Ethylene Production Process Research Articles

Improving Net Energy Efficiency of Dimethyl Ether Production Process by Methanol Dehydration

Dimethyl ether (DME) is a source of fuel that produces clean energy for the future. Methanol can be used as a raw material for the manufacture of DME as a natural gas that is treated for synthesis. This paper evaluates how to improve net energy efficiency in DME production and how to review the net energy efficiency calculations in DME production. Methods used for production of DME are methanol dehydration, thermodynamics examination, also improving the net energy efficiency of DME with the addition of the heat exchanger (E-100), the addition of a heater (E-104) before entering a column (T-102), and moved the mixer position before the heater (E-100). By modifying the addition of a heat exchanger (E-100), heater (E-104), and changing the position of the mixer in DME production, it has been proven that it can reduce energy requirements in the dimethyl ether synthesis process from methanol and increase net energy efficiency by up to 98.83%. The results of the case study indicate that the addition heat exchanger (E-100) able to reduce the heater load after the creation process and remove the cooler (E-101) that existed before creation, then the addition of the heater (E-104) serves to reduce the load of Qcond2 and Qreb2 on columns (T-102), also the position of the mixer for the methanol recycling flow is moved before the heater (E-100) is intended to remove the heaters (E-103). Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Efficient separation in n‐butyl ether production process from computational thermodynamics to process intensification

AbstractBiobutanol is a green solvent commonly used as gasoline additive. In this study, it is proposed the separation of azeotropic mixtures of butanol and n‐butyl ether (DBE) using ionic liquids (ILs). Suitable anions [AC]− and [H2PO4]− were screened by COSMO‐RS, and the effect of carbon chain length on the separation effect was investigated using quantum chemical calculations. It was found that the anion was primarily responsible for the separation effect, which grew as the carbon chain grew. Molecular dynamics simulations were used to verify the separation performance of the selected ILs. The separation effect of ILs was further verified using vapor–liquid phase equilibrium (VLE) experiments. The extractive distillation process with [PrMIM][AC] as the extractant was investigated to demonstrate the feasibility of ILs for the separation of butanol and DBE. This study analyzes the whole process from microscopic mechanism of action, which provides new ideas for the separation of alcohol‐ether systems.

Integrated Modeling and Optimization for Ethylene Production Processes Coupled with the Utility System

Integrated Modeling and Optimization for Ethylene Production Processes Coupled with the Utility System

SAFETY CONTROL SYSTEMS FOR ETHYLENE PRODUCTION

Ethylene production is a cornerstone of the petrochemical industry, with a global demand that continues to rise. Ensuring efficient, safe, and environmentally responsible ethylene production processes is paramount. Ethylene production control systems, encompassing a range of technologies and strategies, play a pivotal role in achieving these objectives. This abstract provides a concise overview of key aspects of ethylene production control systems, their challenges, and their critical importance. Ethylene production control systems serve as the backbone of modern ethylene plants, orchestrating complex operations to meet production targets while maintaining product quality. These systems integrate advanced technologies such as distributed control systems (DCS), safety instrumented systems (SIS), and process optimization tools to manage variables like temperature, pressure, flow rates, and feedstock composition. Efficiency and yield optimization are central objectives in ethylene production control. Control strategies are designed to maximize product output while minimizing energy consumption and raw material wastage. Additionally, safety control systems are a crucial component, mitigating risks associated with the highly flammable nature of ethylene and ensuring the well-being of personnel and environmental protection. Challenges in ethylene production control systems include managing the variability in feedstock quality, adapting to changing market demands, and adhering to stringent environmental regulations. As feedstock composition can fluctuate, control systems must continually adjust to maintain product quality and consistency. The emergence of Artificial Intelligence (AI) has brought new dimensions to ethylene production control. AI-driven predictive maintenance, anomaly detection, and process optimization are being explored to enhance operational efficiency and reduce downtime. In summary, ethylene production control systems are pivotal in sustaining the global supply of ethylene, a vital chemical compound. Their roles extend beyond production to safety and environmental compliance, making them indispensable components in the petrochemical industry's pursuit of efficiency and sustainability. To meet growing demands and address evolving challenges, the integration of advanced technologies, such as AI, holds promise for the future of ethylene production control systems. A strong safety culture is the backbone of safety control in ethylene production. This involves fostering a work environment where safety is paramount, and all personnel are equipped with the knowledge, awareness, and commitment to safety objectives. Clear communication and continuous training ensure that safety becomes an integral part of the organizational DNA. These systems represent a synergy of hardware, software, and operational protocols meticulously designed to monitor, detect, and respond to anomalous conditions, with a primary objective of averting incidents that could culminate in equipment damage, environmental harm, or harm to personnel. Keywords: Ethylene Production, Control Systems, Petrochemical Industry, Efficiency, Safety, Environmental Responsibility.

Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization.

As greenhouse gases such as CO2 continue to promote global warming, the reduction of CO2 emissions is attracting increasing attention. In this study, we design a process for producing dimethyl ether (DME), which is a promising means of using CO2 as a resource. Design variables such as temperature and pressure need to be optimized to reduce CO2 emissions while maintaining high product purity and DME production. Conventional process designs determine these design variables from the chemical background and through trial-and-error simulations, which are very time-consuming. The proposed method optimizes the design variables efficiently by repeating the process simulations and selecting promising candidates for the design variables using machine learning. For an adaptive design of experiments, Bayesian optimization is used to achieve the objectives of the DME process while efficiently optimizing the design variables. In addition, we also optimize the design variables considering variations in the temperature and pressure data, meaning robust Bayesian optimization. The proposed method successfully identifies design variables that satisfy all experimental targets in an average of 54 simulations while achieving 100% of the targets with product purity 0.95-1.00, amount of DME in the product 350-845 kmol/h, and CO2 emissions 0-835 kmol/h, confirming the effectiveness of the proposed robust Bayesian optimization method.

Technical and economic analysis of ethylene production process with considering energy and water minimization through pinch technique

Technical and economic analysis of ethylene production process with considering energy and water minimization through pinch technique

Distributed Optimization Subject to Inseparable Coupled Constraints: A Case Study on Plant-Wide Ethylene Process

Plant-wide optimization plays a vital role in improving the overall performance of large-scale industrial processes. Considering the modeling complexity and convergence difficulty of centralized plant-wide optimization, this paper proposes a distributed framework by decomposing the global optimization problem into a set of subproblems, where multiple local units interact with each other between nodes. According to the proposed framework, plant-wide optimization problem can be effectively solved by distributed optimization. To eliminate the limitations of existing distributed algorithms, we introduce constraint node to describe the inseparable coupled constraints between nodes. By combining Lagrange duality and parameter projection, the proposed algorithm can solve optimization problems with multiple constraints. Taking ethylene production process as an example, the global energy consumption optimization is guaranteed without the whole-process mechanism model. Numerical simulation and industrial experiment results demonstrate that the proposed algorithm can reduce the energy consumption of the entire ethylene process with fewer computation time.

Dynamic material flow analysis of Chinese ethylene production processes and optimal pathway exploration with potential environmental-economic impacts

China is expected to become the largest ethylene producer globally; environmental problems related with traditional ethylene production process of Chinese petrochemical industry will be further stern. This study aims to propose a feasible and optimal ethylene production expansion pathway to improve the trade-off between the environment and economy. A novel approach was adopted with material-energy-value flow analysis integration by combining dynamic scenario analysis. According to the results of scenario analysis, cleaner and lightweight production processes optimization achieved by adopting the alternative ethylene production process mix alleviated environmental pressure and achieved a weak decoupling of the low-carbon economy. A 26.1% increase in carbon productivity was achieved compared with the baseline scenario, whereas the coal to ethylene route should be phased-out, as more than 90% of the pollutants are generated by 10%–17% of the coal to ethylene facilities. However, CO2 emissions from ethylene will inevitably increase along with doubling ethylene production. An imbalance in pollutant generation also emerges if large-scale naphtha is exclusively promoted for ethylene plant implementation. The study provides methodological support for investigating feasible roadmaps for key product capacity expansion, and has practical meaning for high-quality industrial development domestically and globally.

Anchoring ultrasmall Pd nanoparticles by bipyridine functional covalent organic frameworks for semihydrogenation of acetylene.

Acetylene hydrogenation is a well-accepted solution to reduce by-products in the ethylene production process, while one of the key technical difficulties lies in developing a catalyst that can provide highly dispersed active sites. In this work, a highly crystalline layered covalent organic framework (COF) material (TbBpy) with excellent thermal stability was synthesized and firstly applied as support for ultrasmall Pd nanoparticles to catalyze acetylene hydrogenation. 100% of C2H2 conversion and 88.2% of C2H4 selectivity can be obtained at 120 °C with the space velocity of 70 000 h-1. The reaction mechanism was elucidated by applying a series of characterization techniques and theoretical calculation. The results indicate that the coordination between Pd and N atom in the bipyridine functional groups of COFs successfully increased the dispersibility and stability of Pd particles, and the introduction of COFs not only improved the adsorption of acetylene and H2 onto catalyst surface, but enhanced the electron transfer process, which can be responsible for the high selectivity and activity of catalyst. This work, for the first time, reported the excellent performance of Pd@TbBpy as a catalyst for acetylene hydrogenation and will facilitate the development and application of COFs materials in the area of petrochemicals.

Multi-scale energy efficiency recognition and diagnosis scheme for ethylene production based on a hierarchical multi-indicator system

It is essential to monitor and diagnose the energy efficiency level of ethylene production process to detect any abnormities in the production and adjust production parameters appropriately to ensure the energy efficiency. However, few studies have been carried out concerning the diagnosis of abnormal energy efficiency in the ethylene production with appropriate energy efficiency indicator system from multiple dimensions due to the incompatibility of the diagnosis algorithm and the complexity of this petrochemical production. Therefore, to achieve a comprehensive energy efficiency diagnosis and anomaly analysis for the ethylene production, a multi-scale energy efficiency recognition and diagnosis scheme based on a hierarchical multi-indicator system is proposed. Firstly, a novel recognition scheme for abnormal energy efficiency is conducted from a single-indicator scale to a multi-indicator scale based on a three-level indicator system with respect to the energy and materials flows in the system, process, and equipment level of the ethylene production, so that the energy efficiency of the ethylene production can be monitored in different levels and granularities. Secondly, an integration-decomposition diagnosis method to analyze the energy efficiency is proposed based on the Malmquist production index, where the overall efficiency is decomposed into production efficiency, pure production technical efficiency, and scale efficiency to form a multi-perspective in-depth diagnosis and exploration. Thirdly, a principal component analysis is converted into a preliminary abnormal cause diagnosis algorithm based on statistics so that the multi-scale and multi-level energy efficiency recognition and diagnosis for the ethylene production is achieved. Finally, the effectiveness of the diagnosis scheme is verified by applying it to a Chinese ethylene plant, and the results show that the abnormal energy efficiency can be identified effectively and the consistency of the diagnosis results with the actual situation is approximately 0.818 at least.

Novel long short-term memory neural network considering virtual data generation for production prediction and energy structure optimization of ethylene production processes

Production optimization and energy efficiency improvement can help realize the energy conservation and carbon emission reduction in the process industry. However, the amount of statistical data acquired in the process industry is relatively small, which is not conducive to production optimization modeling and analysis. Therefore, this paper proposes a novel production prediction and energy structure optimization model based on the long short-term memory neural network (LSTM) combining the Monte Carlo (MC) (MC-LSTM). The MC method can expand the real production data as the input of the LSTM model to realize the production prediction. At the same time, indicators of inefficient samples can be optimized for higher energy efficiency by analyzing the result of the MC-LSTM model. Finally, the proposed model is applied to predict the production and optimize the energy structure of ethylene plants in the process industry. The experiment shows that the prediction accuracy of the ethylene production process based on the proposed model is about 96.57%, which is better than other prediction models, and the energy-saving potential is 13.22% approximately.

Molecular insights into azeotrope separation in the methyl tert-butyl ether production process using ChCl-based deep eutectic solvents

Deep eutectic solvents (DESs) were first applied in the azeotrope separation process for the production of methyl tert-butyl ether (MTBE). Quantum chemistry and molecular dynamics simulations were used to explore the interaction mechanism of ChCl-based DESs in the liquid–liquid extraction of the MTBE–methanol (MeOH) system. The results showed that the extraction of MeOH by DESs mainly depended on the Van der Waals interactions between Cl atoms in ChCl and H atoms in the MeOH hydroxyl group. Hydrogen bond donors also enhanced the extraction effect to a certain extent. Liquid-liquid equilibrium experiments were carried out for the three selected DESs, and the ideal separation effect was obtained. The complete separation process of the MTBE-MeOH system was developed based on experimental data. This work demonstrates the application of DESs in the MTBE-MEOH system and provides guidance for the selecting and analysis of other DES extractants for the separation of low-carbon alcohol systems.

Electrified steam cracking for a carbon neutral ethylene production process: Techno-economic analysis, life cycle assessment, and analytic hierarchy process

Electrification is regarded as one of the solutions for decarbonization. To reduce the emissions of carbon dioxide from the steam cracking which is a conventional ethylene production process, the electrical heating furnace can be applied to the steam cracking called the electrified furnace. Here, to determine whether the electrified steam cracking is proper for carbon neutrality, the techno-economic analysis, life cycle assessment, and the analytic hierarchy process were implemented. The analysis was proceeded by the fourteen cases divided according to steam cracking, electrified steam cracking, and seven electricity generation methods. As a result, considering the technology development level, economic feasibility, and environmental impact, the best case was the steam cracking with hydropower, and the electrified steam cracking with hydropower, solar power, and wind power had high potential. As improving the technology development of the electrified furnace, the electrified steam cracking can be expected to become the proper ethylene production process to achieve carbon neutrality.

Techno-economic viability study of an ethylene production process through ethane steam craking

Techno-economic viability study of an ethylene production process through ethane steam craking

Shell, Dow test electric cracking

Dow and Shell have started up a small unit in Amsterdam to test their electric steam cracking concept. The companies hope to generate data that will allow them to retrofit gas-fired furnaces. They aim to deploy a larger, multimegawatt pilot plant by 2025. Ultimately, the firms say, powering an ethylene cracker with renewable electricity could reduce the emissions related to the ethylene production process by 90%.

Shaping of metal–organic frameworks through a calcium alginate method towards ethylene/ethane separation

The separation of ethylene and ethane is a crucial, challenging and cost-intensive process in chemical engineering. Metal–organic frameworks (MOFs) are a class of novel porous adsorbents used for the separation of ethylene/ethane mixtures. However, MOFs are normally crystalline powders that cause multiple problems, such as dust, abrasion and heat/mass loss, as well as significant pressure drops on the adsorption bed resulting in a sudden stop in production. To solve these issues, we have prepared four different sphere-shaped adsorbents, including Mg-gallate, Co-gallate, MUV-10(Mn) and MIL-53(Al) using a calcium alginate method to achieve excellent ethylene/ethane separation performance. The performance of the sphere-shaped adsorbents has been validated using mechanical strength measurements, powder X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, gas adsorption isotherms and dynamic breakthrough experiments. The excellent mechanical strength of these sphere-shaped adsorbents meets the criteria for industrial application in gas separation. Thus, the energy consumption and operating cost will be further reduced in the ethylene production process. We believe that this shaping method will open a prosperous route to the development of MOFs toward higher technology levels and their commercial application.

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.

Improved distributed optimization algorithm and its application in energy saving of ethylene plant

This paper investigates an energy optimization problem for ethylene production process without system dynamics. Fundamentally different from the conventional centralized optimization strategies, the energy optimization in the ethylene production process is transformed into a distributed optimization problem with input constraints due to multiple interconnected units involved. Based on the combination of consensus mechanism and gradient tracking technique, a distributed algorithm with adaptive step-sizes is derived to optimize the coil outlet temperature (COT) and steam hydrocarbon ratio (SHR) in the thermal cracking process as well as the temperature and duty in the product separation process. Besides, considering the mechanism limitation, a projection operation is adopted to deal with input constraints. The experimental results show that the proposed distributed algorithm is superior to the centralized method in terms of computational efficiency and robustness with a faster convergence speed.

Three-phase electrochemistry for green ethylene production

The electrochemical conversion of greenhouse gases (mainly CO2 and CH4) into ethylene has attracted worldwide attention. Compared with thermal cracking and dehydrogenation ethylene production processes, electrochemical ethylene production is an energy-saving and environmentally friendly process with high atom and energy economies. Great efforts have been made in enhancing the performance of electrochemical COx reduction and alkane dehydrogenation reactions in recent years. The complicated interactions between gas reactants, electrolytes, and catalysts force the three-phase interface mass transfer process an important issue in determining the electrochemical activity and product selectivity. Herein, we summarize the recent progresses on electrochemical ethylene production. Special attention has been paid to the principles for the design of gas–liquid–solid and gas–solid–solid three-phase interfaces and their influence on the electrochemical COx reduction and alkane dehydrogenation reactions. The comprehensive understanding of those different ethylene production reactions together from the perspective of the three-phase interface-related mass transfer process would provide new insights into the design of advanced electrochemical cells for green ethylene production.

Production capacity assessment and carbon reduction of industrial processes based on novel radial basis function integrating multi-dimensional scaling

The high energy consuming and carbon emission of complex industrial processes has become a huge challenge all over the world. Therefore, a novel production capacity assessment and carbon reduction modeling technology of industrial processes is presented in this paper by using radial basis function (RBF) neural network integrating multi-dimensional scaling (MDS) (MDS-RBF). The factors affecting the production capacity can be obtained in the low-dimensional space by using the MDS method, which can maintain the same distance in the original multi-dimensional space. Then the main influencing factors are obtained by analyzing the top largest eigenvectors in low-dimensional space. After, the main influencing factors are set as training sets of the RBF to build the production capacity assessment and carbon reduction model. The factors after the MDS process can decouple the original factors effectively, which greatly improving the prediction performance of the RBF. Compared with the RBF, back propagation (BP) neural network, and BP integrating MDS (MDS-BP), the prediction accuracy of the presented model is verified by University of California Irvine datasets (UCI). At last, the introduced modeling technology is applied to the ethylene production processes, which is great helpful to the energy optimization and carbon emission analysis.