SMART IT FRAMEWORKS FOR AUTOMATION AND QUALITY CONTROL IN CHEMICAL MANUFACTURING

Abstract

The Industry 4.0, artificial intelligence (AI), and high regulatory standards of quality and data integrity prompt a structural change in the chemical manufacturing sector. The conventional automation architectures that have been based on remote distributed control systems (DCS), programmable logic controllers (PLCs), and laboratory information systems are no longer needed to provide real-time quality assurance, operational agility, and regulatory-compliant data management. This review is based on advances in the field of smart manufacturing, process analytical technology (PAT), soft sensing, predictive quality analytics, digital twins, and multi- agent/service-oriented control architecture with particular attention to its implementation in a Smart IT framework to perform automation and quality control of chemical manufacturing. The paper reviews the current standards and paradigms including Quality by Design (QbD), GAMP 5, ALCOA(+), and real-time release testing (RTRT) after which it evaluates the current IT and control architectures (pyramidal ISA-95 style, SOA, holonic and multi-agent systems) and AI-based quality technologies (predictive quality, computer vision, soft sensors, digital twins). Contributing to this, it suggests a layered Smart IT model that integrates physical properties, connectivity and edge infrastructure, integration services, AI driven intelligence, and governance and compliance into a coherent and closed loop quality and automation structure. The framework puts importance on modularity, service orientation, human-oriented multi-agent coordination, and regulatory-by-design validation of AI components. Lastly, the paper provides implementation roadmaps, illustrative scenarios, and open research directions which incorporate the unified views of the experts in IT, AI, and chemical engineering.