Closed Municipal Solid Waste Direct Gasification And Melting Incineration Process Control Strategy Research

With the accelerating pace of the urbanization, municipal solid waste (MSW) has been increasing rapidly due to the shorter MSW production period in cities. As a result, most cities in China are faced with the problem of MSW treatment. At present, the direct gasification melting incineration (DGMI) technique of MSW, characterized by being pollution-free, recyclable and low in decrement, has been widely used and thoroughly studied.How to stabilize the gasified melting in MSW's DGMI process is the key to the effective disposal of MSW, the control of dioxin disposal and the use of heat energy. But the intrinsic features of MSW complicate the physiochemical process in MSW's DGMI process. In light of the features of MSW and the present development of DGMI technique, the dissertation aims to study the stability of MSW's DGMC process. Whereas, any attempt on this basic research in this field is not made in our country up to the present.The dissertation firstly introduces the main techniques used for incineration treatment of MSW in China and analyzes and evaluates the advantages and disadvantages of each technique. Based on the analysis of MSW incineration technique and in light of the mibishi (closed) MSW incinerator which was developed by our university, the dissertation proposes the theoretical framework of the MSW's DGMI control process and presents the systematic and thorough studies which are based on the theoretical and emulative basis.The studies are conducted from the following perspectives:The dissertation sums up the application and development of MSW's DGMI, expounds artificial nerve network (NN) model and learning algorithm (LA), probes into the latest research and application of MSW's DGMI technique, analyzes the technical process of direct gasification melting incinerator of MSW and proposes the control system components of the mibishi (closed) MSW incinerator.The analysis of the mechanism in MSW's DGMI process results in the identification of the main factors that affect the temperature of melting zone and the temperature stability of the second chamber. In light of the single fuzzy control, the dissertation sets forth the adaptive fuzzy control (AFC) strategies of the temperature of melting zone and the temperature stability of the second chamber. Besides, it presents the design and application of the AFC strategies in MSW's DGMI process. That is, the designs of the adaptive fuzzy controller of the melting temperature, the adaptive fuzzy controller in the second chamber and the fuzzy PID controller that controls the three stroke of the exhaust-heat boiler as well as various factors that affect the performance of each controller are all presented in details in the dissertation.The simulative comparisons of the above-mentioned controllers in their application in MSW treatment process demonstrates that AFC strategies help improve the performance and effectiveness of the system controllers.The dissertation also integrates the three numerations of genetic algorithm (GA), NN and fuzzy control (FC) in order to build up the typical state function in MSW's DGMI process. GA is capable of screening the optimum multi-target control strategy out of different targets and formulating automatically the FC rules in MSW's DGMI process. Intelligent control system based on the three integrated numerations of GA, NN and FC is capable of optimizing the control effect in MSW's DGMI process. The emulative experiments on the control system that is based on the three integrated soft numerations verify the fact that the intelligent controller (IC) used in MSW's DGMI process is practical and stable in performance and that IC is also of reference value in establishing other control systems for incinerating other types of MSW.In addition, the dissertation presents the soft dioxin measure model (SDMM) that is based on GA and BP NN. It also introduces the soft measure's modeling structure, modeling method, node-stimulating function of NN, net layer, learning preciseness, hidden layer node number, target function error, the soft measure, initial weight and阈值, the selection and setting of learning velocity and so on. It also compares the application effect of the pure BP numeration and SDMM that is based on GA and BP NN. Furthermore, it analyzes the control mechanism in MSW's DGMI process and the parameters that affect SDMM performance. Moreover, it explores the feasibility of the GA and NN technique that are used in the MSW's DGMI process.Finally, the dissertation summarizes the findings in the whole research, elaborates the construction of the adaptive control strategies in MSW's DGMI process and the necessary improvements of SDMM. It analyzes the future use of SDMM. The prospects and the main areas of the research into MSW's DGMI process are also presented.