Ethylene Plant Research Articles

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer nonlinear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables (MDNSGA-II) is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and mutation strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

Process Synthesis of Mixed Refrigerant System for Ethylene Plants

The refrigeration system is one of the most important and critical utility systems in the chemical and petrochemical industries. The traditional cascade refrigeration system (CRS) has been used for decades in ethylene plants. However, the mixed refrigerant system (MRS) with higher energy efficiency and less capital expenses than CRS has not widely been applied in ethylene plants. In this paper, a general methodology has been proposed for the synthesis of a tertiary refrigerant of MRS containing a mixture of methane, ethylene and propylene that can satisfy all the cooling/heating refrigeration that the traditional ethylene–propylene CRS serves in an ethylene plant. The exergy-temperature chart combined with the exergy analysis is presented to analyze comprehensively the thermodynamic nature of both CRS and MRS. Furthermore, the comparison of economic performances of these two different systems is also conducted. The efficacy of the developed MRS methodology has been demonstrated by a case study, where the ...

A thermal design method for the performance optimization of multi-stream plate-fin heat exchangers

An optimization design method based on field synergy principle is developed for Multi-stream plate-fin heat exchangers (MPHEs) with a segmented differential model. The heat exchanger is divided into a number of sub-exchangers along the main stream, and each sub-exchanger consists of N passages along the height of the exchanger. Compared with the traditional heat exchanger design, this method allows temperature and pressure fields to be obtained via coupling calculation with consideration of variable physical properties and the axial heat loss of the heat exchanger. Finally, the heat exchanger is optimally designed using a temperature-difference uniformity optimization factor based on field synergy principle. This design model can provide an accurate temperature field and pressure field, because the stream properties are determined by the mean temperature and pressure of each local sub-exchanger. Optimum results indicate that the temperature distribution on the cross section of the heat exchanger is relatively uniform and that the temperature difference of heat transfer for each stream is always a small value. These characteristics prove the feasibility and effectiveness of this design model. In this paper, a case of five stream plate-fin heat exchangers for an ethylene plant is designed under a practical cold box operating condition with the proposed model, the structure and heat transfer of which are optimally determined. The design model and optimization method proposed in this work can provide theoretical and technical support to the optimization design of MPHEs.

Molecular association of Arabidopsis RTH with its homolog RTE1 in regulating ethylene signaling.

The plant hormone ethylene affects many biological processes during plant growth and development. Ethylene is perceived by ethylene receptors at the endoplasmic reticulum (ER) membrane. The ETR1 ethylene receptor is positively regulated by the transmembrane protein RTE1, which localizes to the ER and Golgi apparatus. The RTE1 gene family is conserved in animals, plants, and lower eukaryotes. In Arabidopsis, RTE1-HOMOLOG (RTH) is the only homolog of the Arabidopsis RTE1 gene family. The regulatory function of the Arabidopsis RTH in ethylene signaling and plant growth is largely unknown. The present study shows Arabidopsis RTH gene expression patterns, protein co-localization with the ER and Golgi apparatus, and the altered ethylene response phenotype when RTH is knocked out or overexpressed in Arabidopsis. Compared with rte1 mutants, rth mutants exhibit less sensitivity to exogenous ethylene, while RTH overexpression confers ethylene hypersensitivity. Genetic analyses indicate that Arabidopsis RTH might not directly regulate the ethylene receptors. RTH can physically interact with RTE1, and evidence supports that RTH might act via RTE1 in regulating ethylene responses and signaling. The present study advances our understanding of the regulatory function of the Arabidopsis RTE1 gene family members in ethylene signaling.

Dow unveils $4 billion in new projects

At its annual shareholders meeting last week, Dow Chemical announced a five-year, $4 billion round of investments on the U.S. Gulf Coast and beyond. The company will add two furnaces at an ethylene cracker set to open later this year in Freeport, Texas. The additional equipment will expand the cracker’s capacity by one-third to 2 million metric tons per year, making it the largest ethylene plant in the world, Dow says. The company will also build a 600,000-metric-ton polyethylene plant on the Gulf Coast. Improvement projects at existing plants, mostly in North America, will yield another 350,000 metric tons of polyethylene capacity. Dow is also building a 450,000-metric-ton polyolefins plant in Europe to serve the high-pressure pipe market. It will be the first new polyolefins plant Western Europe has seen since the late 2000s. In recent years, capital spending in petrochemicals has favored lower-cost regions such as North America—with feedstock

Nova to buy Williams’s ethylene plant

In its second big U.S. Gulf Coast deal in as many months, the Canadian petrochemical maker Nova Chemicals has agreed to buy Williams Partners’ Geismar, La., ethylene cracker and related assets for $2.1 billion. Nova will get Williams’s 88% interest in the Geismar cracker, which in 2015 was expanded by 40% to a capacity of 900,000 metric tons per year. Saudi Basic Industries Corp. owns the other 12%. Also included in the deal is Williams’s interest in an ethylene trading hub in Mont Belvieu, Texas, and more than 200 hectares of undeveloped adjacent land that Nova says “represents a significant opportunity for future growth.” Last month, Nova formed a partnership with its European sister company, Borealis, and the French firm Total to build a $1.7 billion ethylene cracker near Total’s refinery in Port Arthur, Texas, and a polyethylene plant in Bayport, Texas. Nova makes ethylene and polyethylene in Joffre, Alberta,

Another ethylene plant to rise on the Gulf

In yet another petrochemical venture meant to take advantage of cheap U.S. shale gas, French oil giant Total is joining with Canada’s Nova Chemicals and Austria’s Borealis to build a $1.7 billion ethylene cracker and a polyethylene plant, both in Texas. The cracker will have 1 million metric tons per year of capacity. It will be located in Port Arthur, adjacent to a Total refinery and an ethylene cracker joint venture that Total and BASF started up in 2001. The polyethylene plant, in Bayport, will have 625,000 metric tons of capacity and will be based on Borealis’s Borstar technology. The three-way partnership will also include a 400,000-metric-ton-per-year polyethylene facility in Bayport that Total currently owns and operates. Total, which will own half of the venture, has been contemplating the ethylene cracker since 2013 and has permits in hand from the Texas Commission on Environmental Quality. The companies expect to make

Operator Training Simulator for Ethylene Plant

In a petrochemical plant, training new operators to operate plant safely and consistent is a need. Conventional training for operators relies on senior operators' experience. When experience operator leaves or resigns, it will take longer duration to train new personnel to gain the equivalent experience of previous or senior operators. The operators control the plant by using Distributed Control System (DCS) as an interface and ensure the reliability and productivity of plant sustains. The more challenges they face, the more experience they gain. Most of the operators are trained by real time events they faced on the job but errors or plant upsets should be avoided. It is estimated that in the United States alone, 10 billion USD is lost annually in the process industry caused by errors when faced with plant upsets. Operator training plan is therefore the crucial step to reduce the losses as well as the risk of operating the plant. Operator Training Simulator (OTS) is an alternative solution to safely provide the operators with various realistic operational conditions of the plant, including start-up, shutdown, and emergency conditions. This article discusses on the methodology of building an OTS for Ethylene plant, that are beneficial for educating operator response when faced with critical process upsets and succeeding quicker start-up. A dynamic process model was developed to give accurate dynamic behaviour of the plant process mainly during start-up, shutdown and upset conditions. Testing and verification processes of the models such as Model Acceptance Test (MAT), Factory Acceptance Test (FAT) and Site Acceptance Test (SAT) were deliberated. Few issues or problems faced during the testing were also discussed. Based on the experience of developing the simulator, process model was developed using mass and energy balance method does not reflect the actual plant conditions. It might be suitable for commissioning a new plant. However, once the plant is started all the parameters need to be realigned to meet the actual plant data. Process model for existing plant should always apply the actual plant data as a primary data. Especially during MAT, FAT and SAT processes, experience of the panel man is the best input data to get the best output of the simulations.

Energy and environmental efficiency evaluation based on a novel data envelopment analysis: An application in petrochemical industries

Petrochemical industry is a high energy consumption and heavy pollution industry. Therefore, energy and environmental efficiency evaluation becomes extremely important to achieve sustainable development of the petrochemical industry. This paper proposes a novel DEA model to estimate the energy and environmental efficiency of the petrochemical industry thoroughly. The proposed method introduces the efficiency variable to each energy input and undesirable output and uses environmental performance index (EPI) to represent the overall performance of different decision making units (DMUs). Moreover, the scores of efficiency variables reflect the environmental performance of energy inputs and undesirable outputs, and the larger score represents the greater performance of the DMUs. Meanwhile, the EPI synthesizes the environmental efficiency value of energy inputs and undesirable outputs to help us grasp the energy and environmental comprehensive performance of DMUs, which also contributes to measure the potential of energy saving and emission reduction of petrochemical industry simultaneously. Finally, the proposed model is applied to the energy and environmental efficiency analysis of different ethylene plants under different scales. The experimental results demonstrate the validity of the proposed model and reveal some implications for improving energy efficiency and carbon dioxide emissions reduction of ethylene plants in the petrochemical industry.

Linear optimization fusion model based on fuzzy C-means: Case study of energy efficiency evaluation in ethylene product plants

Ethylene production shares the common characteristics of different techniques and scales, high dimension and multi-correlation. However, the existing methods do not take the influence of the technology, scale and materials into account. In order to analyze the energy efficiency of ethylene plants better, this paper proposes a linear optimization fusion model based on fuzzy C-means (FCM) clustering. The FCM clustering is used to obtain the corresponding classification and clustering centers of the large scale time series data of the ethylene production process. Then the mean squares of time-series variables are calculated by various clustering centers. The multidimensional energy efficiency benchmarking of ethylene plants can be extracted by the fusion weighting vectors in linear optimization fusion model. The experimental results indicate that the proposed method intuitively reveals the running state and the operating level of ethylene plants and accurately identify the reason of energy consumption for improving energy efficiency.

Thermodynamic and economic assessment of the production of light olefins from bioethanol

The production of light olefins from bioethanol was evaluated in terms of thermodynamics and economics using Aspen Plus and Aspen Hysys as process simulators. The conceptual design of two different plants were proposed: one for ethylene and another for propylene and butenes production. A bioethanol sample containing characteristic compounds after biomass fermentation (e.g., ethanol, water, and aliphatics) was used as raw material in the plants, which could handle 10% of the Colombian bioethanol market. The plants delivered streams of 99mol% ethylene, 90mol% propylene, and a mixture of butenes. A class-V-order economic assessment of both optimized, heated-integrated plants was carried out giving total investments of 5.37 USD million for the ethylene plant (459 USD/ton ethylene-year) and 10.27 USD million for the propylene plant (2100 USD/ton of propylene and butenes-year). Traditional production routes from petroleum have reported comparative investment costs of 1300 USD/ton-year for ethylene and 1450 USD/ton-year for propylene. This initial feasibility analysis shows the potential of olefin production, mostly ethylene, from real fermented bioethanol, as an interesting approach to the biorefinery concept that could make their production more sustainable and economic.

Emission Constrained Dynamic Scheduling for Ethylene Cracking Furnace System

The scheduling of an ethylene cracking furnace system should not only focus on the plant profitability but also address the concern of environmental sustainability. On the basis of the previous study (Zhao et al.1), this Article has developed a new scheduling model for the optimal operation of the cracking furnace system in ethylene plants by considering more practical operating features, such as nonhomogeneous last-batch ending times and furnace load makeups, as well as the background air-quality conscious decoking. With the newly developed model, the simultaneous furnace shutdown can be successfully avoided at the end of the scheduling time horizon; meanwhile, certain manufacturing capacity losses and upsets can be alleviated during furnace decoking operations. Furthermore, the obtained scheduling solution will be responsible for background air-quality concerns by wisely allocating furnace decoking emission peaks into time windows that would cause less adverse environmental impacts. The efficacy of the ...

Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

Abstract The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.

World chemical outlook

World chemical outlook

Pilot-Scale Testing of a Polymerization Inhibitor in a Unit for Separating Heavy Fractions from Pyrolysis Gas

We present the results of pilot-scale testing of the thermal polymerization inhibitor Dewaxol 3002 under elevated operating temperature conditions. The reagent, synthesized based on heterocyclic compounds and an aromatic amine, was utilized in the unit for separating heavy fractions from pyrolysis gas for an EP-60 ethylene plant. It was established that the inhibitor is high-performance and the runtime between repairs was lengthened for boilers damaged by clogging due to polymerization of heavy unsaturated components of the pyrolysis gas.

Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes

Energy optimization and analysis of complex chemical processes play a significant role in the sustainable development procedure. In order to deal with the high-dimensional and noise data in complex chemical processes, we present an energy optimization and analysis method based on extreme learning machine integrating the index decomposition analysis. First, index decomposition analysis has been used to decompose the high-dimensional data to three energy performance indexes of the activity effect, the structure effect and the intensity. And then, those indexes and the production/conductivity of the chemical process are defined as inputs and outputs of the extreme learning machine respectively to build energy optimization and analysis model. Finally, the proposed method has been applied to optimizing and analyzing energy status of the ethylene system and the purified terephthalic acid solvent system in complex chemical processes. The experiment results show that the proposed method has the characteristics of fast learning, stable network outputs and high model accuracy in handling with the high-dimensional data. Moreover, it can optimize energy of chemical processes and guide the production operation. In our experiment, the production of ethylene plants can be increased by 5.33%, and the conductivity of purified terephthalic acid plants can be reduced by 0.046%.

Energy and carbon emissions analysis and prediction of complex petrochemical systems based on an improved extreme learning machine integrated interpretative structural model

Energy saving and carbon emissions reduction of the petrochemical industry are affected by many factors. Thus, it is difficult to analyze and optimize the energy of complex petrochemical systems accurately. This paper proposes an energy and carbon emissions analysis and prediction approach based on an improved extreme learning machine (ELM) integrated interpretative structural model (ISM) (ISM-ELM). ISM based the partial correlation coefficient is utilized to analyze key parameters that affect the energy and carbon emissions of the complex petrochemical system, and can denoise and reduce dimensions of data to decrease the training time and errors of the ELM prediction model. Meanwhile, in terms of the model accuracy and the training time, the robustness and effectiveness of the ISM-ELM model are better than the ELM through standard data sets from the University of California Irvine (UCI) repository. Moreover, a multi-inputs and single-output (MISO) model of energy and carbon emissions of complex ethylene systems is established based on the ISM-ELM. Finally, detailed analyses and simulations using the real ethylene plant data demonstrate the effectiveness of the ISM-ELM and can guide the improvement direction of energy saving and carbon emissions reduction in complex petrochemical systems.

Red-oils in ethylene plants: formation mechanisms, structure and emulsifying properties

In most ethylene plants, the caustic sections suffer from red-oils formation which results from the polyaldol condensation of some oxygenate species in basic media. These red-oils lead to solid material deposition which is generically referred to as fouling and which can cause severe energy losses or operational issues. This specific red-oil fouling can be successfully mitigated by regularly washing the caustic scrubber with a hydrocarbon washing stream which acts as a solvent. However, such washing streams can be further rerouted to the quench water system, increasing the risk of emulsion formation due to its amphiphilic components, i.e., red-oils, compromising the safe operation of the whole plant. The complexity of red-oil structures renders its analysis and characterization challenging. Consequently, there has been no study showing the effect of the type of hydrocarbon wash on the red-oils structure and its potential emulsifying properties. This study describes an in-house developed analytical technique based on UPLC-High resolution QTOF which allows characterizing hydrocarbon streams with complex mixtures of oxygenated species. This method has been successfully applied to monitor red-oil structures during a plant trial where Pyrolysis Gasoline and Toluene-Xylene hydrocarbon washes were successively applied. The data revealed that reactive compounds from the Pyrolysis Gasoline react with the polyaldol species, likely through a Diels–Alder mechanism, thus increasing the diene adduct concentration in the red-oil. It was further found that toluene-xylene washes hindered the emulsifying properties of the red-oils more effectively than Pyrolysis Gasoline washes, in that they could best inhibit the formation of polyaldols adducts.

Integrated model of refining and petrochemical plant for enterprise-wide optimization

Abstract In this paper, a novel integrated optimization approach which couples the up-stream refinery and the down-stream ethylene plant is proposed. When the material balances of the intermediate products between the two complex processes are considered, the potential of increasing the overall margin can be explored. A multi-period enterprise-wide mixed-integer nonlinear programming (MINLP) model is formulated to optimize the production planning of the processing units in the refinery and the ethylene plant simultaneously. Due to the model complexity, the Lagrangian algorithm is applied to decompose the integrated mathematical model into an MILP problem for the refinery and a small-scale MINLP problem for the ethylene plant. The performance of the proposed model was investigated on industrial examples to illustrate the economic potential and trade-offs involved in the enterprise-wide network. Results show that the integrated approach gains improvement in overall profit compared with the traditional sequential approach.

Simulation of ethane steam cracking with severity evaluation

Understanding the influence of operating parameters towards cracking severity is paramount in ensuring optimum operation of an ethylene plant. However, changing the parameters in an actual plant for data collection can be dangerous. Thus, a simulation model for ethane steam cracking furnace is developed using ASPEN Plus for the assessment. The process performance is evaluated with cracking severity factors and main product yields. Three severity factors are used for evaluation due to their ease of measurement, which are methane yield (Ymet), Ethylene-Ethane Ratio (EER) and Propylene-Ethylene Ratio (PER). The result shows that cracking severity is primarily influenced by reactor temperature. Operating the furnace with coil outlet temperature ranging between 850°C to 950°C and steam-to-hydrocarbon ratio of 0.3 to 0.5 has led to optimum main product yield.