Abstract
A huge portion of available minerals and materials are in the form of fine powder that makes their management and utilization a tedious job. Pelletization, a size enlargement technique, is used to tackle aforementioned problems and considered as a combination of two subprocesses; wet or green pelletization and induration. Green pelletization is highly sensitive to the slightest variation in operating conditions. As a result, identification of the impact of varying parameters on the behaviour of the process is a challenging task. In this paper, we employ MLP and SVM, two soft computing methods, to exhibit their applicability in predicting pellet characteristics. The scarcity of training data is addressed by employing genetic algorithm. Results demonstrate the better accuracy of MLP over SVM in forecasting green pellet attributes.
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Mohammad Nadeem, Haider Banka, Venugopal, R. (2016). Comparison of Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in Predicting Green Pellet Characteristics of Manganese Concentrate. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_25
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DOI: https://doi.org/10.1007/978-981-10-0448-3_25
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