Artificial intelligence reshapes chemical engineering: from reaction design to smart manufacturing

Artificial intelligence has rapidly emerged as a transformative force across chemical engineering. From academia to industry, chemical engineers are leveraging data‑driven models and advanced algorithms to optimize production, enhance safety, design novel processes and materials, and generate new scientific insights. A review published in ENG. Chem. Eng. surveys these developments, illuminating how AI is driving the industry toward smarter manufacturing and greener production.

Reaction engineering and process optimization. AI‑driven models learn from experimental or plant data to predict reaction outcomes and optimal conditions. Reinforcement learning controllers handle nonlinear, multivariable dynamics, enabling self‑optimizing reactors that adjust inputs continuously. Digital twins—virtual replicas synchronized by real‑time data—integrate mechanistic models with data‑driven surrogates. AI‑augmented digital twins analyze sensor data to forecast process behavior, detect anomalies, and suggest optimal setpoints, significantly improving real‑time optimization, operator training, and asset management.

Molecular design and materials discovery. Machine learning models trained on existing data enable virtual screening of enormous chemical libraries. Foundation models—large neural networks pre‑trained on extensive chemical data sets—can be adapted to specific tasks such as property prediction, synthesis planning, and de novo molecule generation. Generative models (GANs, VAEs, diffusion models, and LLM‑based generators) propose new candidate materials for downstream screening. Closed‑loop autonomous laboratories combine AI‑driven proposals with robotic synthesis and testing, dramatically accelerating R&D cycles. Large language models and natural language processing extract synthesis steps, conditions, and performance metrics from the literature, populating structured databases and knowledge graphs.

Process safety and sustainable operations. AI algorithms continuously analyze streams of process data to detect early warning signs of equipment failure or unsafe conditions, enabling predictive maintenance. LLM‑based pipelines applied to incident reports, operating logs, and safety manuals can surface recurring failure modes and recommend best practices. For sustainability, AI handles multivariable, multi‑objective optimization to reduce energy consumption, emissions, and waste. AI tools evaluate the environmental impact of alternative process routes, optimizing not only for economics but also for environmental performance.

Smart manufacturing and future outlook. The convergence of AI, the Industrial Internet of Things, advanced analytics, and automation is creating production systems that are highly flexible, efficient, and responsive. AI monitors and optimizes processes around the clock. Emerging directions include quantum machine learning for catalysis and AI‑enabled circular economy transitions linking molecular design with life‑cycle assessment.

Challenges and responsible AI. Key challenges remain: data scarcity and quality, trustworthiness (explainable AI, uncertainty quantification, and guardrails), integration with legacy systems, computational costs, and cybersecurity. Responsible AI—spanning safety, ethics, and environmental considerations—is increasingly central to the conversation. Human oversight, domain constraints, and validation against mechanistic models will remain essential.

This work affirms that AI integration is essential for the next leap forward in chemical engineering. By combining intelligent algorithms with human creativity and judgment, the field is shaping a smarter, greener future for chemical manufacturing.