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2017 | OriginalPaper | Chapter

Optimal Operation of an Evaporation Process

Authors : J. L. Pitarch, C. G. Palacín, A. Merino, C. de Prada

Published in: Modeling, Simulation and Optimization of Complex Processes HPSC 2015

Publisher: Springer International Publishing

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Abstract

Real-time optimization (RTO) aims to drive a process to its best efficient steady-state operation point. However, these optimizations often disregard long-term effects such as fouling, catalyst deactivation, etc., which leads to degraded operation or even equipment damage. This work deals with these issues in a multiple-effect evaporation process, with the goal of reducing the specific steam consumption. The proposed approach considers: a grey-box model of the process, data reconciliation to update the model, and RTO to search for the best operating point. The fouling effects arising in the heat exchangers are modeled as a function of the operation time and control decisions, and such model is integrated in the RTO. Moreover, fouling forces periodic stops for cleaning, with their corresponding associated costs. So, an economic optimization is proposed to predict the right day to perform maintenance tasks. Modern nonlinear-programming environments which include automatic differentiation have been used for implementation.

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Footnotes
1
Henceforth average is a mean value between the ones of all inlet and outlet flows in the equipment. Note that these values do not necessary correspond to any actual one, i.e., they’re just representative values of the mixture in each equipment, which is spatially-dependent and unmeasured.
 
2
Actual chemicals in the product are subject to confidentiality, so densities have been omitted.
 
3
This type of robust estimator is chosen by being convex and simple to tune, allowing the user to balance the insensitivity to outliers [14].
 
4
Obtained by classical least-squares error minimization.
 
5
A basic control layer which guarantees the stability of the plant within the operation range is assumed to be in place.
 
6
Note that (16) is a monotonous function in 0 ≤ c ≤ 1, so its minimum is located in an extreme. So, this problem can be handled by NLP software without the need of mixed-integer programming.
 
7
Indeed, the REI 2 is convex for T f > 0, and so is (16). Therefore (16) has a global minimum. See for example the evolution for c = 1 in Fig. 5c.
 
8
This consideration is done for simplicity, as inputs will be updated periodically by definition of RTO. Nevertheless, time-varying and/or stochastic predictions can be incorporated to improve accuracy/robustness of initial decisions.
 
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Metadata
Title
Optimal Operation of an Evaporation Process
Authors
J. L. Pitarch
C. G. Palacín
A. Merino
C. de Prada
Copyright Year
2017
Book
Modeling, Simulation and Optimization of Complex Processes HPSC 2015

Print ISBN: 978-3-319-67167-3

Electronic ISBN: 978-3-319-67168-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67168-0_16

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