Hybrid Plant Models Call for a Different Plant Modelling Paradigm and a New Generation of Software (Heresy in the land of moles, fractions, & rigorous physical properties)

TL;DR

This paper advocates for a new plant modeling paradigm in process systems engineering, emphasizing mass-based models and hybrid data-driven approaches to improve convergence and consistency across different abstraction levels.

Contribution

It introduces a novel paradigm shift towards mass-based plant models, enabling inheritance across models and simplifying calculations by avoiding mole fraction-based flash calculations.

Findings

Mass-based models improve convergence and accuracy.

Hybrid data-driven node models fit naturally in the new paradigm.

Inheritance of solutions across abstraction levels enhances consistency.

Abstract

This paper is an invitation to the process systems engineering community to change the paradigm for process plants. The goal is to achieve much easier convergence while retaining accuracy on par with the rigorous models. Accurate plant models of existing plants can be linear or much less nonlinear if they are based on mass component flows and stream properties per unit mass properties instead of molar flows and mole fractions. Accurate stream properties per unit mass can be calculated at stream specific conditions by linear approximations which in many instances eliminates mole fraction-based flash calculations. Hybrid data-driven node models fit naturally in this paradigm, since they used measured data, which is either in mass or in volumetric units, but never in moles. Instantiation of models at all levels of abstraction (planning, scheduling, optimization, and control models) from the same plant topology representation will ensure inheritance of solutions from mass-only to mass-and-energy to mass-and-energy-and-stream-properties, thereby ensuring consistency of solutions between these models. None of the existing software provides inheritance between different levels of plant abstraction (i.e. inheritance between models for different business applications) or different levels of abstractions per plant sections or per time periods, which motivates this exposition.