An interpretable fuzzy logic based data-driven model for the twin screw granulation process
An interpretable fuzzy logic based data-driven model for the twin screw granulation process
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DOI：
10.1016/j.powtec.2020.01.052
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发表时间：
2020-03-15
影响因子：
5.2
通讯作者：
Salman, Agba D.
中科院分区：
文献类型：
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作者：
AlAlaween, Wafa' H.;Khorsheed, Bilal;Salman, Agba D.
本研究提出了一种基于模糊逻辑的双螺杆造粒过程建模框架。首先，开发了具有不同结构的各种模糊逻辑系统(FLSS)来定义各种规则库。利用奇异值分解-QR分解(SVD-QR)方法对提取的模糊规则进行评估，并相应地将其约简为单一规则库。然后，通过简单易懂的IF-THEN规则，实现了简化的模糊逻辑集来从数学和语言上描述TSG过程。语言输出提供了一个可访问的框架，以增加对工业背景下这一复杂过程的理解。在实验室规模的实验中得到了验证，结果表明，新提出的模型成功地预测了颗粒尺寸，并加深了对TSG过程的理解。此外，该框架的性能优于标准FLS和人工神经网络(ANN)，在R-2上的总体性能分别提高了约16%和29%。(C)2020爱思唯尔B.V.保留所有权利。
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 R-2, respectively. (C) 2020 Elsevier B.V. All rights reserved.