Study On Reginal Shared Technology Of Water Supply Facilities

Regional water supply was a task which strongly involved in political and technical factors and had been successfully applied abroad and in some domestic area. It was essentially important to deal with some critical puzzles such as lack of fundamental water supply facilities in small town and rural area, difficulty to maximize the efficiency of scale economics for water-supply industry, contradictions existing in water protecting and pollution treatment, and unknow of confirmation way on safety, quality and satisfy volume. However, at present, in china, regional water supply was still on its way of groping, with mainly decided by personal subjective experience and without systematical method support yet. It was more like a bottom-up spontaneous action strategically, which would definitely face the obstacles when considering connecting administrative divisions. And tactically, the original subjective methods were still dominated and far more enough in resolving the above mentioned puzzles in regional water supply. This thesis initially merged three theories into original water supply research, i.e. scale economies effect, facility location theory, large scale system optimization theory, and provided a scientific method to define quantitatively from candidate water resources, slot & number of water plants, supply area and scale.Based on a wide references home and abroad, this thesis analyzed current method of regional water supply in China, and did systematically elaboration on theory fundamental of regional water supply with aspects: scale economies effect, facility location theory, and large scale system optimization theory,and briefly analyzed the application of the various theory in water supply sharing. This thesis analyzed the influencing factors of location for water resource and water plants from engineering technical perspective, and used two-stage location to determine the location of water resource and water plants for candidate. Locations of water resource and water plants were preliminary screened according to the basic material and influencing factors of water resource and water plants in the first stage. Based on the first stage, this thesis established the hierarchical structure model, constructed judgment matrix along with weight calculation, and quantitatively defined the position of candidate water resource and water plants. The thesis pointed out that the traditional water supply analysis failed to explicitly illustrate the internal relationship of each sub-system in water supply system. According to large scale system optimization theory, regional water supply system was divided into five subsystems: water subsystem, water delivery subsystem, water purification subsystem, water distribution system, and user subsystem. It pointed out the supply and demand balances between water system and water supply system, and water supply system and user requirement respectively. Througe large scale analysis, it also figured out that regional water system had a hierarchical structure of regional water system under the overall objective. The thesis defined the association relationship of various subsystem using user subsystem as the input variable of water distribution subsystem, water purification subsystem for the out variable, and based on this, water purification subsystem as the input variable of water delivery subsystem, water subsystem for the out variable. Based on the large scale system hierarchical control theory, the thesis divided regional water supply system into two interrelated subsystem, and constructed decomposition-coordination model by interaction prediction.This thesis hypothesized the minimal system engineering cost as target, established a layout optimization model of water supply facilities for regional spacial layout via introducing facility location theory, and decomposed and coordinated the model by utilizing the decomposition-coordination method. The optimal solution of each subsystem was obtained by Particle Swarm Optimization, and it was fed back to coordinator. Then, the optimal solution of coordinator was obtained by Particle Swarm Optimization.