AI/ML in Chemical Engineering: From molecular design to industrial process optimization
TL;DR
This comprehensive narrative review synthesizes recent advances in AI/ML methodologies – including graph neural networks, physics-informed neural networks, deep reinforcement learning, and generative artificial intelligence – and critically evaluates their applications spanning molecular property prediction, catalyst design, pharmaceutical development, reactor optimization, process control, and sustainability initiatives.
Abstract
The integration of artificial intelligence and machine learning into chemical engineering represents a paradigm transformation that fundamentally reconceptualizes practice across molecular, process, and industrial scales. This comprehensive narrative review synthesizes recent advances in AI/ML methodologies, including graph neural networks, physics-informed neural networks, deep reinforcement learning, and generative artificial intelligence – and critically evaluates their applications spanning molecular property prediction, catalyst design, pharmaceutical development, reactor optimization, process control, and sustainability initiatives. Contemporary AI/ML approaches demonstrate unprecedented capabilities in navigating complex multiscale phenomena while maintaining computational tractability and physical interpretability. Landmark achievements include 71% reduction in experimental iterations for reaction optimization through deep reinforcement learning, 98% accuracy in predictive maintenance using LSTM-based fault detection, sub-1% prediction errors in virtual metrology for semiconductor manufacturing, and substantial improvements incarbon capture efficiency through machine learning-guided materials discovery. Physics-informed neural networks address the critical challenge of plant-model mismatch by synergistically integrating mechanistic knowledge with data-driven learning, enabling extrapolation beyond training domains while respecting conservation laws. Explainable AI techniques, particularly SHAP analysis, enhance operational acceptance by 52% in safety-critical applications through transparent decision-making pathways. Despite remarkable progress, persistent challenges remain in data quality and standardization, model interpretability for regulatory compliance, computational scalability for real-time control, and integration with legacy industrial infrastructure. The review identifies transformative future directions including multi-modal learning frameworks, transfer learning for data-scarce applications, quantum machine learning for molecular design, and human-AI collaborative systems. Successful deployment demands interdisciplinary collaboration uniting chemical engineering domain expertise with computational intelligence, guided by principles of transparency, reproducibility, and responsible innovation to address sustainability imperatives while maintaining operational excellence and safety in chemical manufacturing.