Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 70, Issue -, Pages 133-148Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2014.04.013
Keywords
Advanced process control; Differential algebraic equations; Model predictive control; Dynamic parameter estimation; Data reconciliation; Dynamic optimization
Funding
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1161036] Funding Source: National Science Foundation
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This paper describes nonlinear methods in model building, dynamic data reconciliation, and dynamic optimization that are inspired by researchers and motivated by industrial applications. Anew formulation of the l(1)-norm objective with a dead-band for estimation and control is presented. The dead-band in the objective is desirable for noise rejection, minimizing unnecessary parameter adjustments and movement of manipulated variables. As a motivating example, a small and well-known nonlinear multivariable level control problem is detailed that has a number of common characteristics to larger controllers seen in practice. The methods are also demonstrated on larger problems to reveal algorithmic scaling with sparse methods. The implementation details reveal capabilities of employing nonlinear methods in dynamic applications with example code in both MATLAB and Python programming languages. (C) 2014 Elsevier Ltd. All rights reserved.
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