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This paper describes an optimization problem to minimize the cost of power consumption for the electrochemical process of zinc (EPZ) depending on varying prices of electrical power. A series of conditional experiments was conducted to obtain enough data, which reflect the complex relationships among the factors influencing power consumption. Two backpropagation neural networks are used to build a process model that describes these relationships. An equivalent Hopfield neural network is constructed to solve this nonlinear optimization problem with technological 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 it also contributes to balancing the power grid load.