Research On Operation Optimization Technology Of Wet Desulfurization System Of Thermal Power Plant

In recent years,as the national air pollution control has become more and more stringent,many power plants have carried out ultra-low emission transformations on the desulfurization system.While effectively reducing SO₂emissions,it will generate a large amount of material and energy consumption.Research on the optimization of the operation of the desulfurization system is important for power plants.The energy-saving and emission reduction is of great significance.This paper takes a wet desulfurization system of a unit as the research object,analyzes the influencing factors and cost of the desulfurization system on the basis of the technological process,and establishes a rolling regression model to optimize the slurry circulation volume,while reducing operating costs as the goal.Combining operation target value mining with boundary condition prediction,a data-driven desulfurization system operation optimization strategy is proposed.First,the process flow of the desulfurization system is introduced,the main reaction mechanism in the absorption tower is explained,and the typical subsystems of the desulfurization system are introduced and explained.Analyze the influencing factors and costs of the desulfurization system,and discuss the three aspects of absorbent factors,flue gas factors,and operating factors from the aspects of desulfurization efficiency.The costs incurred during the operation are discussed in terms of material consumption,energy consumption,and sewage.Analyze at all levels,give calculation models of various costs,and analyze the reasons for deviations in combination with actual operating data,providing a theoretical basis for subsequent research.Secondly,select variables that have significant correlation with desulfurization efficiency,analyze the correlation between the main factors affecting desulfurization efficiency,convert the exponential relationship between desulfurization efficiency and various influencing factors into linear relationships,and use multiple regression to establish a static model of desulfurization efficiency.In actual operation,due to changes in working conditions,the model often changes with the migration of working points.A regression model of desulfurization efficiency based on rolling window is proposed.In practical applications,the current parameters can be input into the model to obtain the lowest circulating slurry volume.The minimum circulating slurry volume guides the commissioning of the slurry circulating pump.Finally,a data-driven desulfurization system operation optimization strategy is proposed.The operation optimization of the desulfurization system is divided into two parts:the establishment of a target value operating condition library and the prediction of boundary conditions.First,the fuzzy C-means clustering algorithm（FCM）is improved,with the unit desulfurization cost as the goal,and on the premise of meeting the emission requirements,Use the improved FCM algorithm to find the operating target value under specific operating conditions,establish a library of operating target operating conditions,and then use the rolling ARIMA model to make advance short-term predictions of the boundary conditions of the desulfurization system.In actual operation,the operating target value of the corresponding parameter can be found from the working condition library through the predicted value of the boundary condition to guide the operation of the operator.