Performance Assessment for Advanced Process Control

Performance Assessment for Advanced Process Control

Thöne, C.M.

Baldi, S. (mentor)

Mechanical, Maritime and Materials Engineering

DCSC

Systems & Control

2015-08-28

Model predictive control (MPC) is a control technique that is frequently used in the process industry. Two advantages of model predictive controllers is their ability to predict the impact of disturbances, and to easily account for constraints. However, a disadvantage of model predictive controllers is that they rely on mathematical models of the controlled processes. Changes in process conditions may cause the model to no longer accurately represent the real process, which in turn may result in a drop in performance. Detecting performance drops and taking subsequent action is thus highly desirable. This thesis aims at assessing current methods that detect and revert these performance drops due to model-plant mismatch. To this end, a benchmark distillation column model representing a typical industrial process was designed. For two different control configurations, models were identified using prediction-error identification, and model-predictive controllers were subsequently designed for reference tracking and rejection of disturbances. Performance of these model-predictive controllers has been compared, and results show that the so called double-ratio configuration is better at disturbance rejection, and shows more robust performance. Further, a performance index that tracks the average variance of the controlled output, computed over a time-range, was developed. A methodology is shown which can be used to compute the optimal time range, when the historic variance, a desired false alarm rate, and threshold is known. In the event a performance drop due to model-plant mismatch is detected, the plant should be re-identified in order to restore nominal performance. This can however be a costly proce- dure. The second part of this thesis therefore focuses on least-costly identification methods. Recently, a new experiment design method was developed that is aimed at minimizing the length of an identification experiment, while constraints on the minimal accuracy of the to-be- identified model and the maximum values of the in- and output signals are honoured Analysis of this new minimal-time algorithm shows that the identification experiment time can be reduced by up to 56 % when compared with conventional least-costly identification methods. Further, Monte-Carlo simulations have been performed for different initializations and it has been shown that the minimal-time algorithm manages to find its global minimum for various initial conditions, although the probability to obtain it from an arbitrary simulation is different from system to system. The last part of the thesis discusses the relevance and applicability of performance monitoring and re-identification in practice. While literature assumes tracking the variance of the controlled output is a good indicator, in reality wrong limits on the control inputs, a sub-optimal economic function in the model-predictive controller, and up-time of the model-predictive controllers are more important factors that determine the economic benefits gained from a model predictive control system.

model predictive control
performance monitoring
process control
Least costly identification

http://resolver.tudelft.nl/uuid:38391497-885e-456e-b5a6-f3d9207533ca

Student theses

master thesis

(c) 2015 Thöne, C.M.