摘要
针对城市生活污水A^2O工艺处理过程中出水水质的软测量建模,本文提出一种机理模型与补偿模型相结合的软测量预测模型。首先根据污水处理厂基本参数建立ASM2机理模型,并采用PSO算法调节模型动力学参数;然后在此基础上,利用SVM建立出水水质软测量补偿模型。最后,将该软测量模型应用与某一实际污水处理厂进行模拟运行,并对预测结果进行误差分析。仿真结果表明,该模型可获得较准确的预测结果,较好地反映污水处理的实际运行状况,为模拟污水处理、预测出水水质提供决策支持。
In order to modeling the soft sensor model of effluent quality for the A2O municipal sewage treatment process, a soft sensor predictive model combining a mechanism model with a compensation model is proposed in this paper. Firstly, according to the basic parameters of sewage treatment plant to build the ASM2 mechanism model, as well as PSO algorithm is used to adjust the kinetic parameters of the ASM2 model. Then, on this basis, SVM regression is used to compensate the prediction error of mechanism model. Finally, the sol~ sensor model is applied with an actual sewage treatment plant to imitation operation, and analysis the error of prediction result. The simulation results show that the model can obtain accuracy prediction results and reflect the operation of sewage treatment efficiently, so that providing decision support for simulation and effluent quality prediction of sewage treatment.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2013年第10期1143-1147,共5页
Computers and Applied Chemistry
基金
国家自然科学基金资助项目(11101012)
关键词
A^2O污水处理工艺
ASM2模型
软测量
粒子群算法
支持向量机
A2O sewage treatment process
activated sludge model No.2
soft sensing
particle swarm optimization
support vector machine
