Evaluation of ANN, ICA-ANN and PSO-ANN predicting ability in the prediction of CO2 emissions during the calcination of cement raw material
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Date
2021-05-25
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University of Oum El Bouaghi
Abstract
Cement industry releases large amounts of carbon dioxide CO2 as by-product to the atmosphere during the calcination of cement raw material. In fact, the calcination is a complex process and not completely understood. The amount of CO2 emitted varies with the grain size, chemical composition, burning temperature and time to pass through the kiln during calcination process. However, due to interaction of several parameters, it is not easy to establish accurate mathematic model to calculate the real amount of CO2 emission. Moreover, using the laboratory experiments to determine the amount of CO2 emissions are not usually easy, time-consuming, expensive and require good quality of reagents and equipments.
To overcome the above problems, artificial neural network (ANN), ANN optimised by imperialist competitive algorithm (ICAANN), ANN optimised by particle swarm optimization (PSOANN)
are applied to predict amount of CO2 emissions.
A comparative accuracy of these tools is evaluated based on the coefficient of determination R2, R2 adjusted, mean absolute percentage error (MAPE) and scatter index (SI).
The results obtained are promising and demonstrate that all proposed tools represent a good alternative for the prediction of CO2 emission with adequate accuracy. PSO and ICA are capable to improve the predicting accuracy of ANN. In addition, PSO-ANN can predict slightly better than ICA-ANN. Based on testing data, the results obtained show that 98.61%, 98.18% and 97.5% of experimental data are explained by PSO-ANN, ICAANN and ANN, respectively with average relative error less than 1.41%and SI less than 0.1.
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Keywords
CO2 emissions; calcination process; artificial neural network; imperialist competitive algorithm; particle swarm optimization