Assessment of the Dyad Agentic Modeling Language in Process Systems Engineering

In this work, Dyad, a modeling and simulation language with specialized multi-agent large language model (LLM) workflows, is introduced for Process Systems Engineering (PSE) applications. Dyad is designed to overcome key limitations of general-purpose LLMs, such as context collapse, retrieval failures, and physically inconsistent hallucinations, by decomposing engineering tasks across coordinated agents equipped with domain-specific tools, optimization routines, and access to mechanistic models. Using crystallization as a testbed, we evaluate Dyad against the main general-purpose agentic AI systems (ChatGPT 5.1 and Gemini 3) on three canonical PSE challenges: (i) soft sensing and system monitoring via an ATR-FTIR calibration problem for paracetamol in ethanol--water mixtures; (ii) dynamic modeling of potassium sulfate crystallization and dissolution through population balance models (PBMs) that Dyad iteratively refines, calibrates, and updates when necessary; and (iii) nonlinear model predictive control (NMPC) for batch crystallization, where Dyad proposes control objectives, constraints, and tuning parameters that achieve near set-point tracking with computation times compatible with real-time operation. Across these case studies, Dyad not only proposes state-of-the-art modeling strategies, but also automatically selects model structures, estimates parameters, and suggests refinements that improve extrapolation, particularly near equilibrium conditions, where it recovers physically consistent steady states while alternative PBMs exhibit nonphysical drift. When compared with general-purpose LLMs (ChatGPT and Google Gemini) prompted on the same tasks, Dyad delivers more accurate models, with coefficients of determination exceeding 0.98 for calibration tasks and reductions in mean absolute percentage error from over 100% to below 50% for crystallization dynamics, as well as implementable NMPC formulations with computation times below 7 s per control move. In contrast, other LLMs remain confined to high-level suggestions or approximate parameter guesses and do not produce calibrated models or controllers suitable for real-time operation. These results position specialized multi-agent LLM workflows as practical, trustworthy assistants for accelerating monitoring, modeling, model update, and control in chemical and process industries.