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首页> 外文期刊>Journal of Chemical Technology & Biotechnology >Application of neural network prediction model to full-scale anaerobic sludge digestion
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Application of neural network prediction model to full-scale anaerobic sludge digestion

机译：神经网络预测模型在厌氧污泥消化中的应用

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BACKGROUND: Process modeling is a useful tool for description and prediction of the performance of anaerobic digestion systems under varying operation conditions. The objective of this study was to implement a model to simulate the dynamic behavior of a large-scale anaerobic sewage sludge digestion system. Artificial neural network (ANN) models using algorithms best suited to environmental problems (the Levenberg–Marquardt algorithm and the ‘gradient descentwith adaptive learning rate’ back propagation algorithms) were used to model the anaerobic sludge digester of the Ankara Central Wastewater Treatment Plant (ACWTP) using dynamic data. RESULTS: Based on the relatively lowmean square error (MSE),mean absolute error (MAE) andmean absolute percentage error (MAPE) and very high r values, ANNmodels predicted effluent volatile solid (VS) concentration and methane yield satisfactorily. Effluent VS and methane yields were predicted by the ANN using only conventional parameters such as pH, temperature, flow rate, volatile fatty acids, alkalinity, dry matter and organic matter. The best back propagation algorithm was the gradient descent with adaptive learning rate algorithm in both models. In the training of the neural network, four-fold cross-validation was used for validation of the model for better reliability. CONCLUSION: The proposed ANN models were shown to be capable of dynamically predicting the VS and CH_4 production rates for real system behavior. Only relatively simple monitoring parameters are needed to build the model for this complex anaerobic digestion process.
机译：背景：过程建模是描述和预测厌氧消化系统在不同运行条件下性能的有用工具。这项研究的目的是建立一个模型来模拟大型厌氧污泥消化系统的动态行为。人工神经网络（ANN）模型使用最适合环境问题的算法（Levenberg-Marquardt算法和“具有自适应学习率的梯度下降”反向传播算法）来对安卡拉中央废水处理厂（ACWTP）的厌氧污泥消化池进行建模）使用动态数据。结果：基于相对较低的均方差（MSE），均值绝对误差（MAE）和均值绝对百分比误差（MAPE）和非常高的r值，ANN模型可以令人满意地预测废水的挥发性固体（VS）浓度和甲烷产率。 ANN仅使用常规参数（例如pH，温度，流速，挥发性脂肪酸，碱度，干物质和有机物）来预测废水的VS和甲烷收率。两种模型中最好的反向传播算法是具有自适应学习率算法的梯度下降。在神经网络的训练中，使用四重交叉验证对模型进行验证以获得更好的可靠性。结论：所提出的人工神经网络模型能够动态预测实际系统行为的VS和CH_4生产率。仅需要相对简单的监控参数即可为这种复杂的厌氧消化过程建立模型。

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