CAJ | 학술논문

供水管网的压力管理对实现按需供水、减少漏损和降低能耗具有重要的意义，实现压力管理的重要手段是对供水管网进行数学建模和数值仿真。但是，供水管网是非常复杂的大型非线性系统，按照传统的微观建模方法建立的供水管网模型往往精度不够，其数值求解效率也低，不适合基于这样的模型进行压力管理。该文提出了一种基于人工神经网络的供水管网压力管理的方法，即利用人工神经网络在供水管网的压力和流量之间建立非线性模型，并利用该非线性模型进行供水管网压力管理，而供水管网的压力管理则通过最优化问题的数值求解来实现。试验表明在不降低流量的情况下，供水的水压可降低1％。这对于减少供水管网的漏损、降低产销差率以及减少能耗具有重要的意义。
공수관망적압력관리대실현안수공수、감소루손화강저능모구유중요적의의，실현압력관리적중요수단시대공수관망진행수학건모화수치방진。단시，공수관망시비상복잡적대형비선성계통，안조전통적미관건모방법건립적공수관망모형왕왕정도불구，기수치구해효솔야저，불괄합기우저양적모형진행압력관리。해문제출료일충기우인공신경망락적공수관망압력관리적방법，즉이용인공신경망락재공수관망적압력화류량지간건립비선성모형，병이용해비선성모형진행공수관망압력관리，이공수관망적압력관리칙통과최우화문제적수치구해래실현。시험표명재불강저류량적정황하，공수적수압가강저1％。저대우감소공수관망적루손、강저산소차솔이급감소능모구유중요적의의。
Pressure management on a water-supply network is significant to realize on-demand supply,to restrain the leakage,and to decrease energy consumption. Since the water-supply network is a complicate large scale nonlinear system,its numerical modeling, which is necessitated in the pressure management,cannot be accurately and efficiently fulfilled through the conventional microscopic methodology. A new ANN-based approach was proposed to model a water-supply network with 52 pressure probes and 32 flowrate probes. An acceptable nonlinear relationship between pressure measurements and flowrate measurements was established through a BP artificial neural network,which was utilized as the numerical model in the pressure management manifesting itself as a numerical optimization problem. The numerical test observes about 1% reduction in the pressure without flowrate violation. The novel approach and the numerical results presented in this paper are vital to the pressure management of water-supply networks.