Control structure selection based on economics: Generalization of the back‐off methodology

Abstract

The back-off methodology has been extensively developed and refined in the last 20 years and offers a systematic tool for solving the simultaneous design and control problem. Previous work has been based on linear process and control models that ensure quick determination of the optimal solution at the expense of potential loss in the accuracy due to nonlinear process characteristics. In this work a new formulation is proposed where use is made of a nonlinear process model that ensures improved accuracy and also offers an improved opportunity for the simultaneous consideration of process design and process control. Two case studies are studied in detail and demonstrate the advantages of the new formulation.

A Systematic Methodology for Comprehensive Economic Assessment of Process Control Structures

“…As such, this step will search and record information such as the value of product leaving the unit operation, cost of raw materials arriving at the unit operation, and cost of utilities. To this end the following information sources can be used:  A rigorous mathematical approach such as mixed integer quadratic programming as discussed by Braccia et al 15 , or the use of mixed integer linear programing or nonlinear programming as demonstrated in Gutierrez et al 16 , or the "back-off" concepts outlined in Zumoffen et al, Kookos and Perkins and Psaltis et al respectively [17][18][19] .…”

A trust‐region framework for integration of design and control

“…Back‐off terms are used to determine the required modifications to move the operating point away from the active constraints into the feasible region obtained from a joint optimization‐flexibility problem . Kookos et al imposed back‐off terms on constraints to generate tighter upper and lower bounds for simultaneous consideration of design and control. Recently, we have developed a back‐off formulation using PSEs .…”

“…Several studies have addressed the integration of design and control using MINLP techniques. [37][38][39][40][41] An in-depth analysis of the challenges in the field of simultaneous design and control can be found elsewhere. 1 As mentioned above, simultaneous design and control mostly results in nonlinear programming (NLP), MINLP, or MIDO formulations.…”

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

“…The most basic method of producing robust solutions is through the use of overdesign factors, which provide a buffer to prevent variability from impacting feasibility. [33][34][35] Furthermore, stochastic simulations can be performed using a given probability distribution, to improve the calculation of back-off terms. 24 More advanced methods are emerging with the potential to provide higher quality solutions at the cost of higher computational complexity.…”

“…28 Advances in computational power have enabled the implementation of more challenging optimization formulations that explicitly consider the complete non-linear plant model. [33][34][35] Furthermore, stochastic simulations can be performed using a given probability distribution, to improve the calculation of back-off terms. 36,37 Although each of the aforementioned studies makes use of back-off terms in their optimization problems, the algorithms and approximations are significantly different.…”