Dynamics and Control of Fluidized Catalytic Crackers. 3. Designing the Control System:  Choice of Manipulated and Measured Variables for Partial Control

Abstract

In this paper is presented an evaluation of different control structures for a fluidized catalytic cracker. A systematic methodology for evaluating such structures was developed which is suitable for complex nonlinear processes in which the number of manipulated variables is smaller than the number of variables that are part of the specifications. It is shown that the choice of the control structure and the variables entering it is far more important than that of the multivariable algorithms to be used. Criteria for the choice of the measured and manipulated variables entering the dynamic structure are presented. The role of additional so-called slow variables in steady state control is discussed and demonstrated by examples. It is shown that linear algorithms are sufficient for dynamic control but nonlinear considerations dominate the choice of the control structure. This paper is one of the first that thoroughly discusses the compromises and choices a designer faces in designing a control system for a complex nonlinear system. Areas in need of further research are outlined.

Self-optimizing control: the missing link between steady-state optimization and control

“…The work may at first seem unrelated but if one translates the words and notation, then one realizes that Shinnar's ideas are close to the ideas presented in this paper and in Morari et al 1980). The similar later paper by Arbel, Rinard and Shinnar (1996) extended the FCC case study, and introduced the concepts of 'dominant variables' and 'partial control'. Tyreus (1999) provides some interesting ideas on how to select dominant variables.…”

Fault Detection and Diagnosis in Chemical Processes Using Sparse Principal Component Selection

“…But mℝMℝ can only solve the supervised learning problems which have classification labels. Arbel et al (1996) suggested the process variables that are preponderant in achieving specific objectives should be selected. Jiang et al (2013) proposed the sensitive principal component analysis for fault detection and diagnosis in chemical processes.…”

Comparison of model predictive control strategies for a fluidized catalytic cracker

“…In our case, we did not find multiple steady states. Because of its economic importance and the control difficulties encoutered due to its multivariable characteristics, large interactions, important nonlinearities and the presence of constraints, FCCs have been the subject of many control studies such as controllability analysis and control structure [20,21], [12], [22], [23], model predictive control [24,25,26,5,27], nonlinear model predictive control [4,28], PI observers [29]. Different control configurations are studied by these authors with various choices for inputs, outputs and their respective numbers.…”