Deep learning in chemical engineering: Strengths, weaknesses, opportunities, and threats

Abstract Deep learning (DL) is revolutionizing chemical engineering by enabling high‐fidelity process modelling, automatic fault detection, and faster molecular discovery. Using artificial neural networks (ANNs), DL provides predictive capabilities that support automation, real‐time monitoring, and better decision‐making. It excels at capturing non‐linear relationships in chemical reactions, improving energy efficiency and advancing molecular design. This review applies a strength, weaknesses, opportunities, and threats (SWOT) analysis to assess DL adoption. Strengths include superior predictive performance on complex non‐linear systems compared to traditional models. Weaknesses are its black‐box nature (limited interpretability), high computational cost, and scarcity of high‐quality labelled data. These limitations create opportunities for hybrid models (e.g., physics‐informed neural networks), autonomous digital twins (DT), and green chemistry optimization. Threats involve cybersecurity risks in cloud‐connected plants and regulatory hurdles for safety certification. The review proposes a roadmap stressing explainable AI (XAI), data benchmarks, and strong academia‐industry partnerships. Addressing interpretability and security is crucial to evolve DL from empirical algorithms into trusted tools for safe, sustainable, and autonomous chemical manufacturing.