Performance Optimization of a Coal Preparation Plant Using Genetic Algorithms
A coal preparation plant typically has multiple cleaning circuits based on size of coal particles. The traditional way of optimizing the plant output and meeting the product constraints such as ash, sulfur and moisture content is to equalize the average product quality from each circuit. The present study includes multiple incremental product quality approach to optimize the clean coal recovery while satisfying the product constraints. The plant output was optimized at the given constraints of 7.5% ash and 1.3% sulfur. It was observed that utilizing incremental product quality process gives 2.13% higher yield which can generate additional revenue of $4,260,000 per annum than that obtained by using the equal average product quality approach in this particular case. This paper introduces a novel approach for optimizing plant output using Genetic Algorithms (GA) while satisfying the multiple quality constraints. The same plant product constraints were used for GA based analysis. The results showed that using GA as an optimization process gives 2.23% higher yield that will result in additional revenue generation of $4,460,000 per annum than average product quality approach. The GA serves as an alternative process to optimize the coal processing plant yield with multiple quality constraints.
ASME 2004 International Mechanical Engineering Congress and Exposition
Gupta, Vishal; Mohanty, Manoj; Mahajan, Ajay; and Biswal, Surendra K., "Performance Optimization of a Coal Preparation Plant Using Genetic Algorithms" (2004). Mechanical Engineering Faculty Research. 448.