Applying Artificial intelligence techniques for predicting amount of CO2 emissions from calcined cement raw materials

This paper aims to predict the amount of carbon dioxide CO2 emissions from raw material used in cement clinker production during calcination process. The amount of CO2 emissions is mainly from the decarbonisation thermal process that is directly related to chemical composition, distribution of particle size and time exposed at high temperature. These influencing factors interact with each other, making the calculation of the amount of CO2 emissions with conventional techniques more difficult. For this reason, several artificial intelligence techniques are applied to predict the amount of CO2 emissions. The key advantage of the proposed techniques is its ability to learn and to generalise without any prior knowledge of an explicit relationship between target and its influencing parameters. The intelligence techniques applied are deep neural network (DNN), artificial neural networks (ANN) optimised using ant colony optimization (ACO-ANN) and genetic algorithm (GA-ANN). The results obtained are promising and show that all intelligence techniques can provide excellent accuracy with high R2 and low error. DNN predicts the amount of CO2 emissions very accurately when comparing to other techniques. Overall, the performance accuracy of ACO-ANN technique is higher than the GA-ANN. According to R2 values, there are more than 99%, 98.5% and 98% of experimental data in testing phases can be explained by DNN, ACO-ANN and GA-ANN respectively with average relative error less than 1.04%. As conclusion, all intelligence techniques can be employed as an excellent alternative to predict the amount of CO2 emissions.

• CO2 emissions, calcination process, deep neural network, artificial neural networks, ant colony optimization, genetic algorithm

• CO2 emissions, calcination process, deep neural network, artificial neural networks, ant colony optimization, genetic algorithm