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. Key words: Fuel mixture, energy consumption, cement cost, cement quality.  
Highlights
In cement production energy consumption takes the largest bulk of production cost
This research work presents the possibility of using the mixture of mineral coal, petroleum coke, heavy oil, agricultural waste, etc. as fuel feed stock
The parameters of Particle Swarm Optimization (PSO), GE and Pattern Search (PS) used a population of 100 particles; C1 = C2 = 2.0; initial weighted of 0.9 with linear decline up to 0.3; search space of the variables to be optimized in the interval 0 < Xn < 3, where n=1,.., 10
Summary
In cement production energy consumption takes the largest bulk of production cost. Due to this impact the cement manufacturers are always concerned about using alternative fuel mixture with low production cost without losing the quality of the final product and less environmental impact to the society. The process consists basically the replacement of the primary fuels by residues generated by other industries such as used tires, waste oils and other industrial wastes, agricultural waste, municipality waste, among others (Kleppinger, 1993). This mixture is intended for a rotary kiln, clinker production; mainly dry process with a pre-heater and calciner. The optimization procedure will take into account process restrictions such as specific heat consumption, cement quality and environmental impact
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