Model Predictive Control Of PH Value In Wet Flue Gas Desulfurization Process

China’s social economy is thriving and flourishing,which gives rise to substantial increase of demand for energy.In recent years,the scale of renewable energy applications,for example,wind and solar power generation has been continuously developing.However,the thermal power still accounts for more than 70% of the market.Due to the increasing demand for electricity,a large amount of SO2 emitted from coalfired power plants with high load have given risen to great harm to the natural environment and health of human beings.In order to solve the conflict between the increasing demand for electricity and ecological protection,most thermal power plants respond to national policies and are equipped with desulfurization devices to achieve ultra-clean emission targets.Therefore,finding the method to make sure the effective operation of desulfurization devices in thermal power plants is of great significance in many disciplines.This thesis takes the limestone wet flue gas desulfurization(WFGD,Wet Flue Gas Desulfurization)process of thermal power generating units as the research object.On the basis of in-depth analysis of key factors affecting the desulfurization efficiency,the following four parts of research work are carried out based on the model predictive control algorithm:(1)The structure and composition of the wet flue gas system of the coal-fired power plant were analyzed,and the desulfurization principle of the process was briefly described according to the main chemical reactions.Then the influence of a single key factor on the overall desulfurization efficiency was briefly analyzed,providing a theoretical basis for subsequent research work.(2)Utilize a 1000 MW coal-fired unit as the subject matter.Firstly,obtain the historical data of the desulfurization system operation through the plant-level monitoring system.Then import the preprocessed experimental data into the MATLAB system identification toolbox to obtain the pH value of the absorption tower slurry.The Hammerstein-Wiener nonlinear model provides a model basis for subsequent control simulation research.(3)By studying the control difficulties of the desulfurization tower object and properties of the slurry pH value model in dynamic and static condition,a predictive control transformation strategy based on the Hammerstein-Wiener inverse model compensation is designed.Using the basic principle of the dynamic matrix algorithm,by adding the inverse model of the nonlinear link,the overall control object presents the characteristics of linear or approximately linear.In this way,a large number of online calculations required to solve the nonlinear model has been avoided.In addition,the control strategy also considers the input constraints existing in the actual system,so that the control effect is closer to the actual operating conditions.(4)In order to verify the effectiveness of the inverse model compensation predictive control strategy,a tracking control simulation study was carried out on the Hammerstein-Wiener model of the pH value of the absorber slurry.The simulation results show that,the predictive control strategy proposed in this thesis is more suitable for the nonlinear process of slurry pH change,and the tracking control performance is superior to the PID controller.Finally,the influence of four parameters on the performance of the controller are mainly studied through simulation experiments,which further confirms the theoretical research of system stability analysis.