A Data-Driven Compensation Method for Production Index of Hydrometallurgical Process

Abstract

The process of hydrometallurgical has the characteristics of many sub-processes with complicated reaction mechanism and long process flow. How to keep the hydrometallurgical process running in the state of optimal economic efficiency is the difficulty task. In this paper, a method based on industrial big data is proposed to compensate the production index of the hydrometallurgical process. Based on the current production index, the just-in-time learning (JITL) idea is used to establish the model that describes the relationship between the compensation value and the economic benefit increment. Then, the compensation value of the current production index is calculated, and the result is applied to the production process. The simulation and offline experiment results verify the effectiveness of the proposed method.

Keywords

• Hydrometallurgical
• industrial big data
• optimal compensation
• JITL