NEURAL NETWORK–BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS

The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics make the design of such reactors a challenging task to accomplish. The models of such processes rely heavily on predictive empirical correlations for the mass and heat transfer coefficients. Unfortunately, published empirical-based correlations have the common shortcoming of low prediction efficiency compared with experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.     

Kinetics and Modeling of Fatty Alcohol Ethoxylation in an Industrial Spray Loop Reactor

The kinetics of the ethoxylation of fatty alcohols catalyzed by potassium hydroxide was studied to obtain the rate constants for modeling of the industrial process. Experimental data obtained in a lab‐scale semibatch autoclave reactor were used to evaluate kinetic and equilibrium parameters. The kinetic model was employed to model the performance of an industrial‐scale spray tower reactor for fatty alcohol ethoxylation. The reactor model considers that mass transfer and reaction occur independently in two distinct zones of the reactor. Good agreement between the model predictions and real data was found. These findings confirm the reliability of the kinetic and reactor model for simulating fatty alcohol ethoxylation processes under industrial conditions.     

Towards modelling production of clean fuels: sour gas formation in catalytic cracking

Although one important by‐product of fluidised‐bed catalytic cracking of hydrocarbons is the sour gas (mainly hydrogen sulfide), there are no kinetic models to predict its generation. Moreover, if feedstock sulfur is not directed to sour gas, it will be present in gasoline, cycle oils and coke. These products are used as fuels, which could emit sulfur oxides during their combustion. In order to be able to model production of clean fuels, a kinetic scheme that considers sour gas as unmatched product was developed; meanwhile, the sulfur distribution in cracking products is predicted. Model parameters are validated using industrial operating data. This kinetic scheme is employed to model steady state operation of an industrial catalytic cracking riser and to find operating conditions that diminish the sulfur content in fuels. Copyright © 2004 Society of Chemical Industry     

Modeling of a Reverse Flow Reactor for Methanol Synthesis Modeling of a Reverse Flow Reactor for Methanol Synthesis

An accurate one-dimensional, heterogeneous model taking account of axial dispersion and heat transfer to the reactor wall, and heat conduction through the reactor wall for methanol synthesis in a bench scale reactor under periodic reversal of flow direction is presented. Adjustable parameters in this model are the effectiveness factors for each of the three reactions occurring in the synthesis and a factor for the bed to wall heat transfer coefficient correlation. Experimental data were used to evaluate these parameters and reasonable values of these parameters were obtained. The model was found to closely predict the reactor performance under a wide range of operating conditions, such as carbon oxide concentrations, volumetric flow rate, and cyclic period.     

Modelling coke formation in an industrial ethane-cracking furnace for ethylene production

The development of a model for predicting coke formation in an industrial ethylene cracking furnace is described. Expressions for predicting the rates of catalytic and pyrolytic coke formation are developed and a differential equation is derived to predict changes in coke thickness with time and position. An expression is developed to account for a decline in the rate of catalytic coke formation with increasing thickness of the coke layer. The proposed coke model equations are used to extend a previously developed ethane pyrolysis furnace model that ignored coke. Three model parameters related to coke formation are estimated using industrial data to obtain reliable model predictions. Two of these parameters are coefficients that appear in the catalytic and pyrolytic coke formation rate expressions. The third is a characteristic-length parameter used to reduce the rate of catalytic coke formation as the coke layer grows. The resulting dynamic model matches the industrial data well and can be used to simulate furnace operation and predict coke thickness profiles over a variety of the operating conditions, thereby helping process engineers who plan the decoking process.     

Modeling of an industrial fixed bed reactor based on lumped kinetic models for hydrogenation of pyrolysis gasoline

In this work, a mathematical model of an industrial fixed bed reactor for the catalytic hydrogenation of pyrolysis gasoline produced from olefin production plant is developed based on a lumped kinetic model. A pseudo-homogeneous system for liquid and solid phases and three pseudo-components: diolefins, olefins, and parraffins, are taken into account in the development of the reactor model. Temperature profile and product distribution from real plant data on a gasoline hydrogenation reactor are used to estimate reaction kinetic parameters. The developed model is validated by comparing the results of simulation with those collected from the plant data. From simulation results, it is found that the prediction of significant state variables agrees well with the actual plant data for a wide range of operating conditions; the developed model adequately represents the fixed-bed reactor.     

Integration of CFD and polymerization for an industrial scale cis-polybutadiene reactor

A computational fluid dynamics (CFD) approach, coupled with anionic polymerization kinetics, was used to investigate the solution polymerization in a 12?m³ industrial scale cis-polybutadiene reactor. The kinetic model with double catalytic active sites was integrated with CFD by a user-defined function. The coupled model was successfully validated by the plant data and then used to investigate the key operating variables. Also, predictions of CFD model were compared with those of continuous stirred tank reactor (CSTR) model. Although the reaction mixture is well mixed in the middle and at the top of the reactor, there exists a poor mixing feeding zone at the bottom, which leads to serious deviations from the ideal CSTR. The polymerization process with nonideal mixing is very sensitive to the inlet temperature and the feeding rate. Enhancing the mixing performance in the feeding zone could be an effective way to improve the product quality.     

Modeling, simulation and control of dimethyl ether synthesis in an industrial fixed-bed reactor

Dimethyl ether (DME) as a clean fuel has attracted the interest of many researchers from both industrial communities and academia. The commercially proven process for large scale production of dimethyl ether consists of catalytic dehydration of methanol in an adiabatic fixed-bed reactor. In this study, the industrial reactor of DME synthesis with the accompanying feed preheater has been simulated and controlled in dynamic conditions. The proposed model, consisting of a set of algebraic and partial differential equations, is based on a heterogeneous one-dimensional unsteady state formulation. To verify the proposed model, the simulation results have been compared to available data from an industrial reactor at steady state conditions. A good agreement has been found between the simulation and plant data. A sensitivity analysis has been carried out to evaluate the influence of different possible disturbances on the process. Also, the controllability of the process has been investigated through dynamic simulation of the process under a conventional feedback PID controller. The responses of the system to disturbance and setpoint changes have shown that the control structure can maintain the process at the desired conditions with an appropriate dynamic behavior.     

Development and implementation of a hybrid scale up model for a batch high shear wet granulation operation

The high shear wet granulation (HSWG) operation consists of several rate processes influenced by the raw material properties, process operational parameters, and equipment design. Their combined effect determines the granule attributes. In literature, these rate processes have been modeled using different dimensionless numbers and their correlations. Each of these dimensionless numbers represent only certain rate processes. Since many of these rate processes occur at the same time, it is necessary to simultaneously model them to account for all the important degrees of freedom. Most of the HSWG scale up approaches in literature calculate scale up conditions based on a single rate process or operating parameter of interest that can lead to sub-optimal process design. We present the development of a hybrid HSWG scale up model accounting simultaneously for all the rate processes. The approach was successfully implemented to scale up the HSWG operation across laboratory, pilot, and commercial scales.     

Estimation of Kinetic Parameters for Hydrogenation Reactions Using a Genetic Algorithm

The kinetics of acetylene hydrogenation in a fixed‐bed reactor of a commercial Pd/Al₂O₃ catalyst has been studied. The hydrogenation reactor considered in this work is an essential part of a vinyl chloride monomer (VCM) plant. Three well‐known kinetic models were used to simulate the hydrogenation reactor under industrial operating conditions. Since none of the models provide appropriate prediction, the industrial data and calculated values were compared and optimum kinetic parameters were evaluated utilizing a genetic algorithm (GA) technique. The best kinetic parameters for the three models were determined under specified industrial operating conditions. The hydrogenation reactor was simulated using the estimated optimum kinetic parameters of the three models. Simulation results from the three models were compared to industrial data and the best kinetic model was found. This kinetic model with the evaluated optimum kinetic parameters can well predict the behavior of the industrial hydrogenation reactor to improve the performance of the process.     

乙烯氧氯化流化床反应器的模型化及操作条件优化

对工业乙烯氧氯化挡板流化床反应器建立了以两相理论为基础的多釜串联反应器模型,利用大量工业数据求解模型参数并检验模型,计算结果表明模拟值与工业值基本吻合.在此基础上,对反应器操作条件进行优化,得到不同生产负荷下的最优操作条件.     

Simulation of a Reverse Flow Reactor for the Catalytic Combustion of Lean Methane Emissions

This work is focused on the performance prediction of pilot scale catalytic reverse flow reactors used for combustion of lean methane-air mixtures. An unsteady one-dimensional heterogeneous model for t...     

粒子群优化算法在催化裂化模型参数估计中的应用

参数估计是化工模型工业应用中的重要课题,有相当的难度。针对催化裂化八集总模型的动力学参数估计问题,考察了不同类型优化算法的应用效果,结果表明,粒子群优化算法简单、容易实现,而且可以避免传统方法对初始值的依赖,并进一步提出用结合Levenberg-Marquardt算法的混合粒子群优化算法提高参数估计效果。工业实例表明,用混合粒子群优化算法得到的动力学参数可以保证模型的预测精度。     

加氢裂化反应动力学模型研究及应用进展

加氢裂化技术在石化行业中的地位已举足轻重。为深入了解加氢裂化反应规律、优化工业装置运行工艺条件和产品分布,实现炼化企业智能化和效益最大化的目标,科研人员对加氢裂化反应动力学模型做了广泛的研究。对加氢裂化反应动力学模型相关研究及应用进展做了综述,介绍了加氢裂化反应动力学模型研究历程,研究目标从初期简易宏观的关联模型发展为按馏程或其他生产方案需求划分的传统、连续集总模型,再进一步发展为复杂微观的分子集总模型;概述了不同加氢裂化反应动力学模型的应用情况,突出说明了传统集总模型的工业实用性和分子集总模型原料、产品适用性;指出有效地将关联、集总模型（尤其是分子集总）结合利用,开发一种全面的混合动力学模型,将是未来加氢裂化以及其他石油加工过程反应动力学模型研究中极具意义和挑战的工作。     

Catalytic Oxidation of Dichloromethane and Perchloroethylene: Laboratory and Industrial Scale Studies

Development of a reliable laboratory scale test for the design of industrial catalysts is crucial. In this article, different laboratory-scale tests were compared with an industrial scale CVOCs treatment. With dichloromethane (DCM) the laboratory scale test results corresponded well to the industrial scale oxidation results. However, the perchloroethylene (PCE) conversions measured in industry were always higher than what was achieved in the laboratory scale indicating that the industrial scale catalytic incinerator operating in transient conditions is highly beneficial in PCE oxidation. It was clearly shown that in order to design high-quality laboratory scale experiments, information on complete composition and total concentration of the emission is needed but also different types of catalytic tests need to be used depending on the industrial reactor. In addition, the catalysts’ performance in an industrial VOC abatement unit was examined as the oxidation efficiencies of DCM, PCE and other hydrocarbons (OHC) were compared after 3, 10 and 23 months of operation. After 23 months and 13,065 operating hours, no significant decrease in the activity of the catalysts was observed showing that the used noble metal catalysts are highly resistant towards these demanding conditions.     

A new generic approach for the modeling of fluid catalytic cracking (FCC) riser reactor

A new kinetic model for the fluid catalytic cracking (FCC) riser is developed. An elementary reaction scheme, for the FCC, based on cracking of a large number of lumps in the form of narrow boiling pseudocomponents is proposed. The kinetic parameters are estimated using a semi-empirical approach based on normal probability distribution. The correlation proposed for the kinetic parameters’ estimation contains four parameters that depend on the feed characteristics, catalyst activity, and coke forming tendency of the feed. This approach eliminates the need of determining a large number of rate constants required for conventional lumped models. The model seems to be more versatile than existing models and opens up a new dimension for making generic models suitable for the analysis and control studies of FCC units. The model also incorporates catalyst deactivation and two-phase flow in the riser reactor. Predictions of the model compare well with the yield pattern of industrial scale plant data reported in literature.     

Modelling the performance of membrane nanofiltration—recovery of a high-value product from a process waste stream

For traditional separation processes there are widely available process design methodologies for scale-up and optimization. However, there is an increasing need for such a rational approach to membrane separation processes, identifying at an early stage operating limits and process options. Such predictive models will reduce development risk and time, thus promoting the wider use of membrane technology in process industries such as pharmaceutical manufacture. Design methods exist that have been verified experimentally at the laboratory scale for simple aqueous solutions. There is now a need for the application of the existing theoretical and experimental methods to separations of real industrial interest.In this paper, we demonstrate this philosophy by describing the rationale for modelling the performance of membrane nanofiltration (NF) used in the recovery of sodium cefuroxime, an industrially important cephalosporin antibiotic having activity against most gram-positive cocci. Sodium cefuroxime is produced in a multi-stage biotransformation process with final purification achieved by low-temperature crystallization with excess quantities of sodium lactate. The efficiency of the crystallization process is not 100% and cefuroxime is lost in the waste stream from the crystallization units. Traditionally, this waste stream has been sent for industrial disposal as the concentrations of sodium cefuroxime are too low for normal separation processes to recover.A systematic study of three commercially available membranes indicated that the Desal-5-DK membrane was most suitable for the recovery process. Excellent agreement between the experimental findings and model predictions was observed for batch NF and a membrane charge isotherm was developed for use in process modelling. The full-scale recovery process was modelled theoretically and NF proved more than adequate for the separation required. An estimate of the industrial scale process operating constraints was made and the NF process was considered as a favourable modification to existing plants.     

A sequential approach to modeling catalytic reactions in packed-bed reactors

A sequential modeling approach is proposed to simulate catalytic reactions in packed-bed reactors. The hydrogenation of alpha-methylstyrene and wet oxidation of phenol are selected as studied cases. The modeling scheme combines a reactor scale axial dispersion model with a pellet scale model. Without involving any fitting parameters, such an approach accounts for the non-linear reaction kinetics expression and different types of pellet-liquid wetting contact. To validate the developed modeling scheme and the parallel approach reported in the literature, the experimental observations for hydrogenation of alpha-methylstyrene to cumene have been employed. The predicted results by both approaches agree reasonably with the experimental data for both gas- and liquid-limited reaction. The proposed sequential approach was also used to simulate the dynamic performance of the reactor and pellets for the catalytic wet oxidation of aqueous phenol over a newly developed but rapidly deactivated catalyst (MnO₂/CeO₂). The simulation results for the catalytic wet oxidation process by both approaches were compared. The simulation describes the time evolution of the catalyst stability at different pellet points along the reactor axis. The performance of trickle beds and packed bubble columns over a range of operating conditions were also investigated, and packed bubble columns were found to achieve higher phenol conversion at the cost of more rapid catalyst deactivation.     

Kinetic modeling of FCC process

Catalytic cracking of petroleum fractions a process termed as FCC is usually carried out in a reactor block with somewhat complicated hydrodynamic regime. The reactor block is considered as a combination of two different reactors. The riser is a near ideal plug-flow displacement of the catalyst and reaction mixture, while the main reactor vessel (separator) is considered as an ideal mixing CSTR. Temperature gradient along the plug-flow riser can vary on a linear and non-linear dependence. This is reflected by the thermal effect on the cracking products, along the altitude of the riser. Moreover, it can exert a considerable influence on the selectivity of the process in general, as characterized by the diversity of different hydrocarbon groups both in the gaseous and liquid products. The fluid catalytic cracking (FCC) is a process of conversion of a heavy oil fraction into lighter products in a catalytic fluidized reactor. The chemical composition and the structure of the feed are reflected on the catalyst's selectivity and the amount of coke deposited. It is, therefore, necessary to consider the feed type on modeling the process. Cracking reaction in the model was represented as a five-stage process. Reaction rates for the plug-flow riser and the ideal mixing separator are described mathematically in differential and algebraic forms. The model takes into account, exponential dependence of the specific reaction rate on temperature, as well as reflects the influence of the real and bulk catalyst densities, circulation rate, equilibrium and fresh catalyst's activities, reactor pressure, feed rate and unit construction. The model was developed based on a data taken from an industrial FCC unit, that were used to compute the kinetic constants and other parameters. Concrete computed kinetic parameters were compared with corresponding experimental data for adequacy. FCC process is in constant technological development with modernization of especially the riser reactor. Kinetic modeling of the catalytic FCC reactor will give a further understanding of the process and explain the complicated mechanism involved for an efficient and optimal conversion of the feed stock.     

Robust model predictive control of an industrial partial combustion fluidized-bed catalytic cracking converter

It is presented here in the study of the application of a robust model predictive control to an industrial partial combustion fluidized-bed catalytic cracking (FCC) converter. This particular type of FCC converter shows an interesting dynamics in which most of the system outputs are integrating with respect to the manipulated inputs. Time delays are also present and the model parameters can change depending on the operating point. Then, the system model should be represented by a set of possible plants, which can stand for different operating conditions of this process system. Moreover, one needs to include a comprehensive model formulation in order to accommodate time-delays for both stable and integrating outputs. The proposed control strategy was tested through simulation for the disturbances commonly found in the FCC converter unit, taking into consideration the plant/model mismatch. Results obtained from the simulated scenarios point out a fine prospective method. The robust controller shows a good potential to be implemented in the real process.