Abstract
This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area.
Highlights
This article presents a comprehensive overview of the modifier-adaptation strategy for real-time optimization
With Integrated System Optimization and Parameter Estimation (ISOPE), process measurements are incorporated at two levels, namely, the model parameters are updated on the basis of output measurements, and the cost function is modified by the addition of an input-affine term that is based on estimated plant gradients
The approach became known under the acronym ISOPE, which stands for Integrated System Optimization and Parameter Estimation [9,10]
Summary
This article presents a comprehensive overview of the modifier-adaptation strategy for real-time optimization. The model-adequacy conditions are difficult to both achieve and verify This difficulty of converging to the plant optimum motivated the development of a modified two-step approach, referred to as Integrated System Optimization and Parameter Estimation (ISOPE) [7,8,9,10]. With ISOPE, process measurements are incorporated at two levels, namely, the model parameters are updated on the basis of output measurements, and the cost function is modified by the addition of an input-affine term that is based on estimated plant gradients. Note that RTO can rely on a fixed process model if measurement-based adaptation of the cost and constraint functions is implemented This is the philosophy of Constraint Adaptation (CA), wherein the measured plant constraints are used to shift the predicted constraints in the model-based optimization problem, without any modification of the model parameters [12,13].
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