An interpretable fuzzy logic based data-driven model for the twin screw granulation process

Abstract

In this research, a new framework based on fuzzy logic is proposed to model the twin screw granulation (TSG) process. First, various fuzzy logic systems (FLSs) having different structures are developed to define various rule bases. The extracted fuzzy rules are assessed and reduced accordingly into a single rule base by utilizing the singular value decomposition-QR factorization (SVD-QR) approach. The resulted reduced FLS is, then, implemented to describe the TSG process mathematically and linguistically via simple to understand IF-THEN rules. The linguistic output provides an accessible framework to increase the understanding of this complex process within an industrial context. Validated on laboratory-scale experiments, it is shown that the newly proposed model successfully predicts the granule size and enhances the understanding of the TSG process. Furthermore, the proposed framework outperforms the standard FLS and the Artificial Neural Network (ANN), with an overall improvement of approximately 16% and 29% in R2, respectively.