Ethylene Plant Research Articles

Two-level comprehensive energy-efficiency quantitative diagnosis scheme for ethylene-cracking furnace with multi-working-condition of fault and exception operation

Because ethylene-cracking furnace accounts for vast majority of energy consumption in ethylene production, energy-efficiency diagnosis of ethylene-cracking furnace is of great significance for improving energy utilization and production operation. In this paper, a two-level comprehensive energy-efficiency diagnosis problem is considered for both furnace level and internal chambers level. Apart from production load and feed composition, fault and exception operation also have influence on energy-efficiency, which leads to a multiple-working-condition energy-efficiency diagnosis problem under fault and exception operation. Therefore, this paper proposes a new energy-efficiency diagnosis scheme for ethylene-cracking furnace. Firstly, a two-level index system is designed to have an overall understanding of energy-efficiency of ethylene-cracking furnace and internal chambers. Secondly, fault and operation diagnosis criteria are established to identify fault samples, exception operation samples, and fault-and-exception-operation samples from inefficient samples after multi-working-condition classification. Thirdly, contributions of operation conditions, outputs, and internal chambers are quantified by step-by-step transformation and total differential methods to locate weak links in energy efficiency. Finally, effectiveness of diagnosis scheme is verified by applying it to a Chinese ethylene plant. Not only are three inefficient types detected, but also contributions of root causes resulting in inefficiency are quantified, which provides energy conservation and efficiency improvement suggestions for decision-makers.

Efficient scale-up synthesis and hydrogen separation of hollow fiber DD3R zeolite membranes

Efficient hydrogen (H2) separation is highly desired from the cracked tail gas of ethylene plant. In this work, DD3R zeolite membranes have been evaluated for potential application in H2 separation under harsh conditions. We developed an efficient scale-up synthesis of hollow fiber DD3R zeolite membranes. The alkalinity of synthesis solution was optimized to endow the good reproducibility of high-quality membranes. 17 pieces of separate membranes, total 406 cm2, were synthesized in one autoclave using the industrial grade agents. Typically, the 25 cm long membrane showed CO2/CH4 selectivity of 447 and CO2 permeance of 1.55×10−7 mol m−2 s−1 Pa−1. The single component permeance of H2 is 1.8 × 10−8 mol m−2 s−1 Pa−1 at 303 K and feed pressure of 0.4 MPa, leading to the ideal selectivity of 124 for H2/CH4, 242 for H2/C2H4 and 424 for H2/C2H6. For the simulated cracked gas, H2 permeance and H2/CH4 separation selectivity was 1.25 × 10−8 mol m−2 s−1 Pa−1 and 128, respectively. More importantly, the DD3R zeolite membrane was stable for more than 1000 h in the simulated cracked gas containing 100 ppm H2S. Together with the linear increase of H2 flux and H2/CH4 separation selectivity of 50 at high pressure of 2.1 MPa, DD3R zeolite membranes pave the way to H2 separation from ethylene industry.

Energetic, economic and environmental (3E) multi-objective optimization of the back-end separation of ethylene plant based on adaptive surrogate model

Ethylene separation is an important part in olefin production process, but it brings about high energy consumption and emissions. Therefore, the energetic, economic and environmental (3E) multi-objective optimization of ethylene separation process is of great significance for the sustainable development of olefin industry. Regarding the high computation cost of conventional optimization approach based on process simulation model, a novel optimization framework named multi-objective adaptive surrogate model assisted optimization (MOASO) is introduced. In this framework, adaptive sampling based on sparsity and IGGD improvement is proposed, aiming at promoting the accuracy of surrogate model successively as optimization proceeds. In addition, a modified non-dominant sorting genetic algorithm-II (NSGAII) embedding a density-based local search operator is developed for evolutionary optimization. The MOASO is applied to typical test functions and a practical ethylene separation process. The results show that the proposed framework has highly acceptable optimization performance and computational efficiency, reducing 72% of the calculation burden. Compared with the actual operating condition, the energy consumption and CO2 emission can be reduced by 3.5 × 104 GJ/year and 3.81 × 105 kg/year while increasing the annual gross profit by 4.36 × 105 $/year under a typical optimized condition. In general, the proposed framework can be used as an effective tool for multi-objective optimization, from which insights for clean production and sustainable development in complex large-scale chemical processes could be gained.

Heat-Integrated Distillation Columns as a Retrofit Cannot Bring Energy Savings to the Ethylene Plant

Separation by distillation is an energy-intensive process. Approximately 40% of energy use in the petrochemical industry is associated with separation by distillation. The heat-integrated distillation column (HIDiC) is a process intensification technology, which aims to reduce the energy required by the distillation process. A study was performed to determine the energy savings that could be achieved by using HIDiC as a retrofit of two critical distillation columns in the ethylene process (ethylene fractionator and deethanizer). The aim was to assess the energy savings of an individual HIDiC and relate it to the energy consumption of the plant as a whole. The outcome of this study is that HIDiC could lower the energy demand, but this itself did not translate into energy savings for the overall plant in either case. The HIDiC ethylene fractionator increased the energy consumption of the overall plant by 16% and the HIDiC deethanizer by 50%. The reasons for this are discussed thoroughly in this article.

Optimization of Ethylene Refrigeration System Using Genetic Algorithms Method

Ethylene refrigeration for gases separation at low temperature and high pressure for olefin production is an important technique in the chemical industry. Since small changes in the operating conditions of such a process can have a significant influence on its economics, optimization is desirable. The present work was aimed to propose and establish a mathematical model for the ethylene refrigeration system of the ethylene plant in Basrah petrochemical complex NO.1 (PC1) and reformulated as a geometric programming problem using Visual Basic for predicting:- (overall efficiency of the ethylene refrigeration system {% }and percent of energy saving %E). Through the formulated model shaft work consumption by the centrifugal compressor, refrigeration effect and coefficient of performance of the system were obtained and other parameters concerning the system. The results of simulation showed a good agreement with the manufacturer manual.In this study the effect of four factors as independent variables on the overall refrigeration system efficiency and percent of energy saving were studied ;evaporator low pressure (PL) in the range of (1-3)bar ,compressor discharge high pressure (Ph) in the range of ( 28-32)bar ,condenser degree of sub-cool temperature (Tsub-D) in the range of (6-22)0C and evaporator degree of superheat temperature (Tsup-D) in the range of (1-5)0C. And the optimum conditions that aimed to minimize the thermodynamic irreversibility i.e (maximize overall refrigeration system efficiency) and also lower operating cost i.e (maximize percent of energy saving) evaporator low pressure (PL) (2.8 bar), compressor discharge high pressure (Ph) (28.7 bar), condenser degree of sub-cool temperature (Tsub-D) (190C), and evaporator degree of superheat temperature (Tsup-D) (3.40C). At these conditions the overall refrigeration system efficiency is (81.8%) and percent of energy saving is 51.18% with respect to conditions in the factory.

Optimal design of ethylene and propylene coproduction plants with generalized disjunctive programming and state equipment network models

In this work, we propose a superstructure optimization approach for the optimal design of an ethylene and propylene coproduction plant. We formulate a superstructure that embeds ethane and propane steam cracking technologies, propane dehydrogenation and olefin metathesis processes. We represent the superstructure with a Generalized Disjunctive Programming model, and solve the problem through a custom implementation of the Logic-based Outer Approximation algorithm in GAMS. We propose a state-equipment-network representation to model potential distillation trains, as well as alternative acetylene reactor configurations. Rigorous models are formulated for distillation columns, compressors, turboexpanders, vessels and several process equipment units. The objective function is to maximize the net present value. We analyze four international price scenarios for raw material and utility costs, while considering global ethylene and propylene prices. We obtain the optimal scheme for each case. Numerical results show that the combination of ethane steam cracking, olefin metathesis and ethylene dimerization is the most profitable configuration under low ethane price scenarios, whereas the combination of ethane and propane steam cracking together with propane dehydrogenation is the optimal solution when the propane price is on the order of ethane price.

Data-Driven Modeling and Cyclic Scheduling for Ethylene Cracking Furnace System with Inventory Constraints

The optimization of cyclic scheduling for an ethylene cracking furnace system (ECFS) is beneficial for ethylene plants. The scheduling problem involves multiple feeds, tanks, furnaces, and differen...

OPTIMIZATION OF ETHYLENE PLANT FRACTIONATION PROCESS

Research and Study of Ethylene Plant Fractionation Process

Ethylene signaling in rice and Arabidopsis: New regulators and mechanisms.

Ethylene is a gaseous hormone which plays important roles in both plant growth and development and stress responses. Based on studies in the dicot model plant species Arabidopsis, a linear ethylene signaling pathway has been established, according to which ethylene is perceived by ethylene receptors and transduced through CONSTITUTIVE TRIPLE RESPONSE 1 (CTR1) and ETHYLENE-INSENSITIVE 2 (EIN2) to activate transcriptional reprogramming. In addition to this canonical signaling pathway, an alternative ethylene receptor-mediated phosphor-relay pathway has also been proposed to participate in ethylene signaling. In contrast to Arabidopsis, rice, a monocot, grows in semiaquatic environments and has a distinct plant structure. Several novel regulators and/or mechanisms of the rice ethylene signaling pathway have recently been identified, indicating that the ethylene signaling pathway in rice has its own unique features. In this review, we summarize the latest progress and compare the conserved and divergent aspects of the ethylene signaling pathway between Arabidopsis and rice. The crosstalk between ethylene and other plant hormones is also reviewed. Finally, we discuss how ethylene regulates plant growth, stress responses and agronomic traits. These analyses should help expand our knowledge of the ethylene signaling mechanism and could further be applied for agricultural purposes.

Interfering Peptides Targeting Protein-Protein Interactions in the Ethylene Plant Hormone Signaling Pathway as Tools to Delay Plant Senescence.

Interfering peptides (iPs) have been recognized as valuable substances to specifically target protein-protein interactions (PPIs) in senescence and disease. Although the concept of iPs has been validated for several PPIs in medical and pharmaceutical research, little attention so far has been paid to the enormous potential iPs that may provide to target and control plant growth and developmental processes or plant environmental responses. However, recent research on PPIs in the ethylene signaling pathway has identified the synthetic peptide NOP-1 derived from the nuclear localization signal of ethylene regulator EIN2 as an efficient inhibitor of typical ethylene responses such as ripening, aging, and senescence. Biophysical and biochemical studies on purified recombinant proteins of the ethylene receptor family from various plant species demonstrate that the synthetic peptide binds in the nM-μM range at the plant target. Here, we describe methods to evaluate and quantify the effect of the NOP-1 peptide on flower senescence as a typical ethylene response in the intact plant system. This approach will help to systematically advance our technological capability to delay plant ethylene responses and to expand shelf-life or vase life of fruits and flowers.

Energy efficiency enhancement of energy and materials for ethylene production based on two-stage coordinated optimization scheme

Nowadays with the growing requirements of sustainable development, energy efficiency enhancement plays a crucial role in the petrochemical industries. In fact, the energy efficiency level depends on the coordination management of energy and materials. Therefore, a two-stage optimization scheme with respect to coordination of energy and materials from the entire process to the key sub-process is proposed for improving energy efficiency level of ethylene production. Firstly, a data-driven model for feedstock and plant-level economic indicators and is established by extreme learning machine (ELM) integrated domain adaptive manifold regularization (DAMR) and particle swarm optimization (PSO), called DAMR-PSO-ELM. Secondly, a multi-objective optimization model for feedstock proportions is established and a cascading-priority weight strategy is made for obtaining the optimal feedstock proportions under the different market demands. Finally, fuel–feedstock ratio optimization models for cracking furnaces are built with respect to the obtained optimal proportions, and an improved elitist teaching–learning-based optimization algorithm is proposed to acquire the optimal fuel. The effectiveness and feasibility of the proposed coordinated optimization scheme are validated from a practical ethylene plant, results show that energy consumption of cracking production is reduced by 4.89% and energy efficiency of the entire ethylene process is increased by 6.82% on average.

Exergy analysis and multi-objective optimisation for energy system: a case study of a separation process in ethylene manufacturing

In chemical industry, most processes face the challenge of high energy consumption. The approach presented in this study can reduce the energy footprint and increase efficiency. The energy system of a separation process in ethylene manufacturing is used to demonstrate the effectiveness of the approach. The chilling train system of the separation process in a typical ethylene plant consumes most cooling and provides appropriate feed for distillation columns. The steady state simulation of system was presented and the simulation results were proved accurate. The conventional exergy analysis identifies that Dephlegmator No.1 (a heat exchange and mass transfer device) has the highest exergy destruction (1401.28kW). Based on advanced exergy analysis, Dephlegmator No.1 has the highest rate of avoidable exergy destruction (89.04 %). Finally, a multi-objective optimisation aiming to maximise system exergy efficiency and to minimise operational cost was performed and the Pareto frontier was obtained. The multi-objective optimized exergy efficiency is 79.53 % (improved by 0.61 %) and the operational cost is 0.02031 yuan/kg (saved by 11.19 %). This study will guide future research to reduce energy consumption in process manufacturing.

Targeted plant hologenome editing for plant trait enhancement.

Summary Breeding better crops is a cornerstone of global food security. While efforts in plant genetic improvement show promise, it is increasingly becoming apparent that the plant phenotype should be treated as a function of the holobiont, in which plant and microbial traits are deeply intertwined.Using a minimal holobiont model, we track ethylene production and plant nutritional value in response to alterations in plant ethylene synthesis (KO mutation in ETO1), which induces 1‐aminocyclopropane‐1‐carboxylic acid (ACC) synthase 5 (ACS5), or microbial degradation of ACC (KO mutation in microbial acdS), preventing the breakdown of the plant ACC pool, the product of ACS5.We demonstrate that similar plant phenotypes can be generated by either specific mutations of plant‐associated microbes or alterations in the plant genome. Specifically, we could equally increase plant nutritional value by either altering the plant ethylene synthesis gene ETO1, or the microbial gene acdS. Both mutations yielded a similar plant phenotype with increased ethylene production and higher shoot micronutrient concentrations. Restoring bacterial AcdS enzyme activity also rescued the plant wild‐t8yp phenotype in an eto1 background.Plant and bacterial genes build an integrated plant–microbe regulatory network amenable to genetic improvement from both the plant and microbial sides.

Energy efficiency assessment and resource optimization using novel DEA model: evidence from complex chemical processes

Energy efficiency assessment and resource allocation optimization are conducive to improve the production and reduce carbon emissions in complex chemical processes. And the traditional energy efficiency assessment method based on data envelopment analysis (DEA) does not distinguish the effective decision-making units (DMUs) better. Therefore, this paper proposed a novel DEA method combining the cosine similarity (CS) (DEA-CS) to estimate the energy efficiency and optimize the resource of complex chemical plants. The ineffective and effective DMUs can be obtained by the DEA. Then, the CS can further distinguish the effective DMUs to obtain the optimal DMU, which makes the DEA model have the better discriminating ability. Moreover, through the input and output of the optimal DMU, the resource of ineffective DMUs can be optimized. Finally, the proposed model is used in energy efficiency assessment and resource optimization of ethylene and purified terephthalic acid (PTA) production plants in complex chemical processes. The experiments show that the energy-saving potential of ethylene and PTA production plants is improved by about 13.61% and 1.22%, respectively. Meanwhile, the average carbon emission–saving potential of the ethylene production plants is reduced by 12.58%, approximately.

Input-output networks considering graphlet-based analysis for production optimization: Application in ethylene plants

Complex systems and massive amounts of data present a huge challenge for modern chemical enterprises, but traditional data statistical analysis and data mining methods have certain limitations. Therefore, this study proposes a production optimization methodology about input-output (I–O) network construction considering the graphlet representation. Maximal structure network is built by mechanism modeling considering I–O relationships and constraints. Then solution structure network is obtained by the direct consumption coefficient matrix of specific production data. The proposed I–O graphlets introduce process information, and different nodes represent different I–O factors in an I–O graphlet. Graphlet-based characterization and analysis can be achieved by networks and graphlets characteristics statistics. The ethylene plant I–O network is provided based on the monthly production data of 26 ethylene plants in China to optimize the ethylene production. Compared with the correlation network, the I–O network has a clear structure and physical meaning of links. Meanwhile, the hierarchical clustering based on I–O graphlets has a higher precision clustering effect than the generic graphlets. Furthermore, the ineffective ethylene plants can improve the energy efficiency and economic benefits according to the optimal production benchmark of the effective ethylene plant.

Mitigation of operational failures via an economic framework of reliability, availability, and maintainability (RAM) during conceptual design

Abnormal process situation may lead to tremendous negative impact on sustainability, wellbeing of workers and adjacent communities, company's profit, and stability of supply chains. Failure of equipment and process subsystems are among the primary causes of abnormal situations. The conventional approach in handling failure-based abnormal situations has usually focused on operational strategies. Such an approach overlooks the critical role of process design in mitigating failure, while simultaneously considering the effects of such failure on process economic performance. The aim of this work is to introduce a systematic methodology that accounts for failure early enough during the conceptual design stages. Once a base-case design is developed, the methodology starts by identifying the sources of failure that are caused by reliability issues including equipment, operational procedures, and human errors for a given process system or subsystem. This allows for the identification of critical process subsystem(s) that are more failure-prone or cause greater downtime than other subsystems. Bayesian updating and Monte Carlo techniques are utilized to determine the appropriate distributions for the failure and repair scenario(s), respectively, in question. Markov analysis is used to determine the system availability. Next, the process revenue is described as a function of inherent availability. The effects of failures are incorporated into profitability calculations to establish an economic framework for trading off failure and profitability. In the proposed framework, the economic potential of alternative design scenarios is evaluated and an optimization formulation with the objective of maximizing incremental return on investment (IROI) is utilized to make a design decision. A case study on an ethylene plant is solved to demonstrate the applicability and value of the proposed approach.

Consequence risk analysis using operating procedure event trees and dynamic simulation

Faults due to human errors cost the petrochemical industry billions of dollars every year and can have adverse environmental consequences. Unquantified human error probabilities exist during process state transitions performed each day by process operators using standard operating procedures. Managing the risks associated with operating procedures is an essential part of managing the overall safety risk. Additional operator training and safety education cannot eliminate all such faults due to human errors; therefore, we propose an operating procedure event tree (OPET) like analysis with branches and events specifically designed to perform risk analysis on operating procedures. The OPET method adapts event trees to analyze the risk due to human error while performing operating procedures. We consider human error scenarios during the procedure and determine the likely consequences by applying dynamic simulation. The modified event tree provides an estimate of the error frequencies.Operating procedure steps were developed, and potential operator faults were determined for two typical equipment switching procedures found in chemical plant operations. Then, dynamic simulation using Aspen HYSYS software was applied to determine the overpressure related consequences of each fault. Finally, the error frequencies resulting from those scenarios were analyzed using operating procedure event trees. We found that a typical ethylene plant gas header would overpressure with 0.6% frequency per manual dryer switch. Since dryer switches occur from every few days up to once per shift, these results suggest that dryer switching should be automated to ensure safe and environmentally friendly operation. Process dryer switching performed manually by operators opening and closing gate valves can be automated with control valves and a distributed control system. A sample distillation column was found to overpressure with 0.85% frequency per manual reflux pump switch.

ZPC selects Axens’ catalysts for Phase 2 ethylene plant at Zhoushan complex

ZPC selects Axens’ catalysts for Phase 2 ethylene plant at Zhoushan complex

Simulation and optimization of cold box and demethanizer system in an ethylene plant

Simulation and optimization of cold box and demethanizer system in an ethylene plant

Optimization of the ethane thermal cracking furnace based on the integration of reaction network

The cracking furnace is the core of an ethylene plant. An optimization method is built based on the integration of reaction network. Starting from atomic scope, an algebraic procedure is used to seek all possible reactions and establish the reaction network. Based on this, the reaction network is integrated and simplified to identify the variation rate of every component. The calculation procedure is developed to identify the variation of each component’s concentration and the selectivity and identify the optimal furnace parameters. For the studied furnace, the residence time corresponding to the minimum by-product production is identified to be 0.4 s, and its by-product generation is 4.3% less than that at the initial residence time (0.3 s).