A two‐step coordinated optimization model for a dewatering process. (21st October 2020)

• Record Type: Journal Article
• Authors: Zhang, Hualu; Wang, Fuli; He, Dakuo; Zhao, Luping
• Is Part Of: Canadian journal of chemical engineering. Volume 99:Number 3(2021)
• Page Start: 742
• Page End: 754
• Publication Date: 2020-10-21
• Subjects: predictive model -- rolling horizon optimization -- thickener; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.23886
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: In actual production processes, the feed mass of a dewatering process is uncertain and a future production state cannot be predicted. This results in improper operation, a substandard production index, and a high energy economic index (EEI). To solve these problems, the authors propose a two‐step coordinated optimization model for the dewatering process based on production data. The prediction model of the dewatering process is first established using the data accumulated during production. A two‐step optimization model is then established to solve the problems existing in the dewatering process. The objective of the optimization is to minimize the EEI in the dewatering process, and the constraints are the ladder electricity price, operation safety, and production index. The genetic algorithm (GA) and gravitational search algorithm‐genetic algorithm (GSA‐GA) are used to solve the two‐step coordinated optimization model, and the computational time can meet the application demand. An offline simulation and a field application showed that the optimization model can be used to improve the production index and reduce the EEI, loss due to the filter cloth, and the frequency of abnormal production.