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多周期公用工程系统最优综合与运行优化问题研究
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摘要
公用工程系统优化综合对于过程工业能源节约、提高经济效益具有重要意义。本文针对多周期公用工程系统最优综合与运行优化问题进行了系统地分析研究。主要研究内容包括以下几个方面:
    (1)将普遍的多周期综合与运行优化问题应用到公用工程系统,建立了多周期公用工程系统最优综合与运行优化问题的数学模型,并对该模型进行了具体化处理使之便于数学求解。
    (2)针对多周期公用工程系统运行优化问题的特点及传统分枝定界算法的局限性,采用加速算法思想,引入了改进的分枝定界算法进行求解,减小了问题的可行解空间,使得求解所需机时大为减少,最优解搜索能力增强,并通过求解实际应用算例验证了改进算法的有效性。
    (3)当多周期公用工程系统最优综合与运行优化PM问题的单元和周期增多时,可能的组合数将呈指数增长,采用通常的算法往往不能在合理的时间内获得问题最优解。针对这一问题,采用了两级分解算法,将PM问题分解为两个规模较小的子问题,建立了多周期公用工程系统最优综合模型和运行优化模型,分先后进行分步求解,减少了求解所需机时,增大了能够求解的问题规模,应用算例求解结果表明,两级分解算法对于PM问题是一种行之有效的算法。
The synthesis and operation optimization of utility systems are very important to process industry to save energy and get economic profit as much as possible. This dissertation has carried out systematic research on the synthesis and operation planning problem of utility systems for multi-periods operation with varying demands. The main research includes:
    (1) Apply a general mixed-integer optimization model for multi-periods synthesis and operation planning problem(MSOP) to utility systems and then obtain the PM model. For feasible mathematic solving, this PM model has been reformulated.
    (2) Aiming at the characters of the multi-periods operation planning problem for utility systems and the difficulties encountered when use the standard Branch-and-Bound algorithm, the thought of accelerating is adopted and the accelerated Branch-and-Bound algorithm is introduced. The solution space is decreased and the computational costs are reduced and the ability of searching optimal solution is enhanced. Through two examples, the validity of the algorithm has been proved.
    (3) For the multi-periods synthesis and operation planning problem of utility systems(PM), the combination load will show exponential growth with the number of units and periods. Usual algorithms can not achieve optimal solution in reasonable time. To overcome this problem, the Bilevel Decomposition Method is introduced. With the method the PM problem is divided into two subproblems to solve, thus computational costs are reduced and the size of problems solvable is increased. With two examples of different size the algorithm has been tested for it’s applicability and validity.
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