The Research On Adaptive Fuzzy Sliding-mode Control For Municipal Wastewater Nitrogen-removal Process

The municipal wastewater biological nitrogen removal(BNR)process that removes nitrogenous pollutants is a vital part in wastewater treatment.The effective control of this process is crucial to improve the efficiency of the process and to ensure that the effluent quality meets the standard.However,it is challengeable to achieve a stable control of BNR process due to the complex dynamics and the strong disturbances during the process.How to design effective control methods to maintain stable operation of BNR process has been an important research topic in the field of municipal wastewater process control.To ensure a stable operation of municipal wastewater BNR process,this dissertation studies the mechanism knowledge and key features of BNR,and focuses on several major issues such as modelling,optimizing and controlling of the process in time-varying disturbances with uncertainties.First,data-driven and knowledge-driven methods are studied to realize online modeling of the controlled BNR process.Second,a cooperative fuzzy sliding mode control(SMC)method that combine data and knowledge is designed to overcome the contradiction between the controlled variables.Moreover,a multi-objective optimization method is proposed to solve the suitable setpoint for the controlled process.Finally,an adaptive fuzzy SMC system for BNR process is developed and applied in a test environment of municipal wastewater treatment plant.The main research work and innovations of the thesis are as follows:1)Data-driven Multi-unit SMC for Municipal Wastewater BNR ProcessA data-driven fuzzy SMC method is proposed to address the complex biochemical dynamics in controlling multiple-unit dissolved oxygen(DO)concentration for municipal wastewater BNR process.First,a data-driven fuzzy neural network(FNN)is designed to model the unknown multi-unit DO dynamics in BNR.Then,a multivariable SMC is developed to suppress the modelling error and to achieve an accurate DO concentration control.Finally,an adaption law of the control parameters is applied and the stability of the system is verified based on Lyapunov theory.Through benchmark simulation experiments,it is verified that the proposed method can accurately control the multi-unit DO concentration in BNR process.2)Knowledge-driven Multi-unit SMC for Wastewater BNR ProcessA knowledge-driven fuzzy SMC control method is proposed to address the typical disturbance in controlling nitrates concentration for municipal wastewater BNR process.First,a nominal control model that suppresses the typical disturbance is established based on the BNR mechanism knowledge.Second,an adaption law of the control parameters is developed to adapt to the controlled system dynamics and thus improves the control performance.Through benchmark simulation experiments,it is verified that proposed method can successfully suppress the influent disturbance and can achieve a stable control of multi-unit nitrates concentration in BNR process.3)Fuzzy Cooperative SMC for Municipal Wastewater BNR ProcessA data and knowledge multi-info driven fuzzy cooperative SMC method is proposed to address the conflict in controlling the nitrification and denitrification processes in BNR.First,a multi-info fuzzy learning algorithm is designed to fit the correlation relationship of process variables in nitrification and denitrification.Second,a cooperative SMC method is developed to coordinate the control of the controlled variables with a unified control model of the controlled processes.Through benchmark simulation experiments,it is verified that the proposed method can effectively improve the control performance of nitrification and denitrification processes.4)Multi-objective Optimization for Municipal Wastewater BNR ProcessA multi-objective optimal control method is proposed to choose the setpoints for the controlled variables in municipal wastewater BNR process.First,a multi-unit objective model of BNR process is established on process data and knowledge to achieve a dynamic description of the controlled variables.Next,a dynamic multiobjective genetic algorithm is developed to obtain the suitable set-points of the controlled variables.Finally,a cooperative SMC controller is applied to overcome the interference of multiple units and to achieve accurate tracking of the set values.The experimental results show that the proposed method can achieve multi-unit optimization and control of BNR,improves the effluent quality and reduces energy cost.5)Adaptive Fuzzy SMC System for Municipal Wastewater BNR ProcessAn adaptive fuzzy SMC system is developed with the aim of validating the adaptive control method for municipal wastewater BNR process.This system adopts standard application modules in the architecture design to ensure its versatility and compatibility.Its functions include real-time acquisition of operation information,optimization control of controlled variables and real-time evaluation of system performance.The effectiveness of the system is verified by the application in a pilot plant of wastewater BNR treatment in Beijing.The trial operation results show that the system can effectively control the municipal wastewater BNR process and can achieve the expected control objectives.