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Prediction and optimization of biogas production from POME co-digestion in solar bioreactor using artificial neural network coupled with particle swarm optimization (ANN-PSO)
Biomass Conversion and Biorefinery ( IF 4 ) Pub Date : 2020-10-09 , DOI: 10.1007/s13399-020-01057-6
B. K. Zaied , Mamunur Rashid , Mohd Nasrullah , Bifta Sama Bari , A. W. Zularisam , Lakhveer Singh , Deepak Kumar , Santhana Krishnan

Biogas production from anaerobic co-digestion (ACoD) of palm oil mill effluent (POME) and cattle manure (CM) is getting a lot of attention due to its wide availability and relatively simple energy conversion technology. The ACoD process is extremely complex to model with conventional mathematical modeling methods and requires the use of advanced computational tools due to the mixing of different substrates. Artificial neural network (ANN) is a very recent alternative to modeling tools used to predict complex ACoD problems. To get the best performance from ANN, the parameters of ANN need to be optimized. Here, particle swarm optimization (PSO) algorithms can be a great option. The present study investigates the possibility of using the combined ANN-PSO framework to simulate the process and to predict biogas production from the ACoD of POME and CM. The mixture ratio of POME and CM, oxidation by hydrogen peroxide, and ammonium bicarbonate effects were analyzed separately to increase biogas production using solar-assisted bioreactors. From the experiment, five data volumes of the amounts of POME, CM, hydrogen peroxide, ammonium bicarbonate, and biogas yield were recorded. This dataset has been used to design the proposed model. The results of the proposed ANN-PSO framework with an understanding of mean square error (MSE) and correlation coefficient (R) are 0.0143 and 0.9923, respectively. This result indicates that the proposed method is found to be effective and flexible in predicting biogas production from the ACOD of POME and CM.



中文翻译:

人工神经网络结合粒子群算法(ANN-PSO)对太阳生物反应器中POME共消化产生的沼气进行预测和优化

棕榈油厂废水(POME)和牛粪(CM)的厌氧共消化(ACoD)生产沼气由于其广泛的应用范围和相对简单的能量转换技术而备受关注。ACoD工艺对于使用常规数学建模方法进行建模非常复杂,由于混合了不同的基材,因此需要使用先进的计算工具。人工神经网络(ANN)是用于预测复杂ACoD问题的建模工具的最新替代品。为了从ANN获得最佳性能,需要对ANN的参数进行优化。在这里,粒子群优化(PSO)算法可能是一个不错的选择。本研究调查了使用组合的ANN-PSO框架来模拟过程并预测POME和CM的ACoD产生沼气的可能性。使用太阳能辅助生物反应器分别分析了POME和CM的混合比,过氧化氢的氧化作用和碳酸氢铵的影响,以增加沼气的产生。从实验中,记录了POME,CM,过氧化氢,碳酸氢铵和沼气产量的五个数据量。该数据集已用于设计建议的模型。提出的具有均方误差(MSE)和相关系数(R)的ANN-PSO框架的结果分别为0.0143和0.9923。该结果表明,所提出的方法在根据POME和CM的ACOD预测沼气产量方面是有效且灵活的。使用太阳能辅助生物反应器分别分析了碳酸氢铵和碳酸氢铵的影响,以增加沼气的产生。从实验中,记录了POME,CM,过氧化氢,碳酸氢铵和沼气产量的五个数据量。该数据集已用于设计建议的模型。提出的具有均方误差(MSE)和相关系数(R)的ANN-PSO框架的结果分别为0.0143和0.9923。该结果表明,所提出的方法在根据POME和CM的ACOD预测沼气产量方面是有效且灵活的。使用太阳能辅助生物反应器分别分析了碳酸氢铵和碳酸氢铵的影响,以增加沼气的产生。从实验中,记录了POME,CM,过氧化氢,碳酸氢铵和沼气产量的五个数据量。该数据集已用于设计建议的模型。提出的具有均方误差(MSE)和相关系数(R)的ANN-PSO框架的结果分别为0.0143和0.9923。该结果表明,所提出的方法在根据POME和CM的ACOD预测沼气产量方面是有效且灵活的。提出的具有均方误差(MSE)和相关系数(R)的ANN-PSO框架的结果分别为0.0143和0.9923。该结果表明,所提出的方法在根据POME和CM的ACOD预测沼气产量方面是有效且灵活的。提出的具有均方误差(MSE)和相关系数(R)的ANN-PSO框架的结果分别为0.0143和0.9923。该结果表明,所提出的方法在根据POME和CM的ACOD预测沼气产量方面是有效且灵活的。

更新日期:2020-10-11
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