论文标题
在工业干燥中有效模块化：组合优化观点
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint
论文作者
论文摘要
工业干燥过程约占制造中使用的总能量的12％，通过改进的过程控制和新的干燥技术的发展，能源使用的可能性减少了40％。为了实现具有成本效益和高性能的干燥，可以将多种干燥技术组合在一起，以最佳的测序和控制参数的最佳测序和控制参数组合。本文提出了此优化问题的数学公式，并提出了一个基于最大熵原理（MEP）的框架，以同时求解控制参数的最佳值和最佳序列。所提出的算法解决了与多个局部最小值的非凸成本函数的组合优化问题。与最有效的单阶段干燥过程相比，对干燥蒸馏剂干谷物（DDG）产品的仿真结果表现出高达12％的能源消耗。拟议的算法会收敛到局部最小值，并以启发式设计以达到全球最低限度。
The industrial drying process consumes approximately 12% of the total energy used in manufacturing, with the potential for a 40% reduction in energy usage through improved process controls and the development of new drying technologies. To achieve cost-efficient and high-performing drying, multiple drying technologies can be combined in a modular fashion with optimal sequencing and control parameters for each. This paper presents a mathematical formulation of this optimization problem and proposes a framework based on the Maximum Entropy Principle (MEP) to simultaneously solve for both optimal values of control parameters and optimal sequence. The proposed algorithm addresses the combinatorial optimization problem with a non-convex cost function riddled with multiple poor local minima. Simulation results on drying distillers dried grain (DDG) products show up to 12% improvement in energy consumption compared to the most efficient single-stage drying process. The proposed algorithm converges to local minima and is designed heuristically to reach the global minimum.
