BPNN Algorithm Model Application to Prediction and Optimization of Electrochemical Ammonia Removal
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摘要: 利用反向传播神经网络（Back Propagation Neural Network，BPNN）建立氨氮去除效果预测模型和智能控制策略。模型由具有BPNN模型的预测模块和控制模块组成。首先，采用4层隐藏层（每层60个神经元）和负反馈调节机制开发BPNN算法，优化模型并预测氨氮去除率。参数分析及响应面模型对比结果表明所提出的BPNN模型具有更好的决定系数（0.9580）。根据水质变化和确定的氨氮去除率目标，通过BPNN模型获得电化学过程中电流智能调控策略，该智能控制策略减少了水质波动对氨氮去除的负面影响，并使能耗降低38%。Abstract: The electrochemical method is proved to be an effective method to remove ammonia. However, the research on the energy consumption control is neglected. This paper uses artificial intelligence and back propagation neural network (BPNN) to establish the ammonia removal rate prediction model and intelligent control strategy. The model consists of a prediction module and a control module with BPNN algorithm model. Firstly, four hidden layers (per 60 neurons) and a negative feedback adjustment mechanism are used to develop the BPNN algorithm, optimize the model and predict the ammonia removal rate. Through parameter analysis and comparison of response surface models, the BPNN model proposed in this paper has better coefficient of determination and lower mean square error. According to the water quality changes and the determined target of ammonia removal rate, the current control strategy in the electrochemical can be obtained through the BPNN model. Finally, the proposed intelligent control strategy is applied to the electrochemical system for ammonia removal, which can reduce the negative impact of water quality changes and reduce energy consumption by 38% compared with the original strategy.
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Key words:
- neural network /
- ammonia removal /
- electrochemical /
- intelligent control /
- prediction
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图 1 实验装置及检测仪器的流程图（a）与实物图（b）
Figure 1. Flowchart (a) and pictures (b) of the experimental device and testing instruments
图 2 电化学除氨氮过程的BPNN结构示意图
Figure 2. Schematic diagram of the structure of BPNN for the electrochemical ammonia removal process
图 3 氨氮去除率随时间的变化
Figure 3. Variation of ammonia removal rate with time
Reaction conditions: (a) Initial pH is 7, initial mass concentration of ammonia is 150 mg/L, stirring rate is 0; (b) Constant current is 10 mA , initial mass concentration of ammonia is 150 mg/L, stirring rate is 0; (c) Constant current is 10 mA, initial pH is 7, stirring rate is 0; (d) Constant current is 10 mA, initial pH is 7, initial mass concentration of ammonia is 150 mg/L
图 4 不同隐藏层数（a）和单层隐藏层不同神经元数（b）的BPNN模型对应的R2及RMSE；训练集和测试集的RMSE随训练次数的变化（c）
Figure 4. R2 and RMSE corresponding to BPNN model with different numbers of hidden layers (a),different numbers of neurons in single hidden layer (b), and RMSE changes in training set and test set with training time (c)
图 5 BPNN模型预测值与实际值相关度对比图
Figure 5. Comparison of the correlation between the predicted value of the BPNN model and the actual value
图 6 BPNN模型(a, c)与响应面模型(b)对氨氮去除率预测值随时间变化比较图
Figure 6. Comparison of the variation of predicted ammonia removal rate with time by BPNN model (a, c) and response surface model (b)
图 7 不同电流达到目标氨氮去除率所需时间分布图
Figure 7. Time distribution diagram of different currents to reach the target ammonia nitrogen removal rate
图 8 原策略与智能控制策略氨氮去除率预测值变化趋势(a1)与电流变化图(a2)；原策略与智能控制策略能耗实时分析(b1)及氨氮去除率与系统总能耗对比图(b2)
Figure 8. Comparison of predicted ammonia removal rate (a1) and current change trend (a2) of post strategy with intelligent control strategy; Real-time analysis of energy consumption (b1) and comparison of ammonia removal rate and total system energy consumption (b2) of post strategy with intelligent control strategy
图 9 BPNN模型处理水质变化过程智能控制策略图(a)；智能控制策略电流随时间变化图(b)
Figure 9. Intelligent control strategy diagram of BPNN model processing the water quality change process (a); Current change diagram of the intelligent control strategy over time (b)
表 1 BPNN模型的参数
Table 1. Parameters for BPNN models
BPNN parameter Number/Type Input layer neurons 6 Output layer neurons 1 Hidden layers 4 Hidden layer neurons 60 Activation function for hidden layer relu Activation function for output layer tanh 
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