Parametric Optimization of Energy Characteristics of Agricultural Waste Briquettes

The crops generate tons of agricultural residues with high calorific values. Due to the problem of transportation and unavailability of the facilities to make some useful end products out of the crop residues, the farmers are forced to burn them in the field enhancing the carbon footprint. As a solution to this problem, briquettes were developed using pulverized coal and finely shredded crop residues in this work. Present work involves in evaluation of the optimum mixing ratio of pulverized coal and corn cobs for developing briquette samples with high calorific value. Similarly, the mixing ratio has also been approximated using Genetic Algorithm to minimize the ash content. The regression models with high accuracy have been developed considering calorific value and ash content as responses respectively, following multi-linear regression which is a supervised Machine Learning algorithm. The optimum mixture containing 10% coal and 90% corn cobs in manufacturing the briquettes was determined using Genetic Algorithm to maximize the calorific value. The optimum mixture to minimize the ash content was approximated as 36% coal and 81% corn cobs. Additionally, the samples were prepared using the optimum mixtures for the confirmation runs. The data of the confirmation runs converged with the data obtained from Genetic Algorithm.