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Comparative Analysis of Machine Learning Models for Predictive Control of the Cyclopentadine Production Process

Samuel V. Saraiva, Flávio V. Silva and Frede O. Carvalho

Abstract

Different control strategies have been investigated to improve nonlinear system operations. One such strategy is the use of nonlinear predictive controllers (NMPCs) based on machine learning models. These models, such as artificial neural networks (NN), support vector machines (SVMs), and neuro-fuzzy networks (NF), present satisfactory adaptability to the complexity of the processes. In this aspect, a comparative study of the models in the predictive control of a complex system, such as MIMO (multiple-input-multiple-output) process of the production process of cyclopentadiene, is of interest and is the aim of this work. In this aspect, we find, through simulations, that the NMPCs presented adequate performance, especially those based on an SVM, concerning the servo and regulatory problem scenarios, keeping the process at the optimum operating point, especially for unattainable setpoint. The instability in the use of the classical proportional-integral-derivative linear control is also shown.

Funding statement: Coordenação de Aperfeiçoamento de Nível Superior – Brasil (CAPES), Grant Number: Finance Code 001.

A Appendices

The tabulated values for the parameters of cyclopentadiene process are shown in Table 5.

Table 5:

Parameters and variables of the studied process.

ParametersValue
Kinetic Reaction Parameters
Arrhenius constant for reaction 1k01 = 1.287.1012 h−1
Arrhenius constant for reaction 2k02 = 1.287.1012 h−1
Arrhenius constant for reaction 3k03 = 9.043.109 l.h−1. mol−1
Activation Energy reaction 1E1/R = 9758.3 K
Activation Energy reaction 2E2/R = 9758.3 K
Activation Energy reaction 3E3/R = 8560 K
Reaction Enthalpy 1h1 = 4.2 kJ.mol−1
Reaction Enthalpy 1h2 = −11 kJ.mol−1
Reaction Enthalpy 1h3 = −41.85 kJ.mol−1
Physico-chemical parameters
Density0.9342 kg.l−1
Reagent heat capacityCpr = 3.01 kJ.kg−1. K−1
Heat capacity of the cooling liquidCpc = 2.0 kJ.kg−1. K−1
Thermal exchange coefficientU = 4032 kJ.h−1. m−2 K−1
Dimensions of the reactor
Thermal exchange areaAr = 0.215 m2
Reactor volumeVr = 10 l
Cooling liquid massmc = 5 kg

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Received: 2019-09-16
Revised: 2019-11-19
Accepted: 2019-11-22
Published Online: 2020-06-03

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