Cement production optimization modeling: A case study BUA plant

Abstract

This paper deals with cement production optimization modeling using Particle Swarm Optimization (PSO) and the results was compared with Genetic Algorithm (GA) and Pattern Search (PS). This optimization modeling took into account mixtures of primary fuel (mineral coal, pet-coke and heavy oil) and its alternative fuel which is agricultural waste (rice husk, sugar waste and ground shell). The optimization simulation models predict the cost benefit to the manufacturer using alternative fuel, environmental impact to world and finally the quality of the cement produced to the end user. Production cost for one ton of cement using PSO ($23 = 4945), GA ($33 = 7095), PS (38.2 = 8170). The oxides in this research work met standard cement specification: Silica Modulus (M.S-2.9), Alumina Modulus (M.A-1.3), Lime Saturation factor (LSF-93.3%). The results show that the cost of cement production can be reduced by 30 to 70% with the use of alternative fuel (Rice husk, Sugar cane waste, ground nut shell) and without greatly affecting the final product.