流程工业配料过程的随机优化模型及求解算法

针对配料过程原料质量参数存在的不确定性，以原料消耗成本最小为优化目标，将不确定质量参数以随机数的形式引入质量指标约束中，建立了一种配料过程随机优化模型．考虑传统蒙特卡洛抽样方法的不足，采用一种更高效的Hammersley sequence sampling（HSS）技术，获得随机优化模型对应的期望值优化模型．将HSS技术用于遗传算法的种群初始化和交叉、变异操作，以保证种群分布的均匀性，实现随机优化问题的有效求解．工业应用实验结果表明，所提方法不仅能够有效降低原料的消耗成本，而且能够保证产品质量指标满足生产要求，优化结果具有较好的鲁棒性，为配料过程的随机优化控制提供了一个优化模式．

Stochastic Optimization Model and Solution Algorithm for Blending Procedure of Process Industrial

Considering the uncertain quality parameters of raw materials in industrial blending processes, we propose a stochastic blending optimization model, which takes the cost of the raw materials as the optimization objective and incorporates uncertainty parameters into the quality constraints as random variables. Then, in order to overcome the shortcomings of the Monte Carlo sampling technique, we apply the more efficient Hammersley sequence sampling (HSS) technique to obtain an expectation optimization model that corresponds with the stochastic model. We use the HSS technique in the crossover and mutation steps of the genetic algorithm to maintain uniformity of the population and to effectively solve the stochastic blending optimization problem. The results of our industrial experiment show that the proposed method not only greatly reduces the consumption cost of raw materials, but also guarantees the quality of the blending product. This method has robustness and yields a good stochastic optimization mode for blending processes.