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AI-Driven Transformation in Chemical Engineering

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The integration of artificial intelligence (AI) into chemical engineering represents a fundamental shift from traditional, heuristic-based design to a data-centric, autonomous paradigm. This transformation is currently the primary driver for achieving deep decarbonisation, operational excellence, and molecular-level innovation. 1. Core Domains of AI Integration AI accelerates chemical engineering by augmenting traditional thermodynamic modelling with predictive data analytics. A. Molecular Discovery and Materials Science Using generative AI and Graph Neural Networks (GNNs), researchers can now screen billions of potential molecules for specific properties (e.g., carbon capture efficiency or catalyst selectivity) in seconds. Inverse Molecular Design: Instead of testing molecules, AI defines the target property and generates a valid chemical structure to match it. Catalyst Optimisation: Machine Learning (ML) models predict surface binding energies, reducing the need for expensive ...
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