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Causality in Process Systems Engineering: Fundamentals, Applications, and Emerging Trends

delete2025-08-12
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PRE
AI
R
Rodrigo Paredes
M
Marco S. Reis *
DOI：10.1016/j.compchemeng.2025.109345delete
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Abstract

Abstract

En 中文
The increasing availability of high-dimensional data from chemical and industrial processes has enabled the widespread adoption of machine learning and deep learning methods. However, their black-box nature raises critical concerns about reliability, ethics, and security in safety-critical industrial applications, highlighting the need for Explainable Artificial Intelligence (XAI) solutions. In this context, Causality analysis emerges as a foundational approach within XAI, moving beyond correlations to uncover genuine cause-and-effect relationships that are essential for reliable decision-making.
Keywords:
machine learning
deep learning
Explainable Artificial Intelligence
causality analysis
safety-critical applications
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Journal

C
Computers and Chemical Engineering
IF:
3.9
Papers:
8.1K
Citations:
1.7W

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