基于自适应模糊Smith的污水处理系统解耦控制

Decoupling Control Method of Sewage TreatmentSystem Based on Adaptive Fuzzy Smith

污水处理包含化学反应和生物反应等多个过程,整体上跨越了多个学科,在自动化技术的成熟发展下,现阶段污水处理主要依靠自动化处理系统。污水处理系统是一个典型的滞后性和强耦合的复杂系统,受其自身复杂性影响,直接采用常规的控制手段难以达到理想的效果。因此,提出基于自适应模糊Smith研究污水处理系统的解耦控制方法,采用人工神经网络方式,在线学习污水处理系统运行数据,在单元求和的基础上联系相关数据。在自适应模糊Smith原理下建立数据传递函数,解决污水处理系统的滞后性,处理所需要解耦控制的数据对象。根据数据的动态变化情况,平衡数据中污染物质的相关联系,构建污水处理系统的状态保持方程,解耦控制污水处理系统,实现方法设计。实验以某省污水处理厂的处理系统为测试对象,在多个污染区域内分析不同污染物的初始占比,新方法可以在10s内将污染物控制在目标值之内,提高了污水二次利用的效率,具有应用价值。

Sewage treatment includes multiple processes such as chemical reaction and biological reaction,which spans multiple disciplines as a whole.With the mature development of automation technology,sewage treatment at present mainly relies on automatic treatment systems.The sewage treatment system is a typical complex system with hysteresis and strong coupling.Affected by its own complexity,it is difficult to directly adopt conventional control means to achieve ideal results.Based on this,the decoupling control method of sewage treatment system is studied based on adaptive fuzzy Smith.An artificial neural network is adopted to learn the operation data of sewage treatment system online,and related data is connected on the basis of unit summation.In order to solve the lag of the sewage treatment system,a data transfer function is established based on the adaptive fuzzy Smith principle to deal with the data objects that need decoupling control.According to the dynamic changes of the data,the correlation between pollutants in the data is balanced,the state preservation equation of the sewage treatment system is constructed,and the decoupling control of the sewage treatment system is realized in the study.The experiment takes the treatment system of a sewage treatment plant in a province as the test object and analyzes the initial proportion of different pollutants in multiple pollution areas.The new method can control pollutants within 10s with the target value,improve the efficiency of sewage secondary utilization,and has application value.