Assessing the reliability of different real‐time optimization methodologies

Abstract

There is not a consensus about the benefits of implementing Real‐Time Optimization (RTO) technologies to increase the profit of process plants. A lack of experimental and theoretical works which evaluate the scope and limitations of different RTO approaches makes it more difficult to have a sensible opinion about this topic. Most works available in the open literature that study different RTO approaches use few (often one) operation conditions to draw general conclusions about the virtues of a particular methodology. In the present work, we compare the performance of the classical two‐step method with more recently proposed derivative‐based methods (modifier adaptation, Integrated System Optimization Parameter Estimation (ISOPE), and an algorithm based on the Sufficient Conditions of Feasibility and Optimality (SCFO)) under different measurement noise, model mismatch, and disturbance using a Monte Carlo methodology. The results show that the classical RTO method can be reasonably reliable if provided with a model flexible enough to mimic the local process topology, a parameter estimation method suitable for handling measurement noise characteristics, and a method to improve the sample information quality. Implementing a derivative‐based RTO method, in cases of evident model mismatch, should be considered only if the gap between the predicted and the real optimum is large enough and the level of measurement noise is low.

A multi-agent software system for real-time optimization of chemical plants.

“…Described steps and the flow that links them are pictured using an Unified Modeling Language (UML) [29] The absence of structural plant-model mismatch is not guaranteed with the use of highfidelity plant model. Incomplete plant information and measurements noise are important sources of uncertainty in the updated parameters, increasing the plant-model mismatch [27]. Under large structural plant-model mismatch and small excitation in the operation conditions, the MPA method cannot guarantee convergence to the true plant optimum [30].…”

“…This methodology integrates the parameter estimation and the optimization steps. ISOPE optimizes a modified economic function, adding a modifier term coming from the parameter estimation step that allows a first-order correction [27]. The idea is to complement the measurements used in the MPA method with plant derivative information whenever it can be calculated accurately.…”

“…The ISOPE method uses the derivatives of output respect to inputs; also, parameters are updated during ISOPE iterations while MA uses a fixed parameters set during optimization. The main limitation for industrial applications of MA is that the scheme needs an accurate plant gradient available in order to calculate the real plant optimum in the presence of plant-model mismatch [27].…”

“…Concepts of approximately active constraints and Lipschitz continuity are also combined and used to ensure the constraint feasibilities at each iteration [27].…”

“…Assumptions like the knowledge of global Lipschitz constants, global quadratic upper bounds and the exact value of restrictions at current iteration, are very difficult to meet in practical applications [27]. The lack of accurate real process derivatives is also an issue in practice.…”