A two‐layer chance‐constrained optimization model for a thickening‐dewatering process with uncertain variables. (27th September 2021)

• Record Type: Journal Article
• Authors: Zhang, Hualu; Wang, Fuli; Li, Kang; Zou, Guobin; Zhao, Luping
• Is Part Of: Canadian journal of chemical engineering. Volume 100:Number 10(2022)
• Page Start: 2894
• Page End: 2906
• Publication Date: 2021-09-27
• Subjects: dewatering process -- two‐layer chance‐constrained optimization -- uncertain optimization; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.24298
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: 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. Abstract : A two‐layer chance‐constrained optimization model for the thickening‐dewatering process is proposed considering the uncertainty of feed mass and the filter‐press per cabinet. The optimization model is a sample average approximate‐expected value model. To reduce the computational time, the optimization model is divided into process layer and sub‐model layer.