| Rural water supply projects are important part of rural water conservancy and rural construction.Rural water supply projects are of great significance in improving residents' living conditions,improving residents' health,promoting unified management of water resources and strengthening the status of water conservancy.Studying the optimization problem of the pipe network water supply system can effectively reduce the cost of the pipe network and the operation and management costs of the pipe network.At present,there are many researches on the optimization of urban water supply network,while there are fewer problems in optimizing rural water supply networks.In this paper,from the perspective of the optimization design of rural water supply networks,the economic and reliability multi-goal is established,taking the network construction cost and the operation management cost as an economic goal and taking node average surplus head as the reliability goal.The optimization model,based on MATLAB programming,applying improved genetic algorithm,combined with engineering examples,comparing single-objective and multi-objective optimization results,comparing the cost of pipe network under multi-case conditions,and giving a better solution.The main research carried out is as follows:(1)Establish a multi-objective pipe network optimization model based on economy and reliability,and analyze the hydraulic characteristics of the water supply pipe network system to determine the total water flow rate and the water pressure of each node,so as to meet the user satisfaction in the pipe network optimization process.Requirements for water volume and water pressure.The hydraulic calculation in the optimization process of the pipe network is mainly the continuity equation of each node,the energy equation,and the pressure equation between each pipe segment.(2)This paper uses the improved genetic algorithm and NSGA-II as the basis of pipe network optimization,and uses LM algorithm as the solving algorithm of nonlinear equations.Based on MATLAB programming,the pipe network optimization is carried out.Among them,the basic steps of improving the genetic algorithm include coding and initial population formation,fitness value evaluation,selection operation,cross operation and mutation operation.(3)Taking two rural water supply pipe networks as engineering examples,using decimal genetic coding,the population number is set to 100,the maximum number of iterations is set to 200 generations,and the number of operations is set to 50 times for optimization,respectively,regardless of accident conditions.Single-target pipe network optimization and multi-target pipe network optimization,as well as single-target pipe network optimization and multi-target pipe network optimization when considering accident conditions.(4)The first example is a single-source water supply network.When the accident conditions are not considered,the annual cost of the network obtained by single-objective optimization considering only economy is 2.075 million yuan,and the average surplus head of the nodes is 14.94 m.The annual cost of the network obtained by multi-objective optimization considering economy and reliability is 2.461 million yuan.The average surplus water head is 4.65 m.The annual cost of multi-objective optimization is 4.5035 million yuan higher than that of singleobjective optimization,which means the cost increases by 22.59%.When considering the accident conditions,the annual cost of the pipeline network is 2.2707 million yuan and the average surplus water head of the nodes is 8.38 m for the single objective only considering the economy;the annual cost of the pipeline network is 2.5887 million yuan and the average surplus water head of the nodes is 5.34 m for the multi-objective optimization,which takes into account the economy and reliability;and the annual cost of the multi-objective optimization is higher than that of the single objective optimization.The cost increased by 14.0% and the surplus water head decreased markedly.(5)The second example is a double-source water supply network,in which the annual cost is 0.43 million yuan and the average surplus head of the nodes is 8.99 m after single-objective optimization without considering the accident conditions,and the annual cost of the network is 0.508 million yuan after multi-objective optimization considering the economy and reliability,and the nodes are flat.The average surplus head is 8.57 m.The annual cost of multiobjective optimization is 0.731 million yuan higher than that of single-objective optimization,that is,16.81% higher than that of single-objective optimization and the surplus water head decreased.When considering accident conditions,the annual cost of single-objective optimization is 0.532 million yuan,and the average surplus head of nodes is 12.13 m.The annual cost of multi-objective optimization considering economy and reliability is 0.566 million yuan.The average point surplus head is 10.11 m.The annual cost of multi-objective optimization is 0.037 million yuan more than that of single-objective optimization,that is,the cost increases by 6.33% and the surplus water head decreased markedly and the surplus water head decreased markedly.Generally speaking,multi-objective consideration of accident conditions,although the annual cost is high,but it fully takes into account the node surplus head,and can effectively reduce the accident rate,which is more desirable in practical engineering applications.From the Pareto frontier diagram obtained from multi-objective network optimization,it can be seen that the surplus head decreases when the operation cost increases gradually,while the surplus head is larger when the operation cost is lower.In practical engineering,both economy and reliability should be taken into account.Case study shows that the method proposed in this paper can solve the optimization problem of circular water supply network with comprehensive consideration of economy and reliability. |
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