Research On Soft Measurement And Adaptive Optimal Control Strategy In Wastewater Treatment System

Biological wastewater treatment process isone of the most widely，the most effective andeconomic technology in the field of wastewater treatment and water pollution control incurrent and future for a long time。For urban wastewater treatment process is a complexbiological and chemical process，multivariable and randomness ambiguity, time-varying,nonlinear and multiple objective features,the application of traditional control method cannoteffectively solve these problems, so it is hard to optimize system. Therefore, the intelligentcontrol system which takes optimal water quality and energy conservation as the goal is thekey to solve the problem of optimization control in the in the wastewater treatment plant atpresent.This topic is to solve the wastewater biochemical treatment system operation controlof a few problems is based on the theory of intelligent control technique。This thesis comes from National Natural Science Foundation of China (No.60774032)，Special Research Fund of Ministry of Housing and Urban-Rural Development of China underGrant (No.2010-k9-47)，and Key Project of Guangzhou Scientific Program of China underGrant （No.2010Z1-E301），‘Fund of Innovation Creation Academy Group’ established by theGuangzhou Education Bureau（No.2009-11）。This topic in the process of bio chemical wastewater treatment for the control of the fouraspects of research: the water quality control，aeration control， sludge concentration controland energy-saving optimization control, Respectively using intelligent algorithm for softmeasurement modeling to predict water quality control; For aeration system is a kind ofglobal asymptotic stability of the adaptive neural network control; On the sludgeconcentration implement robust direct adaptive fuzzy control; Finally water quality asrestriction factorsas the boundary conditions of the ost of aeration and the recyle sludge,emission optimization fuzzy discrete particle swarm optimization (fdpso) algorithm fordynamic optimization, in order to achieve the whole wastewater treatment plant of the lowestoperating cost optimization control, achieved better effect is validated by computer simulation,for wastewater plant energy saving optimization control provides theory basis, has obtainedcertain research results, has theoretical and practical significance。The main contents of the thesis are outlined as follows.1. Many wastewater treatment process parameters, including water quality, water quantity,load, sludge properties, such as dozens of indicators relate to each other,in this paper, the wastewater plantbiochemical oxygen demand predictive control based on laplacian eigenmap(LE) and support vector machine (SVM) method to establish the soft measurement model。First use of Laplace feature mapping was carried out on the measurement data processing,solve the problem of data relative between each other, so as to improve the accuracy androbustness of the model. At the same time, the application of support vector machine (SVM)modeling method to improve the generalization ability of water quality soft measurementmodel. Using wastewater collection data, the simulation results show that wastewatertreatment soft measurement model based on support vector machine (SVM) have goodprediction effect compared with RBF.2. Aiming at aeration system transfer coefficient of dissolved oxygen in the gain controlparameter uncertainty, presents an adaptive neural control (ANC) strategy that guaranteesglobally asymptotic tracking for a class of uncertain nonlinear systems with function-typecontrol gains. A proportion differentiation (PD) control term with variable gain is developedto globally stabilize the plant so that neural network approximation is applicable. A statetransformation is applied to solve the control singularity problem resulting from the unknowncontrol gain function. A robust control term is developed to achieve asymptotic tracking of theclosed-loop system. Compared with previous global asymptotic tracking ANC approaches, theproposed approach not only simplifies the selection of PD gain, but also relaxes chattering atthe control input. Simulation results have demonstrated the effectiveness of the proposedapproach. The simulation experimental results show that the dynamic stability in this paper,the proposed method cans effectively the concentration of dissolved oxygen compared withPI.3. Aiming at sludge concentration control recyle sludge discharge systems the direction ofthe uncertainty problem, presents a new robust adaptive fuzzy control algorithm directly。Theoverall control input contains a basic direct AFC term and an additional robust control term. ALyapunov-based ideal control law is proposed to solve the control singularity problem and theNussbaum gain technique is applied to solve the control direction problem. Using ane2-modification in adaptive laws, it not only obtains bounded adaptive parameters, but alsoachieves asymptotic convergence of tracking errors. Moreover, the proposed controller hasmore compact structure compared with the previous indirect approaches. Simulated studieshave demonstrated the effectiveness of the proposed approach.the simulation results show thatthis method is to be discharged through the recyle sludge, the sludge concentration of sludgeeffectively stabilize biochemical pool, verify the validity of the proposed method in this paper. 4. Aiming at the optimal control problem of wastewater treatment process operation cost,the fuzzy discrete particle swarm optimization control is proposed to calculate the optimalvalue of operation cost, which takes the two most important control parameters, sludgewastage and dissolved oxygen as control variables, regards total substrate discharge andeffluent water quality as restriction factors and operation cost of residual sludge treatment,sludge recyle and aeration as performance index. In the same water quality under the premise,based on the fuzzy particle swarm of inertia weight strategy to find the lowest cost of theoptimal solution. The test results show that the method can effectively improve the diversityof particle swarm, a reliable global convergence and fast convergence speed, can in thewastewater treatment process optimization effectively in dynamic environment. Simulation ofoptimal algorithm is of high search efficiency and low mean and variance of wastewatertreatment operation cost.