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Using Intelligent Models to Predict Weight Loss of Raw Materials During Cement Clinker Production

Received: 16 October 2019 Accepted: 2 December 2019 The weight loss of raw materials during cement clinker production is often used as an indicator of final product quality. The raw materials are usually limestone mixed with a sand, clay and iron ore. The weight loss is influenced by essentials parameters such as the correct composition, particle size, temperature and duration of burning of the raw materials. It is difficult to determine experimentally the weight loss with high accuracy due to the interaction of its several parameters. Moreover, the determination of the weight loss is expensive, time-consuming, risk associated. Consequently, various intelligent models such as artificial neural network optimized by genetic algorithm (GA-ANN), regression tree ensembles (RTE), least squares support vector machines (LS-SVM), adaptive neuro-fuzzy inference system (ANFIS) are proposed in the present paper to predict the weight loss. The performance of these models is also compared. The results show that all models have great ability as feasible tools and as good alternatives to predict the weight loss quickly, efficiently and less expensive compared to experiment measurements. According to the values of adjusted R there are 99.31%, 99.06%, 98.01% and 97.17% of data can explained by GA-ANN, RTE, LS-SVM, ANFIS respectively with error less than 3.1%.