Industrial Ethylene Oxide Research Articles

The Application of ISO 11138-7 for Industrial Ethylene Oxide Sterilization Qualification

The Application of ISO 11138-7 for Industrial Ethylene Oxide Sterilization Qualification

How Federal and State Regulatory Systems Perpetuate Environmental Injustice in the United States: Industrial Ethylene Oxide Emissions as a Case Study.

Ethylene oxide (EtO), a known human carcinogen, is emitted from facilities across the United States. A 2018 assessment by the Environmental Protection Agency (EPA) showed that areas around EtO-emitting facilities had cancer risk levels up to 24 times the national average. The EPA notified the state Department of Environmental Protection (DEP) about the high cancer risk to their residents. Our aim was to analyze actions and implementation equity at the federal, state, and community levels since the EPA notification. Using publicly available data, we identified U.S. emitters of EtO and then analyzed community, state, and federal actions since the EPA notification through content analysis of internet data using the lens of the environmental inequality formation (EIF) theory. Thirty-one of a total 654 EtO-emitting facilities have an estimated cancer risk of over 100 in a million in neighboring census tracts and are located in 13 states and Puerto Rico, representing 7 EPA regions. Content analysis identified themes of community outcry, agency involvement, and legislative action and found no action without community outcry. By January 2021, 2 facilities had closed, 5 facilities had cut emissions, and 24 facilities in 9 states and 5 EPA regions had taken no action. Wealthier white neighborhoods saw facilities close or cut emissions. Differences in state response correlated with differences in community pressure and state priority setting, resulting in over 1 million people having continued significant EtO exposure for years. The impotence of the federal and state regulatory framework perpetuates environmental injustice in the United States.

Optimal artificial neural network architecture design for modeling an industrial ethylene oxide plant

Optimum selection of input variables, number of hidden neurons and connections among the network elements deliver the best configuration of an ANN, usually resulting in reduced over-fitting and improved test performance. This study focuses on the development of a superstructure-oriented feedforward ANN design and training algorithm whose impacts are demonstrated on an industrial Ethylene Oxide (EO) plant for the prediction of product related variables. Proposed method brings about a mixed integer nonlinear programming problem (MINLP) to be solved, which takes the existence of inputs, neurons, and connections among the network elements into account by binary variables in addition to continuous weights of existing connections. Our investigations show that almost 85% of the ANN connections are removed compared to the fully connected ANN (FC-ANN) with 50% decrease in the number of inputs of the ANN. The modified ANN delivers a better prediction performance over FC-ANN, since FC-ANN suffers from over-fitting.

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.

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.

Selective Oxidation of Ethylene in an Industrial Packed-Bed Reactor: Modelling, Analysis and Optimization

This work is focused on the modelling, analysis and optimization of industrial ethylene oxide production in a packed bed reactor. The aim is to identify the critical variables that maximize the reactor productivity in an existing facility without compromising personnel safety and equipment integrity. The chemical reactions involved are highly exothermic making the internal temperature control of this unit a challenging task. Temperature excursions at dangerous levels have been experienced due to variations in composition and temperature of fresh feed to the reactor. Therefore, the prediction of dynamic temperature and composition profiles in the reactor are important for its safe operation. The model we developed incorporates catalyst deactivation and the effect of an inhibitory agent: 1,2-dichloroethene. The model predictions were found to be in good agreement with the plant data. Our model-based optimization studies show that the optimal set point for the inlet coolant temperature is suitable for preventing reactor hot spots and maximizing ethylene oxide selectivity. The heat integration aspects of the process were addressed.

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.

High-pressure STM of the interaction of oxygen with Ag(111)

To identify surface phases that could play a role for the epoxidation of ethylene on Ag catalysts we have studied the interaction of Ag(111) with O(2) at elevated pressures. Experiments were performed using high-pressure scanning tunneling microscopy (STM) at temperatures between 450 and 480 K and O(2) pressures in the mbar range. Below p(O(2)) approximately 1 mbar the surface largely showed the structure of bare Ag(111). At p(O(2)) above approximately 1 mbar the (4 x 4)O structure and the closely related (4 x 5 radical 3)rect structure were observed. The findings confirm theoretical predictions that the (4 x 4)O structure is thermodynamically stable at the oxygen partial pressure of the industrial ethylene oxide synthesis. However, in other experiments only a rough, disordered structure was observed. The difference is caused by the chemical state of the STM cell that depends on the pretreatment and on previous experiments. The surface was further analyzed by X-ray photoelectron spectroscopy (XPS). Although these measurements were performed after sample transfer to ultra-high vacuum (UHV), so that the surface composition was modified, the two surface states could still be identified by the presence of carbonate or a carbonaceous species, and by the absence or presence of a high-binding energy oxygen species, respectively. It turns out that the (4 x 4)O structure only forms under extremely clean conditions, indicating that the (4 x 4)O phase and similar oxygen-induced reconstructions of the Ag(111) surface are chemically unstable. Chemical reactions at the inner surfaces of the STM cell also complicate the detection of the catalytic formation of ethylene oxide.

The U.S. patent that reached around the world (patent infringement)

The article presents the case filed before the Court of Appeals for the Federal Circuit (CAFC) in the recent case of Union Carbide v. Shell regarding US patent infringement by activities in a foreign country. The case involved a patented method for making the industrial chemical ethylene oxide using silver-based catalysts. The seven-year patent infringement suit that ensued saw the victory of Union Carbide after two jury trials. However, the trial judge had said that Union Carbide was not entitled to any damages for Shell's sales of catalysts to its foreign customers. Union Carbide felt that such damages were proper because the only use for Shell's catalysts was to practice Union Carbide's patented method. Both at trial and on appeal, Union Carbide based its claim on a section of the Patent Laws [Section 271(f)], which Congress had enacted in 1984 in response to another court fight about offshore infringement. This section, in relevant part, i.e., 271(f)(2), provides that it is an act of infringement to supply "any component of a patented invention" from the United States with the intent that the component will be employed (combined) outside the United States in a manner that would infringe if done in the United States, if the component is 1) especially adapted for use in the invention and 2) not a staple article or commodity of commerce suitable for a substantial noninfringing use. Before the Union Carbide case, Section 271(f)(2) had been limited to apparatus and composition patents. Union Carbide wanted to extend its scope to method patents. The Union Carbide case is another example of the shrinking size of the world. Courts respond to changing realities, and with the global marketplace and outsourcing, one can expect that offshore infringement will become an increasingly common issue faced by patent owners and the infringers they sue.

Trimethylamine as a Gas‐Phase Promoter: Highly Efficient Epoxidation of Propylene over Supported Gold Catalysts

The probable reaction pathways for propylene epoxidation with O2 and H2 in the presence of trimethylamine on trimethylsilylated Au-Ba(NO3)2/titanosilicate catalyst are discussed (see figure). The use of trimethylamine as a gas-phase promoter for nanoparticulate gold catalysts leads to a direct one-stage production of propylene oxide (PO) with steady space–time yields comparable to values for current industrial ethylene oxide production.

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.

Modelling an Industrial Ethylene Oxide Reactor

An industrial catalytic tubular reactor used to make ethylene oxide is described by a system of nonlinear partial differential equations. The dimensionless model is integrated by the method of lines. Excellent agreement between the plant and the model predicted data has been obtained. Simulations show that increases in the temperature of the reactor are accompanied by higher rates of reaction, which is desirable, and increased production of CO2, which is undesirable. This difficult to control industrial reactor case study can be used to test control theories.

Ethylene oxide resistance of micro-organisms in dust compared with the resistance of Bacillus subtilis spores.

The resistance of standardized spore preparations of Bacillus stearothermophilus and Bacillus subtilis to industrial ethylene oxide sterilization procedures was compared. Moreover, the resistance of the most resistant preparation was compared with that of micro‐organisms in dust and dirt. The experiments demonstrated the importance of the water content to the ethylene oxide resistance of “test pieces” intended for the control of the microbiological effect of sterilization procedures. It was demonstrated also that micro‐organisms in dust and dirt may possess an ethylene oxide resistance in the same range as that possessed by B. subtilis spores in the vacuum‐dried routine “test pieces” of Statens Seruminstitut, Copenhagen. Inactivation curves representing the subtilis strain and some of the micro‐organisms which had survived the industrial sterilization procedures revealed that the decisive factor in the inactivation was the water content inside the micro‐organisms or in their immediate vicinity.