This paper describes an optimization prolem to minimize the cost of power consumption for the electrochemical porocess of zinc (EPZ) depending onvarying prices of electrical power. Ae series of conditional experiments was conducted to obtain enough data, whic reflect the complex relationships among the factors influecing power consumption. Two backpropagation neural networks are used to build a process model that describes these relationships. An equivalent Hopfield neural network is cnstructed to solve this nonlinear optimization poroblem with tcchnological constraints, a penalty function is introduced into the network energy function to meet the equality constraints, and inequality constraints are removed by altering the sigmoid function. An optimal power-dispatching control system (OPDCS) has been developed to provide an optimal power-dispatching scheme and keep the ePZ running economically. Since the OPDCS was put into service in a smeltery, the cost of power consumption has decreased significantly, and cost of power consumption has decreased significantly, and it also contrbutes to halancing the power grid load.