Scenario-Based Multi-Objective Genetic Algorithm Optimization of Closed Ball-Milling Circuit of Esfordi Phosphate Plant

Abstract

The main goal of this article is to demonstrate an approach based on integration of process simulation and Multi-Objective Genetic Algorithm (MOGA) concepts to solve a real grinding circuit optimization problem by finding the best operating condition under which process objectives can be achieved. Esfordi phosphate plant is located near city of Bafgh at Yazd province of Iran and produces 5 Mt of phosphate annually. The fine particles (nearly −20 µm) in hydrocyclone underflow which contain a high grade of iron are subjected to over grinding. In addition to electrical energy loss, this causes problems in the downstream process, i.e., flotation stage. The main goals of this study were to solve this problem by adjusting operating condition so that (a) hydrocyclone overflow particle size can be increased from 94.2 µm to 100 µm and (b) increase hydrocyclone underflow particle size from 205 to 500 µm. The second process objective will decrease fine particles in hydrocyclone underflow stream. First, plant sampling campaigns were carried out to calibrate ball mill and hydrocyclone models to be used for performing simulation trials. Then, full circuit simulations were done and optimized by MOGA search process to find the best operating condition that produces hydrocyclone overflow and underflow streams with predefined particle sizes simultaneously. The results indicate that there are various solutions that can be recommended for plant testing and performance improvements. The results of plant implementation of one solution for scenario No. 4 showed improved circuit performance and also validated simulator predictions.