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首页> 外文会议>International conference on artificial neural networks >LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process
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LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process

机译：LSTM和一维卷积神经网络用于厌氧消化过程的预测监控

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Anaerobic digestion is a natural process that transforms organic substrates to methane and other products. Under controlled conditions the process has been widely applied to manage organic wastes. Improvements in process control are expected to lead to improvements in the technical and economic efficiency of the process. This paper presents and compares 3 different neural network model architectures for use as anaerobic digestion process predictive models. The models predict the future biogas production trend from measured physical and chemical parameters. The first model features an LSTM layer, the second model features a 1-D convolutional layer and the third model combines 2 separate inputs and parallel treatment using LSTM and 1-D convolutional layers followed by merging to produce a single prediction. The predictions can be used to adaptively adjust the substrate feeding rate in accordance with the transient state of the digestion process as defined by liquid feeding rate, the organic acid and ammonium ion concentrations and the pH of the digester liquid phase. The training and testing data were obtained during 1 year of continuous operation of a pilot-plant treating restaurant wastes. PLS regression and ICA were used to select the most relevant process parameters from the data. The 1-D Convolutional based model comprising 272 trainable parameters predicted the future biogas flow rate changes with accuracy as high as 89% and an average accuracy of 58% . The work-flow can be applied to optimize the control of the study digester and to control bioreactors in general.
机译：厌氧消化是将有机底物转化为甲烷和其他产物的自然过程。在受控条件下，该工艺已广泛应用于有机废物的管理。预期过程控制的改进将导致过程的技术和经济效率的提高。本文介绍并比较了三种用作厌氧消化过程预测模型的神经网络模型体系结构。这些模型根据测得的物理和化学参数预测未来的沼气生产趋势。第一个模型具有LSTM层，第二个模型具有1D卷积层，第三个模型结合了2个单独的输入，并使用LSTM和1D卷积层进行并行处理，然后合并以生成单个预测。这些预测可用于根据消化过程的过渡状态（由液体进料速度，有机酸和铵离子浓度以及蒸煮器液相的pH值定义）适应性地调整底物的进料速度。培训和测试数据是在处理餐馆垃圾的中试工厂连续运行1年期间获得的。使用PLS回归和ICA从数据中选择最相关的过程参数。基于一维卷积的模型（包含272个可训练参数）预测了未来沼气流量的变化，其准确度高达89％，平均准确度为58％。该工作流程可用于优化对研究消化池的控制并总体上控制生物反应器。

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