Decomposition algorithm for PVC plant planning optimization based on piecewise linear approximation

GAO Xiaoyong; FENG Zhenhui; WANG Yuhong; HUANG Dexian

doi:10.11949/j.issn.0438-1157.20171532

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Decomposition algorithm for PVC plant planning optimization based on piecewise linear approximation

更新时间：2025-12-10

- Decomposition algorithm for PVC plant planning optimization based on piecewise linear approximation
- CIESC Journal Issue 3, Pages: 953-961(2018)
- 作者机构：
  
  1. 中国石油大学(北京)海洋工程研究院,北京,102249
  2. 中国石油大学(北京)自动化系,北京,102249
  3. 中国石油大学(华东)信息与控制工程学院,山东,青岛,266580
  4. 清华大学自动化系,北京,100084
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20171532
  CLC： P29
- Online First：05 March 2018，
  
  Published：05 March 2018
- 稿件说明：
移动端阅览
GAO Xiaoyong, FENG Zhenhui, WANG Yuhong, et al. Decomposition algorithm for PVC plant planning optimization based on piecewise linear approximation[J]. CIESC Journal, 2018, (3): 953-961.
DOI：

GAO Xiaoyong, FENG Zhenhui, WANG Yuhong, et al. Decomposition algorithm for PVC plant planning optimization based on piecewise linear approximation[J]. CIESC Journal, 2018, (3): 953-961. DOI： 10.11949/j.issn.0438-1157.20171532.

摘要

聚氯乙烯全流程生产过程计划优化往往描述为复杂MINLP模型，求解难度非常大，为此引入分片线性技术逼近实际生产中的非线性特征，建立基于HH的MILP模型，进一步提出一种基于离线层级模型的分解算法来加速求解过程：第一层在对生产设备以最优能耗点进行层级划分得到离线层级模型的基础上优化一个等价MILP问题，确定表征设备操作状态的二值变量；第二层以HH模型为基础，在二值变量确定的情况下，代入计划优化模型调整设备的工作点，最终确定模型的最优操作决策方案。最后，以一个实际工厂规模的案例来验证模型和算法的有效性，结果表明本算法在基本不损失优化结果性能的前提下可以大大提高求解效率，缩短求解时间达99%以上。

Abstract

In the previous work

the multiperiod planning optimization model of the whole production process has been presented. Due to the high energy consumption characteristics of PVC produced by calcium carbide method

the piecewise linear model to approximate the nonlinear items in real process was introduced and a mixed integer linear programming (MILP) model was established. However

it is difficult to solve it due to the large scale and the complex nonconvexity. Thus

a hierarchical decomposition algorithm is proposed to accelerate the computation progress. The problem is divided into two levels

in the first level

the operating states of equipment are optimized

which would be the hard-to-solve binary variables in the plantwide planning model; in the second level

the determined binary variables are embedded into the plantwide planning model

and thus a reduced scale scheduling optimization is executed. A case study was provided to verify the effectiveness of the proposed algorithm. Computational results show that the proposed algorithm can accelerate the computation greatly and the production costs are close to or even better than those given in the previous work.

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