(251b) Methodology for Technology Selection in RTO Systems | AIChE

(251b) Methodology for Technology Selection in RTO Systems


Chachuat, B. - Presenter, McMaster University
Rodger, E. - Presenter, McMaster University

Optimization in the process industry represents a natural choice for reducing production costs, improving product quality, and meeting safety requirements and environmental regulations. But when the optimal operating conditions are determined based on a process model, the resulting process operation can be highly sensitive to model uncertainty and process disturbances, which can lead to suboptimal or even infeasible operation. A natural approach to combat uncertainty and avoid conservatism is to incorporate measurements in the optimization framework.

Traditionally, the optimization is based on a first-principles model that is updated in real-time using the available measurements [1]. These two-step RTO systems are well-accepted by industrial practitioners, yet the requirements to ensure no offset from the actual plant optimum are virtually impossible to meet, due to the lack of integration between the model-update and optimization steps. In response to this, the modifier-adaptation methodology has been developed recently [2]. Similar to two-step RTO, the available process model is embedded within a nonlinear program (NLP) that is solved at each RTO execution, but the process measurements are now used to update so-called modifiers that are added to the cost and/or constraint functions in the optimization model (rather than the process model itself). This methodology greatly alleviates the requirements for having no offset from the actual plant optimum, yet it comes at the cost of estimating the gradient of the plant outputs from process measurements; this typically makes modifier-adaptation RTO systems more sensitive to noise than their two-step analogs.

In this presentation, an algorithm that combines modifier adaptation RTO with a gradient-update scheme based on Broyden's method is first presented. The convergence properties of this algorithm are analyzed as well as its sensitivity with respect to measurement noise. Because inaccurate/noisy gradient estimates can cause offset and/or large variations from the plant optimum, thereby counteracting the benefits of modifier adaptation, a novel methodology that combines model- and modifier-adaptation strategies is introduced next. In this approach, the available measurements are used to update a mixed subset of model parameters and modifiers, which are selected so as to minimize the lost profit due to offset and variance of the RTO system predictions [3]. These developments are illustrated via numerical simulation for a selection of benchmark problems.


[1] Marlin T. E. and Hrymak A. N., "Real-time operations optimization of continuous process," AIChE Symp. Ser.316:156-164, 1997

[2] Marchetti, A., Chachuat, B., and Bonvin, D., "Modifier-adaptation methodology for real-time optimization," Ind. & Eng. Chem. Res. (in press, DOI:10.1021/ie801352x), 2009

[3] Forbes, J. F., and Marlin, T. E., "Design cost: A systematic approach to technology selection for model-based real-time optimization systems," Comp. & Chem. Eng.20(6/7):717-734, 1996.