A two-layer chance-constrained optimization model for a thickening-dewatering process with uncertain variables,The Canadian Journal of Chemical Engineering

当前位置： X-MOL 学术 › Can. J. Chem. Eng. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A two-layer chance-constrained optimization model for a thickening-dewatering process with uncertain variables
The Canadian Journal of Chemical Engineering ( IF 1.6 ) Pub Date : 2021-08-18 , DOI: 10.1002/cjce.24298
Hualu Zhang ₁ , Fuli Wang _{1,

2} , Kang Li ₁ , Guobin Zou ₃ , Luping Zhao ₁

Affiliation

The feed mass and the filter-press mass per cabinet (FMP) are uncertain variables in the thickening-dewatering (TD) process. These uncertain variables must be considered for the optimization; otherwise, the energy economic index (EEI) and the safety risks will increase. Therefore, in this paper, a two-layer chance-constrained optimization model for the TD process with uncertain variables is proposed. The optimization model is a sample average approximate-expected value model (SAA-EVM), and scenarios are generated by Monte-Carlo simulation. To reduce the computational time, the optimization model is divided into a two-layer chance-constrained optimization model. The computational time is reduced by reducing the dimensions of the decision variables. Simulation results show that this two-layer chance-constrained optimization model can reduce the EEI and safety risks and improve the stability of the process, while the computational time meets the requirements of mineral processing plants.

中文翻译：

不确定变量浓缩脱水过程的两层机会约束优化模型

进料质量和每柜压滤机质量 (FMP) 是浓缩脱水 (TD) 过程中的不确定变量。优化时必须考虑这些不确定变量；否则，能源经济指数（EEI）和安全风险会增加。因此，本文针对不确定变量的TD过程提出了一种两层机会约束优化模型。优化模型是样本平均近似期望值模型（SAA-EVM），场景由蒙特卡罗模拟生成。为了减少计算时间，优化模型分为两层机会约束优化模型。通过减少决策变量的维度来减少计算时间。

更新日期：2021-08-18

点击分享查看原文

点击收藏

阅读更多本刊最新论文