Back Propagation Neural Network (BPNN) Algorithm Model Application to Prediction and Optimization of Electrochemical Ammonia Removal
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摘要: 电化学方法已被证明是去除氨氮的一种有效方法，降低其电化学过程能耗是关键点。本研究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。通过参数分析及响应面模型对比，所提出的BPNN模型具有更好的决定系数（0.958）。根据水质变化和确定的氨氮去除率目标可以通过BPNN模型获得电化学过程中电流调控策略。该智能控制策略减少了水质波动对氨氮去除的负面影响，并可降低能耗38%。本研究证明了人工智能和反向传播神经网络在电化学去除氨氮过程中的应用潜力，为实现电化学水处理过程自动调控提供可能。Abstract: The electrochemical method has been proved to be an effective method to remove ammonia, but the research on the energy consumption control has been neglected. This research uses artificial intelligence and back propagation neural network to establish the ammonia removal rate prediction model and intelligent control strategy. The model consists of a prediction module and a control module with a back propagation neural network (BPNN) algorithm model. First, 4 hidden layers (per 60 neurons) and a negative feedback adjustment mechanism are used to develop the BPNN algorithm to 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, reducing the negative impact of water quality changes, and can also reduce energy consumption by 38% compared with the original strategy. This work proves the application potential of artificial intelligence and back propagation neural network in the electrochemical of ammonia removal, and provides the possibility to automate the water treatment process.
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Key words:
- back propagation neural network /
- ammonia removal /
- electrochemical /
- intelligent control /
- prediction
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图 1 实验装置及检测仪器流程图与实物图
Figure 1. Flowchart and physical map of the experimental device and testing instruments
图 2 电化学除氨氮过程的反向传播神经网络（BPNN）结构示意图
Figure 2. Schematic diagram of the structure of the back-propagation neural network (BPNN) for the electrochemical ammonia removal processes
图 3 氨氮去除率随时间变化图
Figure 3. Variation of ammonia nitrogen removal rate with time
(a) different constant currents, initial pH=7, initial concentration of ammonia=150 mg/L, stirring rate=0 r/min; (b) different initial pH, constant current=10 mA , initial concentration of ammonia=150 mg/L, stirring rate=0 r/min; (c) different initial concentrations of ammonia, constant current=10 mA, initial pH=7, stirring rate=0 r/min; (d) different stirring rates, constant current=10 mA, initial pH=7, initial concentration of ammonia=150 mg/L. In the above experiments, the electrode area was constant at 4×6 cm2, the electrode spacing was constant at 2 cm, and the chloride ion concentration was constant at 1 g/L (calculated as NaCl)
图 4 (a)训练集和测试集数据的R2变化以及数据集不同数量的隐藏层神经元对应RMSE；(b)训练集和测试集数据的R2变化以及数据集不同数量的隐藏层神经元对应RMSE；(c)训练集和测试集RMSE图
Figure 4. (a) Changes in R2 of training set and test set data and RMSE values corresponding to different numbers of hidden layer neurons in the data set;( b) Changes in R2 of training set and test set data and data sets RMSE values corresponding to different numbers of hidden layer neurons; (c) Training set and test set RMSE.
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
（a) The comparison between the predicted value of voltage and the actual value; （b) The comparison between the predicted value of pH and the actual value; （c) The comparison between the predicted value of ammonia removal rate 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图
Figure 6. Variation of predicted value of 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 a)BPNN模型原策略与智能控制策略氨氮去除率预测值变化趋势与电流变化图；b)原策略与智能控制策略能耗实时分析及氨氮去除率与系统总能耗对比图
Figure 8. (a) BPNN model post strategy and intelligent control strategy(i.e. New Strategy) ammonia removal rate predicted value change trend and current change diagram; (b) Real-time analysis of energy consumption of post strategy and intelligent control strategy and comparison of ammonia removal rate and total system energy consumption
图 9 (a)BPNN模型处理水质变化过程智能控制策略图；(b)智能控制策略电流随时间变化图
Figure 9. (a) The new strategy diagram of the BPNN model processing the water quality change process; (b) The current change diagram of the new strategy over time
表 1 反向传播神经网络（BPNN）模型的参数
Table 1. Parameters for Back Propagation Neural Network (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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