdf
http://psasir.upm.edu.my/id/eprint/34774/
http://www.ccsenet.org/journal/index.php/jas/article/view/37785
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spelling my.upm.eprints.347742016-09-02T09:08:37Z http://psasir.upm.edu.my/id/eprint/34774/ Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology Tuan Yusof, Tengku Roslina Che Man, Hasfalina Abdul Rahman, Nor'aini Hafid, Halimatun Saadiah Food waste with high carbohydrate content is considered as a suitable substrate for fermentation of methane gas. In this study, co-digestion of poultry manure (PM) and food waste (FW) was used. Response surface methodology (RSM) and artificial neural network (ANN) were applied to optimize parameters of co-digestion of PM and FW at different ratios, initial pH values and temperatures. A comparative analysis was done using RSM and ANN in a predictive model of the experimental data obtained in accordance with the central composite design. The combined effects of the independent variables (ratio, pH and temperature) as the most significant parameters of methane fermentation of PM and FW were investigated. Optimization using RSM and ANN showed a good fit between the experimental and the predicted data as elucidated by the coefficient of determination with R2 values of 0.991 and 0.998, respectively. Quadratic RSM predicted the maximum methane yield to be 537 mL CH4/g VS at the optimal conditions; ratio 80:20 (PM : FW); temperature 35 °C; and initial pH 7.11. The maximum predicted methane yield by the ANN model was 535.82 mL CH4/g VS at the following conditions; ratio of poultry manure to food waste 80:20; temperature 35 °C; and pH 7.00. The verification experiments successfully produced 538 mL CH4/g VS within 14 days of incubation. These experiments indicated that the developed model was successfully used to predict the fermentable methane production. Canadian Center of Science and Education 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34774/1/Optimization%20of%20Methane%20Gas%20Production%20From%20Co-Digestion%20of%20Food.pdf Tuan Yusof, Tengku Roslina and Che Man, Hasfalina and Abdul Rahman, Nor'aini and Hafid, Halimatun Saadiah (2014) Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology. Journal of Agricultural Science, 6 (7). pp. 27-37. ISSN 1916-9752; ESSN: 1916-9760 http://www.ccsenet.org/journal/index.php/jas/article/view/37785 10.5539/jas.v6n7p27
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Food waste with high carbohydrate content is considered as a suitable substrate for fermentation of methane gas. In this study, co-digestion of poultry manure (PM) and food waste (FW) was used. Response surface methodology (RSM) and artificial neural network (ANN) were applied to optimize parameters of co-digestion of PM and FW at different ratios, initial pH values and temperatures. A comparative analysis was done using RSM and ANN in a predictive model of the experimental data obtained in accordance with the central composite design. The combined effects of the independent variables (ratio, pH and temperature) as the most significant parameters of methane fermentation of PM and FW were investigated. Optimization using RSM and ANN showed a good fit between the experimental and the predicted data as elucidated by the coefficient of determination with R2 values of 0.991 and 0.998, respectively. Quadratic RSM predicted the maximum methane yield to be 537 mL CH4/g VS at the optimal conditions; ratio 80:20 (PM : FW); temperature 35 °C; and initial pH 7.11. The maximum predicted methane yield by the ANN model was 535.82 mL CH4/g VS at the following conditions; ratio of poultry manure to food waste 80:20; temperature 35 °C; and pH 7.00. The verification experiments successfully produced 538 mL CH4/g VS within 14 days of incubation. These experiments indicated that the developed model was successfully used to predict the fermentable methane production.
format Article
author Tuan Yusof, Tengku Roslina
Che Man, Hasfalina
Abdul Rahman, Nor'aini
Hafid, Halimatun Saadiah
spellingShingle Tuan Yusof, Tengku Roslina
Che Man, Hasfalina
Abdul Rahman, Nor'aini
Hafid, Halimatun Saadiah
Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
author_facet Tuan Yusof, Tengku Roslina
Che Man, Hasfalina
Abdul Rahman, Nor'aini
Hafid, Halimatun Saadiah
author_sort Tuan Yusof, Tengku Roslina
title Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
title_short Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
title_full Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
title_fullStr Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
title_full_unstemmed Optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
title_sort optimization of methane gas production from co-digestion of food waste and poultry manure using artificial neural network and response surface methodology
publisher Canadian Center of Science and Education
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/34774/1/Optimization%20of%20Methane%20Gas%20Production%20From%20Co-Digestion%20of%20Food.pdf
http://psasir.upm.edu.my/id/eprint/34774/
http://www.ccsenet.org/journal/index.php/jas/article/view/37785
_version_ 1643831255086661632
score 13.211869

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