Foundation Models for Process Systems Engineering: General-Purpose Industrial Intelligence for Autonomous Design, Control, and Optimization of Chemical Plants

Foundation models are emerging as a transformative paradigm in Process Systems Engineering (PSE), enabling a shift from traditional equation-driven modeling toward data-driven, general-purpose industrial intelligence. This work explores the conceptual and practical foundations of applying large-scale AI models to the autonomous design, control, and optimization of chemical plants. Unlike conventional approaches that rely on first-principles equations or narrowly trained machine learning models, foundation models are trained on diverse, multimodal datasets that include plant sensor data, historical operating logs, simulation outputs, and process design archives. This enables them to learn transferable representations of industrial processes that generalize across unit operations and even across entire production systems. The integration of foundation models into PSE opens new possibilities for accelerating flowsheet synthesis, improving real-time process control, and enhancing predictive maintenance strategies. These models can serve as surrogate simulators for complex chemical processes, dramatically reducing computational cost while preserving accuracy. Additionally, their ability to capture nonlinear dependencies across interconnected process units allows for improved optimization of energy consumption, yield, and safety constraints. A key advantage of foundation models lies in their adaptability. Once trained at scale, they can be fine-tuned for specific plant configurations or deployed in zero-shot or few-shot settings for new processes. This reduces the dependency on extensive re-engineering traditionally required in chemical plant modeling. Furthermore, when integrated with reinforcement learning and model predictive control frameworks, foundation models enable more autonomous decision-making systems capable of responding dynamically to process disturbances. Despite their promise, challenges remain in interpretability, data quality, and safety assurance, particularly in high-risk industrial environments. Nevertheless, foundation models represent a significant step toward realizing fully autonomous chemical plants, where intelligent systems assist or even replace human decision-making in complex operational tasks. This paper outlines the architecture, data requirements, applications, and future research directions of foundation models in Process Systems Engineering, positioning them as a cornerstone of next-generation industrial AI.

کلیدواژه ها:

Foundation models ، Process Systems Engineering ، Chemical plant optimization ، Industrial AI ، Autonomous process control