نوع مقاله : مقاله پژوهشی
1 مدیریت منابع آب، دانشکده مهندسی شهید نیکبخت، دانشگاه سیستان و بلوچستان، زاهدان،
2 مدیریت منابع آب، دانشکده فنی و مهندسی، دانشگاه یاسوج
3 دانشگاه تربیت مدرس، تهران
4 گروه مهندسی عمران، دانشکده فنی و مهندسی ، دانشگاه یاسوج
5 استادیار، گروه مهندسی آب، دانشگاه فسا، شهر فسا، ایران.
عنوان مقاله [English]
One of the key factors affecting leakage in water distribution systems is network pressure management. The main objective of pressure management in water distribution system is to minimize water leakages along with maintaining the required pressure at every node. A common way to reduce pressure is to locate flow or pressure reducing valves (PRV) and optimal regulation of these vales in water networks.This study aimed at investigating optimal pressure management problems so as to minimize leakage in water distribution networks. To do so, a two-phase approach was proposed for both optimal positioning and setting of pressure reduction valves (PRVs), where location optimization was addressed at the first stage and optimal operation issue of valves was considered in the next step. In the present research, in order to resolve these problems, an optimization model—a simulation based on an emerging algorithms inspired by honeybees’ behavior called as Artificial Bee Colony (ABC)—was utilized. In this model, ABC optimization in MATLAB environment was integrated with hydraulic simulation of EPANET model. In the next step, the obtained results were compared with those of the previous studies. The results revealed that in case all the limitations of the problem are observed, employing this method to determine the position of the pressure reduction valves and regulate them lowered the mean leakage rate of the network from 82.28 to 72.15 lit/sec by 14.75% in three phases of maximum, average and minimum aquatic need periods.This means that the proposed method was effective in regulating the pressure level to minimize leakage in networks. Comparing the results of the present method with those of the previous approaches revealed that GA algorithm reached better response in fewer times than ICA, CA and ABC methods.