A hybrid mathematical model for a three-phase industrial hydrogenation reactor

Santana, Pedro Leite de; Toledo, Eduardo Coselli Vasco de; Meleiro, Luiz Augusto da Cruz; Scheffer, Rafael da Cunha; Freitas, Benata Barbosa Heinz de; Maciel, Maria Regina Wolf; Filho, Rubens Maciel

doi:10.1016/s1570-7946(01)80042-0

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The extreme pressure and temperature conditions and the highly efficient stirring in the RAPTOR®, make it necessary to model all these aspects because the reaction is no longer limited by mass transfer. In the literature, we find studies proposing the dynamic modeling and control of catalytic three-phase reactors [26] [35][36][37][38][39] but as far as we know, the problem of intensified continuous mini-reactors modeling has not been addressed.…”

Section: Model Principlementioning

confidence: 99%

Control and optimization of a three-phase catalytic slurry intensified continuous chemical reactor

Bahroun

Jallut

et al. 2010

Journal of Process Control

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Intensified continuous mini-reactors working in high pressure and temperature conditions are particularly effective at coping with mass transfer limitations during three-phase catalytic reactions. They are highly non-linear, multivariable systems and behave differently from conventional batch, fed-batch or continuous non-intensified reactors. In this paper, the optimization and control of this new process are presented using a two-layer approach consisting of a hierarchical control structure with an optimization layer which calculates the set points for an advanced controller. The latter is based on the concavity of the entropy function and the use of thermodynamic availability as a Lyapunov function. The three-phase catalytic o-cresol hydrogenation performed under high pressure and temperature in a small-scale pilot of the RAPTOR® reactor designed by the French company AETGROUP SAS, is taken as a representative test example to illustrate the strategy. The performance of the control structure is illustrated by simulation.

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Section: Model Principlementioning

confidence: 99%

Control and optimization of a three-phase catalytic slurry intensified continuous chemical reactor

Bahroun

Jallut

et al. 2010

Journal of Process Control

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“…The following assumptions are also considered to derive the dynamic model (Bahroun et al, 2009;Santana et al, 2001;Vasco de Toledo et al, 2001): the temperature of the liquid and gas phases are assumed to be equal; a global mass transfer coefficient is used to represent hydrogen transfer from the liquid surface to the bulk. Equilibrium conditions at the gaseliquid interface are assumed; the pressure drop is negligible; the resistances to mass and heat transfer at the catalyst pellet surface and within the pores are lumped into global heat and mass transfer coefficients; the material balance in the gas phase, that is assumed to be pure hydrogen, is written at steady state; the gaseous phase is assumed to be ideal.…”

Section: Dynamic Modelmentioning

confidence: 99%

Dynamic model based safety analysis of a three-phase catalytic slurry intensified continuous reactor

Bahroun

Valentin

et al. 2010

Journal of Loss Prevention in the Process Industries

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“…Thus, to study the effects of n input variables, the number of experiments to be performed is 2 n . According to Santana et al (2001), the most important variable in a three-phase reactor is the liquid component conversion and, thus, its concentration should be the controlled variable. However, as concentration measurements are expensive and introduce significant delay (Jørgensen and Jensen, 1989), usually the temperature is controlled as an indirect method to control concentration.…”

Section: Dynamic Behaviour Studymentioning

confidence: 99%

Non‐Linear Predictive Control of a Three‐Phase Catalytic Reactor

Costa

Filho

2003

Can J Chem Eng

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A t the centre of a Model Predictive Control (MPC) algorithm is a dynamic model. Until recently, most industrial applications have used linear dynamic models. These models were preferred to the non-linear ones due to the difficulty in developing generic non-linear models from plant data and also because of the computational expense involved when non-linear models are used in the MPC formulation (Piché et al., 2000). The rapid development in computer capacity in the last few years, however, has led to the development of a great number of predictive control algorithms using non-linear models.One of the main problems with linear controllers is that they are not robust, i.e., they do not present good performance for different operating regions and with changing process conditions (Dutta and Rhinehart, 1999). According to Piché et al. (2000), MPC based on linear models is acceptable when the process operates at a single set point and the primary use of the controller is for the rejection of disturbances. However, many chemical processes do not operate at a single set point, as they are operated in different conditions depending on market requirements. The operation in different set points also occurs when an optimization layer is introduced in the control structure. The main characteristic of a multilayer optimization structure is that the optimization level defines the operational conditions of the process. Thus, the control system has to be designed to maintain the process operating adequately in different operational conditions. In this case, to properly control the process, a non-linear model is needed.Although the use of non-linear models may improve the control algorithm performance, the development of such models is not always an easy task. It is desirable to develop techniques to obtain reliable models in a simple and rapid way. In recent years, there has been a strong interest in the use of neural networks to describe chemical processes, due to their ability to approximate highly non-linear systems. Different structures of neural networks have been used as non-linear models in advanced control algorithms (Zhan and Ishida, 1997;Rohani et al., 1999;Kambhampati et al., 2000;Meleiro et al., 2001). Hussain (1999) presents a review of neural network applications in process control. They conclude that neural networks are capable of describing the system in a great number of cases. Besides, they are versatile, as they can be incorporated in various well-known non-linear control methods. Seborg (1999) provides a perspective on the current status of advanced process control and concludes that many industrial applications of nonlinear predictive control employ neural networks. In this work, the predictive control of a three-phase catalytic reactor is considered. A predictive control algorithm, which has a non-linear internal model represented by functional link networks, is proposed. This network structure has been shown to have a good non-linear approximation capability, with the advantage that the estimation of its we...

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A hybrid mathematical model for a three-phase industrial hydrogenation reactor

Cited by 5 publications

References 4 publications

Control and optimization of a three-phase catalytic slurry intensified continuous chemical reactor

Control and optimization of a three-phase catalytic slurry intensified continuous chemical reactor

Dynamic model based safety analysis of a three-phase catalytic slurry intensified continuous reactor

Non‐Linear Predictive Control of a Three‐Phase Catalytic Reactor

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