人工神经网络在强化一级处理系统过程模拟与优化中的应用

针对化学强化一级处理系统（CEPT）处理废水时影响因素多，难以进行适当的控制和处理效果的预测等问题，建立起基于BP人工神经网络的CEPT法处理猪场稳定塘废水预测模型，并应用该模型对烧杯试验进行了模拟。结果表明，预测值和实测值吻合较好，模型对COD、总磷、浊度去除率预测的平均相对误差分别为7．5％、4．8％和4．9％。通过对pH值和絮凝剂投药量等可控参数进行优化计算，得到CEPT系统的最佳操作条件和最合理操作条件。该模型的建立为CEPT法处理废水工艺系统实现自动化控制提供了一条简便实用的途径。

Application of artificial neural network in process simulation and optimization of chemically enhanced primary treatment system

Aiming at the issues that there are many factors in treating wastewater with chemically enhanced primary treatment system, and it is difficult in controlling properly and predicting the effect of treatment, the article sets up a forecasting model of treating swine lagoon wastewater with chemically enhanced primary treatment system that based on BP neural network model and simulates beaker experiments. The results indicate that the data of forecast are coincident with the experimental data well. The average relative errors of removal rates of COD, total phosphorus and turbidity between forecast value and actual value are 7.5% , 4.8% and 4.9% respectively. Combining with the optimization algorithm of pH value and the amount of flocculant used the model can find the best and the most proper operation conditions of chemically enhanced primary treatment system. The establishment of this model offers a convenient and valuable method for the realization of automatic control with chemically enhanced primary treatment system in treating wastewater.