Industrial Ethylene Research Articles

Trends in industrial ethylene production: Innovation in process and catalyst design

Trends in industrial ethylene production: Innovation in process and catalyst design

Stabilization of cuσ+ via strong Cu‐O‐Si interface for efficient electrocatalytic acetylene semi‐hydrogenation

AbstractThe development of a high‐performance electrocatalytic acetylene semi‐hydrogenation catalyst is the key to the selective removal of acetylene from industrial ethylene gas and non‐oil route to ethylene production. However, it is still hampered by the deactivation of the catalyst and hydrogen evolution interference. Here, we proposed an interface engineering strategy involving the Cu and cupric oxide nanoparticles dispersed on amorphous SiO2 (Cu/CuOx/SiO2) by a simple stöber method. x‐ray photoelectron spectroscopy demonstrated the strong interfacial interaction between cupric oxide nanoparticles and SiO2. The formed Cu‐O‐Si interface stabilized the Cuσ+ at high reduction potentials, thus improving the activity and stability of the acetylene reduction reaction, as confirmed by in situ Raman spectroscopy. Consequently, the electrochemical test results showed that at 0.5 M KHCO3, the maximum Faraday efficiency (FE) of ethylene on the optimized Cu/CuOx/SiO2 reached 96%. And ethylene FE remains above 85% at −100 mA cm−2 for 40 h.

New Modeling Approaches for Ethylene Oxychlorination in Fluidized Bed Reactors: Industrial and Low Flow Rate Conditions

A simple model (Continuous Stirred-Tank Reactor) has been developed to predict the behavior of industrial ethylene oxychlorination fluidized beds operating in a turbulent regime. The approach showed good agreement both with results from industrial reactors and with those corresponding to the (Simple two phases-Plug bubble-Mixed flow emulsion approach) validated in the literature. For low flow rates, the use of the (Simple two phases - Plug bubble - Plug emulsion model) adapted to these conditions enabled us to highlight the location and extent of undesirable thermal hot spots for the process, and to propose actions to control them by acting on the temperature and/or on the feed gas flows. By comparing this model with the plug approach, the significant slowdown in ethylene conversion caused by resistance to mass transfer when feed flow rates are low is highlighted. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Direct propylene epoxidation with molecular oxygen over titanosilicate zeolites.

The direct epoxidation of propylene with molecular oxygen represents a desired route for propylene oxide (PO) production with 100% theoretical atomic economy. However, this aerobic epoxidation reaction suffers from the apparent trade-off between propylene conversion and PO selectivity, and remains a key challenge in catalysis. We report that Ti-Beta zeolites containing isolated framework Ti species can efficiently catalyze the aerobic epoxidation of propylene. Stable propylene conversion of 25% and PO selectivity of up to 90% are achieved at the same time, matching the levels of industrial ethylene aerobic epoxidation processes. H-terminated pentacoordinated Ti species in Beta zeolite frameworks are identified as the preferred active sites for propylene aerobic epoxidation and the reaction is initiated by the participation of lattice oxygen in Ti-OH. These results are expected to spark new technology for the industrial production of PO toward more sustainable chemistry and chemical engineering.

Oxidative dehydrogenation of ethane over boron-containing chabazite

We report that boron-containing zeolite chabazite (B-CHA) catalyzes the oxidative dehydrogenation of ethane (ODHE) with high selectivity (>70 %) and excellent stability in the temperature range of 500–600 °C. ODHE rates, in fact, increase over time on stream. Ethane consumption rate has an apparent activation energy of 126 kJ mol−1, with Langmuirian dependence on the oxygen partial pressure and first-order dependence on the ethane partial pressure. Investigation of the catalyst before and after reaction by one-dimensional 11B magic angle spinning (1D 11B MAS) nuclear magnetic resonance (NMR), two-dimensional 11B multiple quantum MAS (2D 11B MQMAS) NMR spectroscopy, and Fourier transform infrared (FTIR) spectroscopy identifies the B-OH group in defect trigonal boron (B(OSi)(OH)2) as the species initiating the ODHE reaction. This result could open a pathway to develop suitable catalysts for industrial ethylene production with lower greenhouse gas emissions than current non-oxidative dehydrogenation routes.

Circular olefin copolymers made de novo from ethylene and α-olefins

Ethylene/α-olefin copolymers are produced in huge scale and widely used, but their after-use disposal has caused plastic pollution problems. Their chemical inertness made chemical re/upcycling difficult. Ideally, PE materials should be made de novo to have a circular closed-loop lifecycle. However, synthesis of circular ethylene/α-olefin copolymers, including high-volume, linear low-density PE as well as high-value olefin elastomers and block copolymers, presents a particular challenge due to difficulties in introducing branches while simultaneously installing chemical recyclability and directly using industrial ethylene and α-olefin feedstocks. Here we show that coupling of industrial coordination copolymerization of ethylene and α-olefins with a designed functionalized chain-transfer agent, followed by modular assembly of the resulting AB telechelic polyolefin building blocks by polycondensation, affords a series of ester-linked PE-based copolymers. These new materials not only retain thermomechanical properties of PE-based materials but also exhibit full chemical circularity via simple transesterification and markedly enhanced adhesion to polar surfaces.

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

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

Ethylene Oxide Catalysis Under Commercial Conditions – A Guide for Researchers

AbstractSelective oxidation of ethylene to ethylene oxide (EO) is performed industrially over catalysts comprised of silver particles supported on α‐Al2O3. In addition to various oxy‐anion and alkali promotors, alkyl chloride species (“chloride moderators”) are continuously introduced into commercial reactors. The effect and usage of chloride moderators is a key requirement for industrial ethylene epoxidation catalysts to reach optimal selectivity. While not an exhaustive review, the aim of this perspective is to provide guidance to academic researchers on the operation of EO catalysts under commercially relevant conditions with particular focus on the importance of performance optimization.

Ethylene Recovery via Pebax-Based Composite Membrane: Numerical Optimization

Membrane technology, particularly polymeric membranes, is utilized in major industrial ethylene recovery owing to the very convenient and robust process. Thus, in this paper, a composite membrane (CM) comprising SAPO-34 and Pebax-1657 was employed to conduct a separation performance under two operating conditions, including temperatures and pressures, ranging from 25.0–60.0 °C and 3.5–10.0 bar, respectively. CO2 permeability and CO2/C2H4 ideal selectivity values that ranged from 105.68 to 262.86 Barrer and 1.81 to 3.52, respectively, were obtained via the experimental works. The separation of carbon dioxide (CO2) from ethylene (C2H4) has then been optimized using response surface methodology (RSM) by adopting a central composite design (CCD) method. As a result, the ideal operational conditions were discovered at a temperature of 60.0 °C and pressure of 10.0 bar with the maximum CO2 permeability of 233.62 Barrer and CO2/C2H4 ideal selectivity of 3.22. The typical discrepancies between experimental and anticipated data for CO2 permeability and CO2/C2H4 ideal selectivity were 1.67% and 3.10%, respectively, demonstrating the models’ validity. Overall, a new combination of Pebax-1657 and SAPO-34 composite membrane could inspire the latest understanding of the ethylene recovery process.

CFD modeling of the ethylene–vinyl acetate copolymerization autoclave reactor: Effects of comonomer ratio on reactor dynamics and copolymer properties

Computational fluid dynamics (CFD) modeling of an industrial ethylene–vinyl acetate autoclave copolymerization reactor was conducted to evaluate the effects of comonomer ratios on reactor dynamics and product properties, including the molecular weight distribution (MWD), and copolymer composition. To accurately predict the MWD of copolymer chains under highly nonideal mixing conditions at a reduced computational cost, the CFD-multicompartment model and probability generating function (PGF) transform were applied. The effectiveness of the developed model was validated by the satisfactorily consistent plant data and simulated results. The model revealed that an increase in the vinyl acetate (VA) content significantly decreased the temperature, promoting combinational termination and chain transfer to polymer reaction for a broad copolymer MWD. The pseudo-kinetic-based analysis showed that VA was consumed more slowly than ethylene, resulting in its lower value in the copolymer composition than that of the comonomer ratios. However, despite nonideal mixing, the copolymer composition was maintained to be uniform for the copolymers produced over the entire reactor volume. In conclusion, the proposed model can be used to understand the governing behaviors of copolymerization under nonideal mixing conditions, design commercial copolymerization reactors, and predict copolymer product qualities.

Comparative life cycle environmental, exergteic, and economic assessment of three hydrocarbon-based ethylene production routes

Currently and in the foreseeable future, steam cracking with sequential cryogenic separation, steam cracking with de-ethanization cryogenic separation (SC-DES), and steam cracking with de-propanization cryogenic separation are the primary ethylene production routes in China. In the era of carbon peaking and carbon neutrality, academia and industry focus on the high-quality development of manufacturing. In this study, a general framework integrating life cycle assessment (LCA), exergetic life cycle assessment (ELCA), and life cycle costing (LCC) was constructed to compare the comprehensive performance of three routes systematically. Furthermore, the model of the foreground process was developed by COILSIMID and Aspen Plus with two typical cracking feedstocks, naphtha and LPG. Based on these, eleven significant indicators from the LCA, ELCA, and LCC integrating framework were calculated to reflect the production performance from environmental, exergetic, and economic perspectives in three ethylene production routes. The results indicated that the overall categories of the SC-DES route are better than the other two routes, but a few indicators lack competitiveness. Since no route showed excellent performance in all indicators, it was not easy to declare that a certain path has the best sustainable performance. However, decision-makers can utilize the obtained results to make optimal decisions beneficial to industrial ethylene production.

State prediction of distributed parameter systems based on multi-source spatiotemporal information

To predict the process states of nonlinear distributed parameter systems (DPS) using various process variables, a novel multi-source spatiotemporal modeling method is proposed in this paper to improve conventional temporal modeling methods. To tackle the challenge of modeling industrial process data of DPS with multi-source spatiotemporal characteristics, a multi-source spatiotemporal network (MS-STN) model, which is the integration of a long short-term memory (LSTM) network to extract information containing temporal characteristics and a convolutional long short-term memory (ConvLSTM) network to extract information containing spatiotemporal characteristics, is constructed. The comprehensive use of various process information is beneficial to improving the fitting accuracy of the model, and the generalization ability of the model can be enhanced at the same time. To prevent the gradient vanishing or gradient explosion in presenting complex spatiotemporal data due to the increase of network layers, a model structure of the residual network based on ConvLSTM is proposed in performing model training. Finally, the industrial ethylene oxychlorination reaction process is taken as an example. The experimental results of predicting the temperature of the reaction tube well demonstrate the effectiveness of the proposed method.

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.

Retrofitting Heat Exchanger Network of Industrial Ethylene Glycol Plant using Heat Integration based on Pinch Analysis

Abstract Heat integration by pinch method is used to modify the heat exchanger network of an industrial ethylene glycol plant. The aim is to reduce the energy cost by operating the plant close to the maximum energy recovery. Pinch analysis identified a pinch temperature of 483 K, a minimum heating utility of 13,490.9 MJ/ton EO, and a minimum cooling utility of 25,697 MJ/ton EO. Using the pinch decomposition diagram and the standard procedure for matching hot and cold streams, a retrofit of the heat exchangers network is developed. The modified heat exchanger network reduces the external cooling duty by 45.5% and the external heating duty by 93.3%. This promising cost savings provide enough justification for restructuring the existing ethylene glycol plant. Moreover, an additional 6% reduction in the external cooling duty can be achieved by integrating the steam turbine below the pinch point.

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.

Uncertainty analysis of NOx and CO emissions in industrial ethylene cracking furnace using high-precision sparse polynomial chaos expansion

ABSTRACT Traditional designing of ethylene-cracking furnaces by computational fluid dynamics (CFD) does not consider the uncertainties in actual engineering. This paper introduces one uncertainty analysis framework based on non-intrusive polynomial chaos expansion (NIPCE) and multi-order Sobol indices to perform uncertainty quantification (UQ) and sensitivity analysis for NOx and CO emissions in the combustion process. In this framework, several operating parameters of the bottom burners that easily cause uncontrollable fluctuations in the flame and emission characteristics are selected as uncertainty variables and characterized as a probability distribution. Computationally expensive CFD simulation of cracking furnace is used to generate a small number of samples, which are carried out to develop high-precision sparse PCE models by the degree-adaptive scheme and least angle regression (LAR) algorithm. And multi-order Sobol sensitivity analysis based on PCE models is performed efficiently to research the influence of uncertain parameters on pollution generation. Under 3% of burner’s uncertainty operating parameters, the results find that the excess air coefficient of 1.20 can obviously reduce the generation of NOx and CO emissions and control the fluctuation caused by uncertainties. Moreover, sensitivity analysis determines the critical variables that affect pollutant emissions to help the actual process avoid the unknown effects of uncertainties.

Correlation Degree and Clustering Analysis-Based Alarm Threshold Optimization

In industrial practice, excessive alarms and high alarm rates are mostly generated from unreasonable settings to variable alarm thresholds, which have become the significant causes of impact on operation stability and plant safety. A correlation degree and clustering analysis-based approach was presented to optimize the variable alarm thresholds in this paper. The correlation degrees of variables are first obtained by analyzing correlation relationships among them. Second, the variables are grouped according to the gray correlation coefficients and clustering analysis, given the weight for fault alarm rate (FAR) in each group. An objective function about the FAR, missed alarm rate (MAR), and the maximum acceptable FAR and MAR is then established with variable weight. Eventually, based on an optimization algorithm, the objective function can be optimized for obtaining the optimal alarm threshold. Cases study of the Tennessee Eastman (TE) industrial simulation process and an actual industrial ethylene production process, in comparison to the initial situation, show that the method can effectively reduce FAR according to correlation degrees among variables in the system, and decrease the number of alarms with reduction rates of 40.5% and 35.3%, respectively.

Flooding and offset-free nonlinear model predictive control of a high-purity industrial ethylene splitter using a hybrid model

This work aims to achieve flooding free control for an industrial ethylene splitter. The tower operates close to maximum capacity and experiences flooding due to feed disturbances. This work develops a replacement of the current control strategy with an offset-free Nonlinear Model Predictive Control (OF-NMPC) to improve the control and avoid flooding. OF-NMPC uses a hybrid model comprised of a Nonlinear Autoregressive Network (NARX) for predicting dynamics, and a first principles steady state model to calculate the tray liquid flow which is a constraint in OF-NMPC. The calculation predicts the steady-state internal flow which would occur at the current values of the manipulated variables, thus providing about 20 min time interval to avoid flooding via controller actions. An Aspen Dynamics model that closely mimics the plant is used to test the proposed OF-NMPC. The simulation results demonstrate the ability of the proposed OF-NMPC design to achieve flooding free control.

Identification of earth-abundant materials for selective dehydrogenation of light alkanes to olefins

Selective ethane dehydrogenation (EDH) is an attractive on-purpose strategy for industrial ethylene production. Design of an effective, stable, and earth-abundant catalyst to replace noble metal Pt is the main obstacle for its large-scale application. Herein, we report an experimentally validated theoretical framework to discover promising catalysts for EDH, which combines descriptor-based microkinetic modeling, high-throughput computations, machine-learning concepts, and experiments. Our approach efficiently evaluates 1,998 bimetallic alloys by using accurately calculated C and CH3 adsorption energies and identifies a small number of new promising noble-metal-free catalysts for selective EDH. A Ni3Mo alloy predicted to be promising is successfully synthesized, and experimentally proven to outperform Pt in selective ethylene production from EDH, representing an important contribution to the improvement of light alkane dehydrogenation to olefins. These results will provide essential additions in the discovery and application of earth-abundant materials in catalysis.

Adaptive system identification of industrial ethylene splitter: A comparison of subspace identification and artificial neural networks

The manuscript considers the problem of data-driven modeling of an ethylene splitter (from an industrial plant). The process presently operates with end composition controllers that does not work well during process transition. The objective of the present work is to investigate the use of different data-driven techniques such as subspace identification and neural network-based methods for the purpose of developing a dynamic data-driven model. To this end, first an ethylene splitter simulation model is built that replicates industrial operation. The ability of the simulation model to capture the key traits of the process dynamics are first established by comparing it with data from the plant operation. The simulation model is subsequently utilized to work as a test bed for future control purposes and to serve as an additional test of the modeling approaches. An online model adaptation scheme is developed to improve the model's prediction capabilities under new operation patterns.