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The development and evaluation of a portable polyethylene biogas reactor

机译：便携式聚乙烯沼气反应器的开发与评估

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Several factors can influence the process of biogas production. The type of reactor is one of the key factors that influence biogas production. Therefore, the aim of this study was to construct a portable horizontal polyethylene-based biogas reactor. In addition, the performance of the developed biogas reactor was tested through digestion of cow manure. The experiments were carried out in Mashhad, Iran, during June–July 2016. Biogas production was studied over a span of 58 days’ hydraulic retention time. Artificial neural network (ANN) models were used to predict the production of biogas based on temperature and pH. The Levenberg–Marquardt learning algorithm was employed to develop the best model. The obtained biogas productivity was 0.27 m3 kgVS-1, indicating that the developed biogas reactor was optimum to convert the substrate into biogas. The ANN results highlighted that the best developed model consisted of an input layer with two input variables, one hidden layer with 15 neurons, and one output layer with the correlation coefficient of 0.90. Overall, it was concluded that the ANN models can be employed to prognosticate biogas production using a portable polyethylene biogas reactor.
机译：有几个因素可以影响沼气生产过程。反应器的类型是影响沼气生产的关键因素之一。因此，本研究的目的是构建便携式卧式聚乙烯基沼气反应器。另外，通过消化牛粪便测试了开发的沼气反应器的性能。实验于2016年6月至7月在伊朗马什哈德进行。对沼气的生产进行了58天的水力停留时间研究。人工神经网络（ANN）模型用于根据温度和pH值预测沼气的产生。 Levenberg-Marquardt学习算法用于开发最佳模型。获得的沼气生产率为0.27 m3 kgVS-1，表明开发的沼气反应器最适合将基质转化为沼气。人工神经网络的结果强调，最完善的模型包括一个具有两个输入变量的输入层，一个具有15个神经元的隐藏层和一个具有0.90的相关系数的输出层。总体而言，得出的结论是，可以使用ANN模型通过便携式聚乙烯沼气反应器来预测沼气生产。

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