基于混合粒子群算法的锌电解过程能耗优化

Energy Consumption Optimization of Zinc Electrolysis Process Based on Hybrid Particle Swarm Algorithm

针对锌电解过程能耗过高的情况,研究其能耗优化问题。根据电力部门实行的分时计价政策,建立以全天锌电解过程电能消耗和总用电费用为目标的锌电解过程多目标优化模型。提出一种带加速度调整的粒子群优化算法,当粒子陷入局部最优时,通过加速度策略增强种群速度,使算法获得持续搜索的能力,有效克服早熟收敛;并和Powell算法相结合构成新的混合粒子群算法,将粒子群算法的全局搜索能力与Powell算法的局部寻优能力有机结合起来。最后将该混合粒子群算法应用于所建优化模型的求解,获得优化生产方案。仿真结果证明了该算法的有效性。工业应用效果表明,按所得优化方案组织生产降低了电能消耗,减少了用电费用。

To high energy consumption of zinc electrolysis process（ ZEP）, the energy consumption optimization problem is discussed. According to the time-sharing price policy of electric power, a muhi-objective optimization model of ZEP is established . A particle swarm optimization algorithm with accelerated velocity （AVPSO） is proposed. The sustainable searching ability of the algorithm is obtained by the accelerate strategy to the population particles velocity updating, so the premature convergence problem is overcome in effect . The AVPSO is combined with Powell to build a new hybrid PSO（HPSO） algorithm, which employs the global and local searching ability brought by PSO algorithm and Powell algorithm. The HPSO is applied to the multi-objective optimization model to obtain the optimal production scheme. The simulation results show the effectiveness of the proposed algorithm, and the practical application results show that the power consumption and its cost are decreased by using the optimal production scheme.