Parametric optimization for floating drum anaerobic bio-digester using Response Surface Methodology and Artificial Neural Network

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• 作者：S. Sathish ; ^{sathishamg88@gmail.com} ; S. Vivekanandan
• 关键词：Biogas yield ; Optimization ; Temperature ; Anaerobic digestion ; RSM ; Artificial Neural Network
• 刊名：Alexandria Engineering Journal
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：55
• 期：4
• 页码：3297-3307
• 全文大小：2373 K
• 卷排序：55

文摘

The main purpose of this study to increase the optimal conditions for biogas yield from anaerobic digestion of agricultural waste (Rice Straw) using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In the development of predictive models temperature, pH, substrate concentration and agitation time are conceived as model variables. The experimental results show that the liner model terms of temperature, substrate concentration and pH, agitation time have significance of interactive effects (p < 0.05). The results manifest that the optimum process parameters affected on biogas yield increase from the ANN model when compared to RSM model. The ANN model indicates that it is much more accurate and reckons the values of maximum biogas yield when compared to RSM model.