Abstract
The chemical engineering (ChE) field is becoming increasingly hybrid digital as new advancements in machine learning (ML) integrate with ChE workflows and industrial plant operations. As data scientists and engineers push artificial intelligence (AI) usage, it is important for the engineering workforce and data science methods to be grounded in ChE fundamentals through methods like science guided machine learning (SGML). SGML is a broad term that includes ML architectures that are informed/embedded with physics, chemistry, and thermodynamics first-principles. This presentation highlights and demonstrates selective SGML advances from 2022 to 2026, including multicomponent phase equilibria, surrogate modeling, uncertainty quantification, and agentic large language models (LLMs). Furthermore, we discuss how each of these advancements accelerates the process design and development workflow, and we demonstrate solving novel process design and separation problems using this workflow, pushing forward the next era of process design through embedded knowledge and reasoning design tools. Next, we discuss the current state, new developments, and future direction of educational programs. We survey different university approaches to implementing data science in ChE education and give our perspective on how these approaches might change with technological advancements. We also show the evolving landscape of advanced ML for improved workforce training. Finally, we propose our perspective on the future of scalable AI implementation into the ChE workforce.