Continuous polymerization in tubular reactors with prepolymerization: Analysis using two‐dimensional phenomenological model and hybrid model with neural networks

Nogueira, André Lourenço; Lona, Liliane Maria Ferrareso; Machado, Ricardo Antônio Francisco

doi:10.1002/app.13258

Cited by 10 publications

(7 citation statements)

References 27 publications

(32 reference statements)

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“…In general, CFRs offer reduced fixed and operating costs and improve heat exchange capability [31,32]. Furthermore, they provide an increase in quality of the final product, since the variations that exist between batches are eliminated [33].…”

Section: Continuously Feeding Reactors (Cfrs)mentioning

confidence: 99%

Chromatographic Methods Applied to the Characterization of Bio-Oil from the Pyrolysis of Agro-Industrial Biomasses

Moraes¹,

Tomasini²,

Silva³

et al. 2017

Frontiers in Bioenergy and Biofuels

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Biomass conversion into solid, liquid and gaseous products by pyrolytic technology is one of the most promising alternative to convert the biomass into useful products and energy. The total characterization of the products from the pyrolysis of biomass is one of the great challenges in this field, mainly due to their molecular complexity. Pyrolysis is a process that causes degradation of biomass in a non-oxidative atmosphere, at relatively high temperatures, producing a solid residue rich in carbon and mineral matter, gases and bio-oil. The yield and properties of the products depend on the nature of the biomass and the type of the pyrolysis process (type of reactor, temperature, gas flow, catalyst). Due to the high molecular complexity of bio-oil, many different technical had been developed to their complete characterization. This chapter describes the principles of the techniques and main application of chromatographic methods (GC, LC, GC × GC, LC × LC, Nano-LC) in the analysis of bio-oils derived from thermo-degradation of biomasses. Especial attention is carried out to two-dimensional techniques that represent the state of the art in terms of separation, sensibility, selectivity and velocity of data acquisition for characterization of complex organic mixtures. For proper use of bio-oil in the chemical industry, it is essential the identification and unambiguous determination of its major constituents. Only then, it is possible to propose a recovery route of some of these components for the development of an industry dedicated to a bio-refinery. For this, chromatographic methods, especially GC × GC/MS, are fundamental because they allow analysis with high sensitivity and accuracy in identifying each constituent of the bio-oil.

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Section: Continuously Feeding Reactors (Cfrs)mentioning

confidence: 99%

Chromatographic Methods Applied to the Characterization of Bio-Oil from the Pyrolysis of Agro-Industrial Biomasses

Moraes¹,

Tomasini²,

Silva³

et al. 2017

Frontiers in Bioenergy and Biofuels

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“…Nogueira et al (2004) on the other hand have applied the hybrid model of neural networks and two‐dimensional phenomenological model to analyse the continuous polymerization in tubular reactors. The neural networks were used in parallel with the one‐dimensional model.…”

Section: Neural Network Applications In Modelling and Control Of Polmentioning

confidence: 99%

Modelling and control of different types of polymerization processes using neural networks technique: A review

et al. 2010

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Polymerization process can be classified as a nonlinear type process since it exhibits a dynamic behaviour throughout the process. Therefore, it is highly complicated to obtain an accurate mechanistic model from the nonlinear process. This predicament always been a "wall" to researchers to be able to devise an optimal process model and control scheme for such a system. Neural networks have succeeded the other modelling and control methods especially in coping with nonlinear process due to their very conciliate characteristics. These characteristics are further explained in this work. The predicament that is encountered by researchers nowadays is lack of data which consequently lead to an imprecise mechanistic model that scarcely conforms to the desired process. The implementations of the neural network model not only restrict to polymerization reactor but to other difficult-to-measure parameters such as polymer quality, polymer melts index and mixture of initiators. This work is aimed to manifest ascendancy of neural networks in modelling and control of polymerization process.Le processus de polymérisation peutêtre catégorisé comme un processus de type non linéaire, puisqu'il affiche un comportement dynamique tout au long du processus. Par conséquent, il est très compliqué d'obtenir un modèle mécaniste précis du processus non linéaire. Cette situation fâcheuse a toujours représenté un « mur » pour les chercheurs qui souhaitent concevoir un schéma de contrôle et de modélisation du processus optimal pour un tel système. Les réseaux neutres ont succédé aux autres méthodes de modélisation et de contrôle, surtout pour s'occuper du processus non linéaire en raison de leurs caractéristiques très conciliantes. Ces caractéristiques sont expliquées plus en détail dans ce travail. La situation fâcheuse rencontrée par les chercheurs aujourd'hui est le manque de données, ce qui mène par conséquentà un modèle mécaniste imprécis qui està peine conforme au processus souhaité. Les mises en oeuvre du modèle des réseaux neutres se limitent non seulement au réacteur de polymérisation, mais aux autres paramètres difficilesà mesurer, comme la qualité du polymère, l'indice de fluidité du polymère et le mélange des initiateurs. Ce travail chercheà manifester l'ascendance des réseaux neutres en modélisant et contrôlant le processus de polymérisation.

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“…On the contrary, axial dispersion models describe hydrodynamics on macro scale with only one or two parameters, but allow for the implementation of complex polymerization kinetics on micro scale. This approach is widely applied to tubular reactors and polymer reactors without any inserts having well predictable hydrodynamic behavior 10,25–31…”

Section: Introductionmentioning

confidence: 99%

Simulation of Polymer Reactors Using the Compartment Modeling Approach

Cremer-Bujara

Biessey

Grünewald

2019

Macro Reaction Engineering

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A compartment modeling approach based on computational fluid dynamics (CFD) simulations is applied to a simplified static mixer geometry. Compartments are derived from velocity fields obtained from cold CFD simulations. This methodology is based on the definition of periodic flow zones (PFZ) derived from the recurrent flow profile within the static mixer. In general, PFZ can be characterized by two different compartments: flow zones with hydrodynamic behavior of a tubular reactor and dead zones exhibiting a more continuous stirred tank reactor‐like characteristic. In CFD studies the influence of changing fluid properties, for example viscosity, on flow profile due to polymerization progress is considered. In the deterministic compartment model, the continuous flow profile within the static mixer is transformed to basic reactor models interconnected via an exchange stream. To reduce model complexity and the number of model parameters, constant volumes of compartments are assumed. Changes in hydrodynamics are considered by a variable exchange flow rate as a function of Re manipulating residence time in compartments. Simulation studies show the influence of decreasing exchange flow rates with polymerization progress, as Re decreases, resulting in a greater increase of viscosity in dead zones. The reactor performance is qualitatively represented by the simulation results.

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Continuous polymerization in tubular reactors with prepolymerization: Analysis using two‐dimensional phenomenological model and hybrid model with neural networks

Cited by 10 publications

References 27 publications

Chromatographic Methods Applied to the Characterization of Bio-Oil from the Pyrolysis of Agro-Industrial Biomasses

Chromatographic Methods Applied to the Characterization of Bio-Oil from the Pyrolysis of Agro-Industrial Biomasses

Modelling and control of different types of polymerization processes using neural networks technique: A review

Simulation of Polymer Reactors Using the Compartment Modeling Approach

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