Integrating Artificial Intelligence, Digital Twins, and Advanced Process Control for Sustainable and Efficient Chemical Manufacturing

Abstract

The chemical industry faces unprecedented pressure to enhance operational efficiency while simultaneously reducing environmental impact and meeting stringent regulatory requirements. This article presents a comprehensive framework for integrating Artificial Intelligence (AI), Digital Twins (DT), and Advanced Process Control (APC) to achieve sustainable and efficient chemical manufacturing. The proposed methodology leverages AI-driven predictive models, including neural networks and reinforcement learning, to enable real-time optimization of complex nonlinear processes. Digital twins serve as the central integration platform, providing a continuously evolving virtual representation of physical systems that fuses process data, mechanistic models, and domain knowledge. The framework incorporates a knowledge graph-based semantic architecture that ensures interoperability, scalability, and adherence to FAIR principles for data and model management. Advanced process control strategies, augmented by AI agents, enable autonomous decision-making and adaptive control under varying operational conditions. A case study on a commercial-scale BTX recovery process demonstrates the system's effectiveness, achieving a 28.52% improvement in economic performance, 79.45% reduction in emissions, and 15.96% decrease in carbon footprint while maintaining strict regulatory compliance. The results validate that the synergistic integration of these technologies creates a paradigm shift toward self-optimizing, resilient, and sustainable chemical manufacturing systems.