Optimal controlled variable selection for individual process units in self optimizing control with MIQP formulations

TL;DR: The focus of this thesis is to devise systematic and good methods to arrive at controlled variables by finding optimal H that minimize the steady state loss of optimality in the presence of both disturbances and implementation errors.

TL;DR: This document summarizes current capabilities, research and operational priorities, and plans for further studies that were established at the 2015 USGS workshop on quantitative hazard assessments of earthquake-triggered landsliding and liquefaction in the Czech Republic.

Abstract: A two-stage refrigeration cycle was modelled and optimized in MATLAB. The optimum was found to be very flat, resulting in small losses from disturbances and implementation errors. The two unconstrained degrees of freedom were used to implement self-optimizing controllers. A subset of five measurements was used for the self-optimizing controller since this gave reasonably small losses. The controllers assured optimal steady-state operation of the refrigeration cycle even when disturbed. Studies of the dynamic responses of the closed-loop system showed relatively large initial deviations from the optimum caused by large time constants for the measurements. An alternative process model with constant temperature differences between the evaporator and the process stream was also investigated. The model was used to show the feasibility of including cost data in the measurements of the self-optimizing controller. It was found that the resulting controllers were able to keep the operation of the refrigeration cycle optimal despite fluctuations in the prices. In both the original and the alternative case it was found that the open-loop responses with constant inputs were almost as good as the closed-loop responses of the self-optimizing controllers. Control is thus not strictly necessary, and a constant input policy may give acceptable losses.

TL;DR: In this paper, a systematic procedure for finding suitable controlled variables based on only steady-state information is presented, where the goal is to find a set of controlled variables which, when kept at constant setpoints, indirectly lead to near-optimal operation with acceptable loss.

TL;DR: Part I of this series presents a unified formulation of the problem of synthesizing control structures for chemical processes that is rigorous and free of engineering heuristics, providing the framework for generalizations and further analytical developments on this important problem.

TL;DR: In this article, the authors summarized some of the important aspects of the steady-state operation, dynamics and control of continuous distillation columns, focusing on two-product columns separating relatively ideal binary mixtures.

TL;DR: The nullspace method is extended to cases with extra measurements by using the extra degrees of freedom to minimize the loss caused by measurement errors and the results are interpreted more generally as deriving linear invariants for quadratic optimization problems.