Ethylene Oxide Reactor Research Articles

Performance enhancement of commercial ethylene oxide reactor by artificial intelligence approach

Abstract The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, an ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and temperature with other operational parameters. A hybrid multi-objective metaheuristic optimization technique, namely ANN-multi-objective genetic algorithm (MOGA) algorithm was used to develop a Pareto diagram of selectivity versus reactor temperature. The Pareto diagram will help the plant engineers to make a strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this hybrid ANN-MOGA modeling and optimization technique, for a 720 KTA ethylene glycol plant, approximately 32,345 ton/year of carbon-di-oxide emission into the atmosphere can be reduced. Along with the reduction of environmental impact, this hybrid approach enables the plant to reduce raw material cost of nine million USD per annum simultaneously.

Modeling and multi-objective optimization of commercial ethylene oxide reactor to strike a delicate balance between profit and negative environmental impact

The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and other operational parameters. This model was used to establish two objective functions, profit and environmental impact. In this paper, the negative environmental impact has been designated by Eco-indicator 99, which considers all the negative health and environmental impacts of a certain product. A recently developed metaheuristic optimization technique, namely multi-objective firefly (MOF) algorithm, was used to develop Pareto diagram of profit vs. Eco-99. The Pareto diagram will help the plant engineers to make strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this modeling and optimization technique, for a 130 kTA EO plant, approximately 7048 t/year of carbon dioxide can be saved from emission into the atmosphere.

Recycling Monoethylene Glycol (MEG) from the Recirculating Waste of an Ethylene Oxide Unit

AbstractIn the ethylene glycol generation unit of petrochemical plants, first a reaction of ethylene oxide takes place which is then followed by other side reactions. These reactions include water absorption with ethylene oxide, which leads to the generation of formaldehyde and acetaldehyde. Over the lifetime of the alpha-alumina-based silver catalyst there is an increase in side reactions, increasing the amount of the formaldehyde and acetaldehyde generated by the ethylene oxide reactor which leads to reduced MEG product purity. Given the need of a petrochemical complex to further strip the aldehyde (formaldehyde and acetaldehyde) to increase the quality of the MEG and increase the lifetime of the alpha-alumina-based silver catalyst, resin beds are designed and their surface absorption capacity is investigated to optimize aldehyde (formaldehyde and acetaldehyde) removal in the recirculating water flow of the ethylene oxide unit. Experiments show that the ion exchange system based on strong anionic resin pre-treated with a sodium bisulfite solution can reduce the aldehyde level from about 300ppm to less than 5ppm. After the resin is saturated with aldehyde, the resin can be recycled using the sodium bisulfite solution which is a cheap chemical substance.

A new analytical approximation to the extended Brinkman equation

In this contribution, a new analytical approximation to the extended Brinkman equation, based on the previous work of Vortmeyer and Schuster is presented. The new approximation can be utilized for modelling pseudo-homogeneous two-dimensional fixed bed reactors. In the present contribution we focus on the applicability of the new approximation to the widely accepted heat and mass transport correlations proposed by Winterberg et al. Replacing the Brinkman equation with the new analytical approximation in an existing reactor model reduces the model complexity and thereby the computational effort with basically no loss of accuracy. This is exemplified by an optimization case study of an ethylene oxide reactor (air-based process variant) which is provided as supplementary material.

Heterogeneous Reactor Modeling of an Industrial Multitubular Packed‐Bed Ethylene Oxide Reactor

AbstractThe one‐dimensional heterogeneous model of an industrial multitubular packed‐bed ethylene oxide (EO) reactor was developed using the equation‐oriented platform Aspen Custom Modeler. Reactor operation was optimized in terms of maximized EO production and selectivity and enhanced safety related to the presence of oxygen in the EO reactor. Good agreement was found between the model results during validation against the available information under identical operating conditions. The model predicts the behavior of the EO reaction and demonstrates the extent of catalyst utilization with product distribution, product yield, by‐product formation, temperature and concentration profiles, over time and along the length of the reactor or catalyst bed. The model sensitivity studies compute the optimum feed flow, oxygen concentration, feed pressure, etc. and suggest the best operational philosophy.

Effect of catalyst selectivity on exergetic and exergoeconomic evaluation of ethylene oxide/ethylene glycol process

Ethylene oxide (EO) is an important petrochemical used for different applications in chemical process industries. EO production cost is higher due to price of ethylene and silver catalyst. Safety constraints limit conversion of ethylene to EO in the reactor. Ethylene consumption in the process is gradually increases due to reduction in catalyst selectivity. Exergy analysis is used as a tool for optimisation of the EO process considering above hurdles. Half of the EO produced in the world is converted into ethylene glycol (EG), hence integrated plant of EO-EG is considered in the present study. Exergy efficiencies of two EG manufacturing processes are compared. Recovery of ethylene from purge gas is also analysed based on exergy. EXCEM method is used for economic evaluation of the process. A proposed parameter EGRex is useful to decide the catalyst life based on exergy loss in the EO reactor and income generated in the process.

Dynamic optimal design of an industrial ethylene oxide (EO) reactor via differential evolution algorithm

In this study, the operating conditions of an industrial fixed-bed ethylene oxide reactor are optimized via differential evolution (DE) algorithm to boost the ethylene oxide yield with taking into account the catalyst deactivation. A mathematical heterogenous model of the reactor has been used in order to evaluate the optimal operating conditions, both at steady state and dynamic conditions. Two different cases have been investigated in this regard. In the first case, optimum inlet molar flow rate, inlet pressure of the reaction side and temperatures of shell and tube sides have been obtained within their practical ranges. In the second case, a stepwise approach has been followed in order to determine the optimal temperature profiles for saturated water and gas in three steps during operation. The objective of each optimization case study is to maximize the ethylene oxide production rate. The effect of optimal operating conditions on the reactor performance has been compared with corresponding industrial conditions over a period of three operating years. 1.726% and 4.22% yield enhancement of ethylene oxide production can be achieved by application of first and second optimization case studies, respectively.

Dynamic mathematical modeling of a novel dual-type industrial ethylene oxide (EO) reactor

In this paper, the dynamic behavior of a novel dual-type industrial ethylene oxide reactor has been proposed with taking catalyst deactivation into account. The configuration of two catalyst beds instead of one single catalyst bed is developed for conversion of ethylene to ethylene oxide. In the first reactor which is an industrial fixed-bed water-cooled reactor, the feed gas is partly converted to ethylene oxide. This reactor functions at very high yield and at a higher than normal operating temperature. In the second converter, the reaction heat is used to preheat the feed gas to the first reactor and a milder temperature profile is observed. The potential possibilities of a two-stage catalyst bed system are analyzed using a 1D heterogeneous dynamic model to obtain necessary comparative estimates. A differential evolution (DE) algorithm is applied as an effective and robust method to optimize the reactors length ratio. The results obtained from the simulation demonstrate that there is a desirable catalyst temperature profile along the dual-type reactor (DR) compared with the conventional single-type reactor (SR). In this way, the catalysts are exposed to less extreme temperatures and thus, diminishing the catalyst deactivation via sintering. Results from this study provided beneficial information about the effects of reactors configuration on catalyst lifetime and ethylene oxide production rate simultaneously.

Development of a Hybrid Model for Industrial Ethylene Oxide Reactor

A hybrid modeling approach for an ethylene oxidation reactor with silver catalyst is proposed. Under the form of mechanistic model, support vector regression is used to construct the catalyst deactivation model with the operating data coming from real plant. Prior knowledge is extracted to enhance the generalization of the deactivation model. With the hybrid model, the prediction error is less than 5% for the prediction of industrial reactor. The approach is shown to predict the production more accurately and have more reliable extrapolation.

Analysis and optimal design of an ethylene oxide reactor

In this work, a recently proposed multi-level reactor design methodology ( Peschel et al., 2010) is extended and applied for the optimal design of an ethylene oxide reactor. In a first step, the optimal reaction route is calculated taking various process intensification concepts into account. The potential of each reaction concept can be efficiently quantified, which is the economic basis for the design of advanced reactors. Based on these results, a promising concept is further investigated and a technical reactor is designed. As an extension to the design method, reactor design criteria for external and internal heat and mass transfer limitations are directly included in the optimization approach in order to design the catalyst packing. The derived reactor concept is investigated with a detailed 2D reactor model accounting for radial concentration and temperature gradients in addition to a radial velocity profile. The example considered in this work is the production of ethylene oxide which is one of the most important bulk chemicals. Due to the high ethylene costs, the selectivity is the main factor for the economics of the process. A membrane reactor with an advanced cooling strategy is proposed as best technical reactor. With this reactor design it is possible to increase the selectivity of the ethylene epoxidation by approximately 3% compared to an optimized reference case.

Multi-Objective Optimization of an Ethylene Oxide Reactor

Multi-Objective Optimization of an Ethylene Oxide Reactor

Multi-Objective Optimization of an Ethylene Oxide Reactor

In this study, multi-objective optimization is performed for a reactor producing ethylene oxide from ethylene. The optimization considered three objectives: the maximization of the ethylene oxide production and selectivity, and the maximization of a safety factor related to the presence of oxygen in the reactor. The Pareto domain for this optimization problem was first approximated using the Objective-Based Gradient Algorithm, and the Pareto-optimal solutions were ranked using the Net-Flow procedure to determine the best operating conditions. From the optimization results, it is recommended that the ethylene oxide reactor be operated at high inlet pressure and gas temperature, and low inlet volumetric gas flowrate and chemical reaction moderator concentration. These operating conditions led to the highest ranked compromise solution, balancing the trade-off between each of the three objectives. Finally, it was found that a decrease in the inlet pressure or variation in the volumetric gas flowrate could readily lead to operating conditions outside of the Pareto domain, and these input variables should therefore be carefully controlled throughout operation of the reactor.

Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor

In this research a dynamic grey box model (GBM) of ethylene oxide (EO) fixed bed reactor has been presented. In the first step of the study, kinetic model of the existing reactions was obtained using artificial neural network (ANN) approach. In order to build the ANN model industrial data of a typical EO reactor were employed. Time, C 2H 4, C 2H 4O, CO 2, H 2O and O 2 mole fractions were network inputs and the multiplication of reaction rate and catalyst deactivation ( r * a)was ANN output. From 164 data, 109 data were employed to train ANN. After employing different training algorithms, it was found that, the radial basis function network (RBFN) training algorithm provides the best estimations of the data. This best obtained network was tested against fifty five unseen data. The network estimations were close to unseen data which confirmed generalization capability of the obtained network. In the next step of study, ( r * a) was estimated with ANN and then the hybrid model of the reactor was solved. Simulation results were compared with EO mechanistic model and also with plant industrial data. It was found that GBM is 8.437 times more accurate than the mechanistic model.

Modeling and Simulation of an Industrial Ethylene Oxide (EO) Reactor Using Artificial Neural Networks (ANN)

In the present work, a one-dimensional heterogeneous model was used for dynamic simulation of an industrial fixed-bed catalytic ethylene oxide (EO) reactor in the presence of long-term catalyst deactivation. In order to determine the level of optimum ethylene dichloride (EDC), a multilayer perceptron (MLP) neural network was used. In addition, the effect of inlet gas velocity on EO mole fraction of gas and solid phases was investigated. The model validation was carried out by comparison of model results with corresponding industrial conditions and over a period of three operating years. A good agreement was found between the simulation results of the dynamic model and historical process data. The error of simulation was found to be less than 5%. The results of the artificial neural network (ANN) modeling showed that the maximum selectivity occurs in the range of 0.37–0.42 ppm of EDC. Also, it was observed that with decrease of gas velocity the difference between the EO mole fraction of gas phase and solid phase increases. This behavior was attributed to the distinct resistances of kinetically controlled and the mass transfer resistance of gas film around the catalyst pellets.

Soft sensor for better control of carbon dioxide removal process in ethylene glycol plant

Low Carbon-dioxide in cycle gas loop of Ethylene Glycol (EG) plant improves catalyst selectivity and overall economics of the plant. Carbon-dioxide produced as a bi-product in ethylene oxide reactor is removed by Benfield process. In this process the carbonate and bicarbonate ratio in lean carbonate solution is considered as an important quality control (QC) variable as the efficiency of carbon-dioxide removal is largely depends on it. In the event of a process malfunction or operating under suboptimal condition, the CO2 content in cycle gas loop will continue to rise until corrective action is taken after obtaining lab results for carbonate and bicarbonate ratio. This time consuming sampling process can be overcome by implementing a technological solution in form of an accurate and robust mathematical model capable of real time QC variable prediction. For well understood processes, the structure of the correlation for QC variables as well as the choice of the inputs may be well known in advance. However, Benfield process is too complex and the appropriate form of the correlation and choice of input variables are not obvious. Here, the processes knowledge, operating experience and statistical methods were applied in developing the soft sensor. This paper describes a systematic approach to the development of inferential measurements of carbonate and bicarbonate ratio using Support Vector Regression (SVR) analysis. Given historical process data, a simple SVR-based soft sensor model is found capable of identifying and capturing the cause and effect relationship between operating variables (model inputs) and QC variables (model outputs). Special care was taken to choose input variables, so that the final correlation and regression coefficient make senses from process engineering point of view. The developed soft sensor is implemented in commercial ethylene glycol plant in an Exaquantum interface and found satisfactorily predicting the carbonate and bicarbonate ratio in real time.

Modeling of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution

This study is motivated by a growing popularity of artificial neural network for process modeling and regression problems. However, many ANN regression application studies are performed by ‘expert' users who have a good understanding of ANN methodology. Since the quality of ANN models depends on a proper setting of ANN architecture and ANN meta-parameters, the main issue for practitioners trying to apply ANN regression is how to set these parameter values (to ensure good generalization performance) for a given data set. Non-expert users face a difficulty in finding an optimum ANN architecture and are often confused about how to choose the ANN meta parameters. The present paper addresses this issue and develops a new hybrid procedure to find the optimum ANN architecture and tunes the ANN parameters, thus relieving the ‘non expert' users. This method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta parameters. The algorithm has been applied for modeling of a commercial ethylene oxide (EO) reactor. The model developed through this hybrid algorithm can predict the EO reactor output with only 0.4% error.

Modelling and Optimisation of an Industrial Ethylene Oxide Reactor

A dynamic model of an industrial packed-bed multi-tubular reactor was developed to investigate performance of an industrial ethylene oxide (EO) reactor, conducting epoxidation of ethylene over a silver-based catalyst. The set of nonlinear kinetic rate equations for the catalytic oxidation process in the presence of ethylene dichloride (EDC) as a moderator was coupled with the governing heat and mass transfer equations along the packed bed. Catalyst deactivation was modelled as a nonlinear function of operating time and the equation was benchmarked against plant data for the period of operation. Our process model was compared with experimental data obtained from an industrial EO reactor. The model predictions were found to agree well with the plant data. The influences of operating parameters such as EDC level, reactant concentrations, reactor pressure, coolant temperature and the feed temperature on reactor performance were investigated. The variables having significant impact on work rate and selectivity were identified. The model was used to optimise the performance of ethylene oxide reactor for maximising work rate and selectivity.

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

AbstractThis article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.

Process Modeling and Optimization Strategies Integrating Support Vector Regression and Differential Evolution: A Study of Industrial Ethylene Oxide Reactor

This paper presents artificial intelligence-based process modeling and optimization strategies, namely, support vector regression – differential evolution (SVR-DE) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR-DE approach, a support vector regression model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Differential Evolution (DE) with a view to maximize the process performance. DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The SVR-DE is a new strategy for chemical process modeling and optimization. The major advantage of the strategy is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR-DE strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the EO production rate and catalyst selectivity.

Bifurcation and Stability Analysis of an Ethylene Oxide Reactor System

In this work, an open-loop and closed-loop nonlinear stability analysis of an industrial ethylene oxide reactor is performed. The simulator, which consists of a gas−gas heat exchanger, a multitubular fixed-bed reactor, a steam generator, and a separation system, was benchmarked with data from an industrial ethylene oxide reactor system. A two-dimensional heterogeneous model was developed for the multitubular fixed-bed reactor. The steady-state operability problem (open-loop bifurcation) was addressed by using nonlinear bifurcation techniques. The stable control regions of the ethylene oxide reactor system were developed as a function of operating temperature, catalyst activity, controller tuning, and disturbance direction and magnitude. A reactor runaway event was simulated, from which it is shown that eliminating the oxygen in the feed to the reactor can be used to prevent reactor runaway.