Study On Optimization Of Water Supply System

In the water supply system, water source and water production scale has a very direct impacton water quality, quantity and pressure. The optimal operation of water supply system should pay attention to the way of realizing the economic benefit, social benefit and environmental benefit.Urban water demand prediction as the basis of long-term planning for urban water supply systemhas a extremely important position, the prediction accuracy will decide on whether to meet the needs of the development of urban water supply and drainage system planning. This paperdescribes the research status and theoryof urban water demand forecasting method, and uses of PSO-GM(1,1,λ,ρ) model with PSO- DV-BP neural network model of combined forecasting to establish the forecast model of urban water demand.On the basis of the research results, using the theory of system engineering and mathematical optimizationto optimize water source and water production scale. After determined the position of waterworks and watersource, building optimization model, analyzing the design of water plant scale and water source and optimizing the cost of water and water treatment costs to achieve optimal constraints. In the optimization model, considering the following several parts: water resources fee, pump stations, water pipes. Each part of the cost only considers thepart of changing the whole system cost and ignores the influence of the invariant,building and solving the model.The model belongs to the planning problem under the nonlinear objective function and constraint conditions. Through the analysis of the characteristics and applicable conditions of various algorithms, selecting the improved genetic annealing algorithm to solve this model.This algorithm belongs to the hybrid intelligent optimization algorithm, has the characteristics of the foster strengths and circumvent weaknesses, greatly improves the global and local convergence ability.Combining the Sichuan province city water supply case,research results will be applied to the optimization of the city water supply system, combined with the actual situation of the city such as water, plant layout and city development demand.