Using Hybrid Neural Networks in Scaling up an FCC Model from a Pilot Plant to an Industrial Unit

Chemical Engineering and Processing 42 (2003) 697Á/713 www.elsevier.com/locate/cep Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit G.M. Bollas b, S. Papadokonstadakis a, J. Michalopoulos a, G. Arampatzis a, A.A. Lappas b, I.A. Vasalos b, A. Lygeros a,* a School of Chemical Engineering, National Technical University of Athens, Zografou Campus, Heroon Polytechniou 9, Athens, GR-157 80, Greece b Chemical Process Engineering Research Institute (CPERI), Centre for Research and Technology Hellas (CERTH), P.O. Box 361, 57001 Thermi-Thessaloniki, Greece Received 13 May 2002; received in revised form 30 September 2002; accepted 30 September 2002 Abstract The scaling up of a pilot plant fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Fluid catalytic cracking; Process modeling; Neural networks; Hybrid modeling; Pilot to commercial unit scale up; Riser hydrodynamics; FCC kinetics 1. Introduction They produce a range of products, which must adapt to seasonal, environmental and other changing demand Fluid catalytic cracking (FCC) is an important oil patterns. Since FCC units are capable of converting refinery process, which converts high molecular weight large quantities of heavy feed into valuable lighter oils into lighter hydrocarbon products. It consists of two products, any improvement in design, operation or interconnected gasÁ/solid fluidized bed reactors: the riser control can result in substantial economic benefits. reactor, where almost all the endothermic cracking FCC pilot plant units are often used to develop reactions and coke deposition on the catalyst occur, accurate prediction and optimization models. The and the regenerator reactor, where air is used to burn off reason is that the operation of pilot plant units can be the coke deposited on the catalyst. The heat produced is easily adapted under a wide range of conditions (feed carried from the regenerator to the reactor by the properties, catalysts operating conditions). The main catalyst. Thus, in addition to reactivating the catalyst, difficulty when translating the pilot scale unit observa- the regenerator provides the heat required by the tions to the commercial unit reality is to predict the scale endothermic cracking reactions [1]. Industrial FCC units up effects, arisen from the small geometrical features of are designed to be capable of using a variety of pilot scale units. feedstocks, including straight run distillates, atmo- Furthermore, due to the complexity of the industrial spheric and vacuum residua and vacuum gas oils. FCC units, it is very difficult to obtain accurate models. The complexity arises from the strong interactions * Corresponding author. Tel.: /30-1-7723973; fax: /30-1-7723155. between the operational variables of the reactor and E-mail address: [email protected] (A. Lygeros). the regenerator. Moreover, there is a large degree of 0255-2701/03/$ - see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0255-2701(02)00206-4 698 G.M. Bollas et al. / Chemical Engineering and Processing 42 (2003) 697Á/713 uncertainty in the kinetics of the cracking reactions and less computational time for the training procedures. catalyst deactivation by coke deposition in the riser Another advantage of hybrid models, which arises from reactor and the coke burning process in the regenerator their internal structure, is that they can be much more [2]. easily interpreted and analyzed than simple ‘black box’ Artificial neural networks (ANNs) are a promising neural networks [12,13]. alternative modeling technique. They are mathematical In literature there exist some efforts of hybrid models, which try to simulate the brain’s problem modeling for the purposes of generalized on-line state solving approach. Neural networks have been known estimation [13], and for fed batch bioreactors [12,14], for decades. The beginning of neural network research but the scientific area of scaling up a pilot plant model can be traced back to the 1940s, but until the early 1980s to a commercial unit using hybrid modeling schemes the research was basically theoretical and limited. The seems not to have been explored. tremendous evolution of digital technology over the past In this paper a detailed kineticÁ/hydrodynamic model two decades provided the necessary computational was developed for the simulation of the riser reactor of power in order to use neural networks in various fields. the FCC pilot plant unit, located in Chemical Process Applications of neural networks in chemical engineering Engineering Research Institute (CPERI) in Thessalo- appeared in the late 1980s [3]. Some published applica- niki, Greece [15]. The catalyst hold-up and its residence tions of ANNs in chemical engineering are: fault time in the reactor were calculated via a comprehensive diagnosis in chemical plants [4], dynamic modeling of hydrodynamic scheme and the conversion and coke chemical processes [5], system identification and control yield were successfully predicted through a Blanding [6], sensor data analysis [7], chemical composition type [16] kinetic model [17]. The applicability of the analysis [8], and inferential control [9]. Quite recently ‘pilot plant model’ to the actual operation of the there have been publications of applying neural net- industrial unit of Aspropyrgos Refinery of Hellenic works in FCC modeling [10,11]. Neural networks Petroleum S.A., the largest and most complex oil appear to be suitable for FCC modeling because: (a) refinery in Greece, is examined. Modifications are they can handle non-linear multivariable systems; (b) made to the model from modeling and design perspec- they are tolerant to the faults and noise of industrial tives. A neural model trained with data from the data; (c) they do not require an extensive knowledge commercial unit is combined with this pilot plant model base; and (d) they can be designed and developed easily in various hybrid modeling schemes and compensates [3]. for the differences in unit geometry, feedstock quality Neural networks can also be used as hybrid models. and catalyst properties, between the ideal operation of The term hybrid modeling is used to describe the the pilot plant and the reality of the commercial unit. incorporation of prior knowledge about the process The structure of the paper is as follows: coupling of under consideration in a neural network modeling hydrodynamic and kinetic theories for the FCC Unit is approach. The way that this incorporation can be presented in Section 2. Section 3 provides a description done depends on the form of the prior knowledge of neural networks and of hybrid models using neural available as well as on the desired properties of the networks. In Section 4 the pilot plant model is pre- model to be created. The two main categories are the sented. In Section 5 there is a description of the design approach, in which prior knowledge dictates the development of the hybrid models. Section 6 presents overall model structure and the training approach, the results of the best hybrid models and provides a which dictates the form of the weights estimation comparison with the pilot plant model and a simple problem [12]. This paper handles only the design neural model. Concluding remarks are presented in approach. A successful design hybrid modeling ap- Section 7. proach can lead for example to models with better generalization and extrapolation abilities compared to pure neural network models, especially when there are 2. Coupling of hydrodynamic and kinetic theories for the only a few and noisy data for the training of the neural pilot and the commercial FCC unit models, which is often the case, when it comes to industrial databases. For example, if the prior knowl- A model was developed to study the strong coupling edge is captured in a phenomenological or an empirical between hydrodynamics and chemical reactions that model, then this can lead to a lower dimensionality of occur in commercial and pilot FCC risers. The flow the input vector used by the neural network, since some regime in the reactor determines each phase residence or all of the variables that are used by this model can be time and thereafter the kinetic conversion of the considered redundant for the neural network training. reaction. Thus the reaction kinetics formulation should Consequently, neural networks with fewer weights can be accomplished with a comprehensive hydrodynamic be applied, which is a crucial point when the data are model in order to have accurate simulation of the sparse. Furthermore, smaller neural networks require process. G.M. Bollas et al. / Chemical

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