Towards the Grand Unification of Process Design, Scheduling, and Control—Utopia or Reality?

Abstract

As a founder of the Process Systems Engineering (PSE) discipline, Professor Roger W.H. Sargent had set ambitious goals for a systematic new generation of a process design paradigm based on optimization techniques with the consideration of future uncertainties and operational decisions. In this paper, we present a historical perspective on the milestones in model-based design optimization techniques and the developed tools to solve the resulting complex problems. We examine the progress spanning more than five decades, from the early flexibility analysis and optimal process design under uncertainty to more recent developments on the simultaneous consideration of process design, scheduling, and control. This formidable target towards the grand unification poses unique challenges due to multiple time scales and conflicting objectives. Here, we review the recent progress and propose future research directions.

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

“…It is therefore desirable that the aspects of controllability and observability are already considered in the process design phase [45,46]. For this purpose, the corresponding systems theory concepts must, on the one hand, be made applicable to the complexity of the nonlinear process models of pharmaceutical process engineering, and suitable metrics must be defined for integration into the model-based process design [47,48]. In the literature, current work is already pointing in the right direction, but the influence of process nonlinearities and process uncertainties has not yet been fully taken into account; see [48] and references therein.…”

Integrated process design, scheduling, and model predictive control of batch processes with closed‐loop implementation

“…11 Model based approaches that integrate scheduling decisions with faster time scale decisions are shown to be promising to account for the dynamic characteristics of the process. [12][13][14][15][16][17] Bhatia and Biegler 18 have proposed one of the first significant contributions to simultaneously address the process design, scheduling, and optimal control of a multipurpose batch process in a dynamic optimization formulation.…”

A trust‐region framework for integration of design and control

“…Nevertheless, a large and growing body of the current literature in this field has proposed novel methods that have attempted to resolve some unanswered and challenging questions in the field. A few reviews have classified the approaches for simultaneous design and control into five main categories: controllability index‐based approaches, dynamic optimization, robust‐based approaches, embedded control optimizations, and black‐box optimization approaches …”

“…A few reviews have classified the approaches for simultaneous design and control into five main categories: controllability index-based approaches, dynamic optimization, robust-based approaches, embedded control optimizations, and black-box optimization approaches. [4][5][6][7][8][9] One of the primitive resolutions for the integration of design and control is a multiobjective framework that can be applied to consider the trade-off between the conflicting goals of the process. As such, relative gain array, structural singular value, integral of squared error, resilience index, and condition number have been used as metrics in terms of objectives or constraints.…”