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Artificial Intelligence for Modelling the Wet Agglomeration Process of Fine Materials: A Survey

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Abstract

Industrial plants often report under-capacity production, primarily due to limited knowledge of the dynamics of underlying size enlargement process. Many researchers have used different techniques to capture the nature of agglomeration system. However, there are still plenty of opportunities to model the process using powerful visualization and simulation tools to understand the process better and to improve its efficiency. Since the field of artificial intelligence (AI) has surfaced as a convincing alternative to handle vague, imprecise and complex non-linear systems, methods such as fuzzy logic, self organizing maps, artificial neural networks etc. can be used to model agglomeration process of fine materials. Present study tries to briefly discuss conventional modelling techniques and suggests new possibilities using artificial intelligence perspective to explore the domain of agglomeration. Since the AI-based techniques are often overlooked in the field of wet agglomeration due to lack of its technical knowledge and possible applications, our work presents highlights the effectiveness of these AI methodologies to solve a variety of problems in the said domain. This work will help the researchers to select appropriate AI method for a given agglomeration problem.

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Authors and Affiliations

  1. Department of Computer Science, Aligarh Muslim University, Aligarh, India

    Mohammad Nadeem & Faisal Anwer

  2. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

    Haider Banka

  3. Department of Fuel and Mineral Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

    R. Venugopal

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  1. Mohammad Nadeem
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  2. Haider Banka
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  3. R. Venugopal
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Nadeem, M., Banka, H., Venugopal, R. et al. Artificial Intelligence for Modelling the Wet Agglomeration Process of Fine Materials: A Survey. SN COMPUT. SCI. 3, 467 (2022). https://doi.org/10.1007/s42979-022-01368-7

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