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成品油调合对提高炼厂经济效益有着重要的作用和意义。成品油调合优化是一个非线性约束优化问题,传统的进化算法由于搜索空间大又没有结构信息,要取得期望的求解效率和解的稳定性都是具有挑战性的任务。针对上述问题,提出了一种基于分片线性代理模型的成品油调合优化方法,它包含分片线性建模和优化2部分内容。首先,利用分片线性函数模型作为成品油调合非线性调合性质指标函数的代理模型,将原非线性约束优化问题转化为一系列线性规划子问题;然后,利用差分进化算法搜索相关线性子区域来获得全局最优值,以达到提高进化算法的求解速度和避免算法陷入局部最优解的目的;最后,通过成品油调合优化案例验证了该方法的有效性。
Blending of refined oil has an important role and significance in improving the economic benefits of the refinery. Blending optimization of refined oil is a nonlinear constrained optimization problem. Due to the large searching space and no structural information, the traditional evolutionary algorithm is a challenging task to obtain the desired efficiency and stability of solution. In order to solve the above problems, this paper proposes a blending optimization method based on piecewise linear proxy model, which includes two parts: linear modeling and optimization. Firstly, the original non-linear constrained optimization problem is transformed into a series of linear programming sub-problems by using the piecewise linear function model as the proxy model of the non-linear blending property index of refined oil blend products. Then, the differential evolution algorithm is used to search for the related linear sub-problems Region to obtain the global optimal value, so as to achieve the goal of improving the speed of solving the evolutionary algorithm and avoiding the algorithm getting into the local optimal solution. Finally, the effectiveness of the method is verified by the refined oil blend optimization case.