Research On Intelligent Prediction And Robust Optimal Control For Wastewater Treatment System

Biological wastewater treatment is a very complex process which is random,time-variant and coupling.The conventional control methods are difficult to obtain satisfactory performance of biological wastewater treatment.The introduction of advanced intelligent control,robust control and optimal theory are strong impetus for development on wastewater treatment research.The least squares support vector machine,the small gain theorem,Kharitonov theorem,loop shaping,V-gap,?H,genetic algorithm and particle swarm optimization algorithm have been studied in other fields,but their researches and applications in wastewater treatment system are far from well-enough.Based on previous work,we further study and synthesize these algorithms.The synthesized algorithms in the thesis are applied in biological wastewater treatment system and many research results have been obtained.The main contents of the thesis are outlined as follows.1.Biochemical oxygen demand(BOD)is one of the key parameter which is widely applied to evaluate the biological and chemical conditions of water quality.Moreover,BOD can be hardly used as feedback data for building control models control of wastewater treatment or as the online monitoring data of effluent water quality index.This paper proposes a novel method that can predict online BOD by synthesizing an online version of Gustafson-Kessel(GK)algorithm and least squares support vector machines(LSSVM).The cluster merging of GK and the sparse online LSSVM with time window are proposed to improve the efficiency in speed and less memory.An Fast Leave-one-out Cross-Validation(FLOO-CV)prediction error-based threshold without any manual work for updating the model is proposed to delete redundant samples that put minimal influence on the global model.The GK-LSSVM is applied to predict BOD values in organic matter in effluents from wastewater treatment plants by the soft-sensor method.The results indicate that the proposed method can improve prediction accuracy,reduce training time and possesse better performance.2.Using the biomass concentration in the recycle stream as control variable,wastewater treatment with the uncertainty of parameters is simplified by fractional linear transformation.The small-gain approach or Kharitonov theorem result to solve a convex optimal problem subjected to several inequality constraints.The genetic algorithm(GA)is used to search the robust PID controller parameters.The novel robust PID is applicated for simplified Activated Sludge Model to keep the concentration of the biomass proportional to the influent flow rate with model uncertainties without solving the steady-state Riccati equations.The proposed controller has lower order and can be easily realized.Simulation shows that the algorithm is simple and effective for uncertain wastewater treatment.3.The dissolved oxygen(DO)concentration has been an important process parameter in biological wastewater treatment process(WWTP),which is a typical uncertain and time-varying system.The complex system has widespread changes in the parameters,outside interference and so on and perfect result can’t be achieved by using traditional control strategy.A novel tuning approach for robust PID controller based on ?H loopshaping synthesis in combination with gap metric and the Particle Swarm Optimization(PSO)algorithm is proposed for uncertain control problem.Different from the traditional research,the controller is designed through the search region constrained by ?H loopshaping synthesis and gap metric theorem.The algorithms of GA and PSO are analysed by using test benchmark functions.The PSO algorithm is used for tuning the robust PID controller parameters based on the underlying constrained optimization problems without resolving complex mathematical calculations.The control technique is applied for the robust controller design so as to get a low order structured controller.The simulation shows that the proposed method can achieve robust performance and the ability of restraining disturbance.4.The biological nitrification and denitrification are able to give an effective solution to removal of organic nitrogen from wastewater.After detailed analyzing the stoichiometric relation and corresponding kinetic equation of each sub-processes,relevant nitrification and denitrification models are constructed.Based on the one basin system,the nitrate nitrogen can be removed by changing the concentration of organic carbon etc.The robustness against structure and parameter uncertainties and unknown disturbances is considered.A novel robust PID controller is proposed by using ?H control theory and hybrid GA and PSO algorithm.Firstly,the novel robust PID controller result to solve a convex optimal problem subjected to several inequality constraints.Secondly,the algorithms of GA,PSO,PSO-GA and GA-PSO are analysed by using test benchmark functions.Finally,GA-PSO.The GA-PSO algorithm is used for tuning the robust PID controller parameters based on the underlying constrained optimization problems without resolving complex mathematical calculations.The results indicated that in comparison with the traditional Ziegler-Nichols and Cohen-Coon PID parameter tuning methods,this method is better dynamic and static performances.5.Based on heavy energy consumption and high operational costs in wastewater treatment process,the optimization and control methods based on intelligent algorithm(genetic algorithm,particle swarm optimization and GA-PSO algorithm)with division of work strategy are provided for the setpoint determination problem of controlled variables in the wastewater treatment process.According to the economic performance index considering both the effluent quality and the operation cost,the optimal setting values of dissolved oxygen and nitrate nitrogen concentration for each day are calculated by intelligent algorithm.Then,the optimal setting values are adapted to design controllers on each time period that are validated in wastewater treatment system.And on the simulation platform of BSM1 provided by the international water association,the setpoints of controlled variables are optimized by the proposed algorithm.The simulation results show that the optimized control system results in a decrease of operational costs by comparing with constant control strategy and GA-PSO algorithm can achieve better effect than those of genetic algorithm and particle swarm optimization algorithm.