Trends and perspectives in deterministic MINLP optimization for integrated planning, scheduling, control, and design of chemical processes.

Deterministic mixed‑integer nonlinear programming (MINLP) is a core mathematical tool for tackling the complex, multi‑scale problems that arise in chemical process engineering. This research looks at how to bring together four key activities—strategic planning, tactical scheduling, operational control, and physical design—into a single computational optimization framework. In the past, these activities have been treated as separate, hierarchical steps. Trying to optimize them all at once is hard because the problems are non‑convex, involve many variables, and mix discrete decisions with continuous ones. The approach described here uses a review‑driven method to give clear guidance on recent algorithmic advances, the capabilities of deterministic solvers, and decomposition techniques that reduce computational difficulty.

By testing the latest deterministic MINLP algorithms, the method pinpoints the main bottlenecks that slow solver convergence and make formulations grow too large. Implementation work focuses on building models of typical chemical process networks. This lets researchers compare a single, large (“monolithic”) optimization model with smaller, broken‑down (“decomposed”) formulations in a systematic way. Overall, this line of research aims to create strong mathematical formulations that link theoretical MINLP progress with real‑world plant‑wide control needs, laying a solid foundation for future algorithmic work in industrial process systems.

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