A Study On Predicting Models And Strategies For SO₂Discharge From Thermal Power Industries

Sulfur dioxide (SO2), which brings great harms to the environment, is one of the main components of the formation of acid rain. The energy structure in China, with the primary energy that mainly consists of coal and the secondary energy which is largely made up of thermal power, cannot be changed fundamentally in a short period, and the government controls the gross discharge of the pollutants, which makes the issue of SO2discharge in thermal power industry be of the top priority of pollution abatement for the country. The studies on controlling the discharge of SO2in thermal power, which were made by the scholars at home mainly focused on macro system level. And there are few studies made from the view of quantitative property.The thesis, taking the factors for the SO2emission reduction in electric power industry in province X as the object of the study, in the premise of analyzing the macro and the micro factors, uses the optimum subset, integrates with the control experience at abroad and comes up with the key factors affecting the discharge of SO2in the electric power industry. Besides, the thesis sets up models of SO2emission by adopting the partial least square algorithm and panel data model, and then by comparing the both finds the optimal model--panel data model. After that, the thesis uses the optimal model to make a prediction for the discharge in some cities from2011to2015. Based on the precious, the thesis suggests some strategies for SO2discharge controlling.Results of this thesis:(1)Confirming the factors for SO2discharge in thermal power industriesThis thesis analyzes the factors affecting SO2discharge in thermal power industries systematically according to the economical levels, technical levels and policy requirements and combining the specific circumstances of province X in southwest of China. Based on this, the thesis takes advantage of the optimum subset to screen the factors and picks out the most representative key factors of SO2discharge in thermal power industries.(2)Setting up and comparing the two models---the partial least square modeland the panel data model for SO2discharge in thermal power industriesThe key factors obtained from the optimum subset screening builds on the two models---the partial least square model and the panel data model for SO2discharge from thermal power industries separately. The thesis analyses parameters and accuracy of the prediction of the two models and comes to the conclusion that the panel data model is the optimal model.(3)Suggesting some pertinent strategies for SO2discharge from thermal power industriesAccording to the results of model analysis, the thesis points out a number of critical issues in the process of SO2discharge from thermal power industries. In the last part, the thesis suggests some strategies for SO2discharge from thermal power industries in terms with the policy formulation of the country, the adjustment of energy structure, the innovation of techniques, and so on.