Research On Thermal Power Pollutant Emission Models And Emission Reduction Cost Optimization Model

Combustion is the main way of using coal, but inevitably accompanied by the release of a large number of pollutants in the process of combustion, so coal is not a kind of clean energy. In the process of coal combustion, the main pollutants are sulfur dioxide (SO2), nitrogen oxide (NOx), etc. How to better control the emissions of pollutants become the focus of attention all over the world. The pursuit of economic benefits must be in place to ensure that the premise of environmental benefits, environmental benefits and economic benefits and win-win is our unremitting efforts to target. From the burning of coal sulfur dioxide (SO2), nitrogen oxide (NOx) although the generating mechanism of different, but in the process of generating synergy, sulfur nitrogen pollutants affect each other, or to promote or inhibit, so both are the influence factors of the other party each other. Combustion conditions are not at the same time, a kind of pollutant emissions change causes another kind of pollution subsequently change, or same increase or decrease or shift, so the prediction model for single pollutant can’t accurately grasp the situation of the unit operation. Based on the BP neural network model, using the classic MIV variable selection method, in traditional input variable, in terms of synergy, the introduction of a moment of sulfur dioxide (SO2), nitrogen oxide (NOx) concentrations were as input variables, then with genetic algorithm to optimize neural network model, the accuracy of the model to predict pollutant emissions is improved. Unit according to the removal of SO2 and NOx costing, the cost of denitration is much higher than desulfurization, whereas the unit content of SO2 in smoke and NOx a lot higher, besides there is synergy, influence with each other, between two generation or same increase or decrease or shift, resulting in different units under the running parameters of exhaust pollutants removal differences between the total cost. So in order to reduce the unit, total cost of the smoke exhaust pollutant removal unit in the parameter optimization model to minimize the total cost for exhaust pollutants removal optimization goal, through the extremal optimization to find the best operation parameters, the way of air distribution, such as oxygen constantly optimized, can provide a reference for the operation of the power plants.