Ethylene Production System Research Articles

Low-carbon transformation of ethylene production system through deployment of carbon capture, utilization, storage and renewable energy technologies

Ethylene industry contributes significantly to the world economy, but the conventional steam cracking based production process generates huge amount of CO2 emissions due to massive use of fossil fuels for power and heat supply. Deploying technologies of carbon capture, utilization and storage (CCUS) and renewable energy is urgently necessary to steer the emission-intensive ethylene industry towards carbon neutrality. To attain this goal, this paper proposes a low-carbon ethylene production system with the joint deployment of CCUS, wind turbine, solar heat collector, electric boiler and thermal energy storage technologies. Energy, economic and CO2 generation/reduction performance models of each equipment are developed first. Based on which, a mixed-integer liner programming framework is constructed to find the optimal capacity configuration and coordinated operation scheme of the multiple devices, laying a foundation for the reliable, economical and low-carbon operation of the integrated system. Case studies on a practical ethylene production system demonstrate that the proposed low-carbon retrofit leads to 57.50% CO2 reduction at the expense of 15.92% total annual cost increase. Discussions are then carried out to investigate the effects of different decarbonization routes, the impact of carbon emission trading price, the approach to promote the deployment of CO2 hydrogenation to methanol, and the advantage of taking carbon capture as flexible load, through which, broader insights are provided for the low-carbon transformation of the ethylene industry.

English

The role of bioengineering in industrial processes cannot be ignored, especially in circumstances where the processes are uneconomical, demand high energy, use finite resources and emit huge amounts of carbon dioxide. Ethylene is a two-carbon unsaturated hydrocarbon that is industry's most important building block for polyester fibres, plastics, and ethylene glycol. For decades, ethylene production has relied on steam-cracking without many improvements, especially on issues of environmental impact and adoption of appropriate renewable approaches. This paper discusses selected microbial pathway modifications as novel approach to developing systems that could be alternatives to conventional ethylene production systems. Bioengineering of the ethylene pathway is suggested in view of the need to meet the criteria of high efficiency, increase sustainability and ensure product qualities and quantities that can exceed the existing approach.   Key words: Ethylene, crude glycerol, tolerance, synthesis, bioengineering.

Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model

Improving production efficiency and optimizing resources can accelerate the sustained and stable development of complex chemical processes. This paper presents novel energy analysis and resource optimization model based on data envelopment analysis cross-model integrated interpretative structural model and analytic hierarchy process to effectively simplify input indicators. The interpretative structural model can divide production data with many uncertain dimensions into some sub-elements with obvious hierarchical relationships. Then the sub-elements in the same layer are merged into a major element by the analytic hierarchy process method for further simplifying input indicators of the improved DEACM. At last, the proposed method is used to build the energy analysis and resource optimization model for PTA and ethylene production systems in complex chemical processes. In our experiments, the difference in efficiency values obtained by the proposed model is more significant and accurate. In addition, it can optimize production efficiency and guide ineffective production processes. The electrical conductivity of the purified terephthalic acid systems can reduce by 0.47%. And the ethylene production of the ethylene production systems can increase by 3.93%, and the carbon emissions of the ethylene production system can reduce by 25,297 tons approximately.

Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature

The petrochemical industry is the top priority of the national economy and sustainable development. For the purpose of improving the energy efficiency in the petrochemical industry, an energy optimization and prediction model based on the improved convolutional neural network (CNN) integrating the cross-feature (CF) (CF–CNN) is proposed. The CF can combine the correlation between features to obtain the input of the CNN, which can avoid over-fitting problems caused by fewer features. Then the CNN is designed as a three-layer structure and the Rectified Linear Unit (ReLU) is introduced to achieve better generalization capability and stability with boiler fluctuations in the petrochemical industry. The developed method has better performances of modeling accuracy and applicability than that of the back-propagation (BP) neural network and the extreme learning machine (ELM) on University of California Irvine (UCI) benchmark datasets. Furthermore, the developed method is applied to establish an energy optimization and prediction model of ethylene production systems in the petrochemical industry. The experimental results testify the capability of the proposed method. Meanwhile, the average relative generalization error is 2.86%, and the energy utilization efficiency increases by 6.38%, which leads to reduction of the carbon emissions by 5.29%.

Production capacity analysis and energy saving of complex chemical processes using LSTM based on attention mechanism

The production data of complex chemical processes are multi-dimensional, uncertain and noisy, and it is difficult to directly control raw materials consumption and measure the product quality. Therefore, this paper proposes a production capacity analysis and energy saving model using long short-term memory (LSTM) based on attention mechanism (AM) (AM-LSTM). The weights of the results sequence in the hidden layer, which have great influence on final results in the output layer, are calculated by the AM. Then the production prediction model is built using the LSTM to extract features of the input data and multiple time series results of the hidden layer. Compared with the common LSTM, the multi-layer perceptron (MLP) and the extreme learning machine (ELM), the applicability and the effectiveness of the proposed model is validated based on University of California Irvine repository (UCI) datasets. Finally, the proposed model is applied to analyze the production capacity and the energy saving potential of the purified terephthalic acid (PTA) solvent system and the ethylene production system of the complex chemical process. The experimental results verify the practicability and accuracy of the proposed model. Furthermore, the results offer the operation guidance for production capacity improvement through saving energy and reducing the energy consumption.

Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries

Data envelopment analysis (DEA) has been widely used in the energy efficiency analysis of industrial production processes. However, the traditional DEA model is not high in the division of the efficiency value of decision making units (DMUs), and produces a large number of DMUs with an efficiency value equal to 1, making it difficult to identify their merits and demerits. Therefore, a novel DEA model based on the affinity propagation (AP) clustering algorithm (AP-DEA) is proposed. Through the AP clustering algorithm, high influence input data of the energy efficiency can be obtained. The merits and demerits of DMUs can then be identified with a high degree of discrimination to obtain better efficiency groups. Finally, the proposed model is applied to evaluate the energy efficiency and optimize the energy configuration of the ethylene and pure terephthalic acid (PTA) production processes in complex petrochemical industries. The experimental results show that this proposed model can improve the efficiency value discrimination of efficiency values by effective DMUs better than the traditional DEA. Moreover, the energy saving potentials of ethylene and PTA production systems are approximately 0.49% and 24.74%, respectively, and the carbon emission reduction of the ethylene production system is approximately 10.04%.

Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes

One of the key issues of the sustainable development in all countries is industrial productivity improvement, especially the production capacity improvement and energy-saving of complex chemical processes. Therefore, this paper proposes a production prediction and energy-saving model based on Extreme Learning Machine (ELM) integrated Interpretative Structural Modeling (ISM) and Analytic Hierarchy Process (AHP). The factors that affect the productivity are divided into different levers by the ISM. And then the attributes of each layer are fused by the AHP based on the entropy weight, which greatly reduces the complexity of the input attributes. Moreover, the production prediction and energy-saving model is established based on the ELM. Compared with the traditional ELM, the validity and the practicability of the proposed method are verified by University of California Irvine (UCI) datasets. Finally, the proposed method is applied in the production capacity prediction and energy-saving of ethylene production systems and Purified Terephthalic Acid (PTA) production systems. The experimental results show the proposed method could reduce the number of hidden layer nodes and improve the training time of the ELM. Furthermore, the prediction accuracy of the ethylene production and the PTA production reaches about 99% to improve the energy efficiency of complex chemical processes.

Effect of 1-Methylcyclopropene on Superficial Scald Associated with Ethylene Production, α-Farnesene Catabolism, and Antioxidant System of Over-Mature ‘d’Anjou’ Pears After Long-Term Storage

To optimize storage quality of ‘d’Anjou’ pears (Pyrus communis L.) produced in the US Pacific Northwest regions, the recommended harvest maturity as indicated by flesh pressure is 67 to 58 N. Over-mature pears are more susceptible to superficial scald and inferior eating quality after storage. However, a significant portion of pears is harvested at an over-mature stage due to rapid on-tree maturation during years with labor shortages. 1-Methylcyclopropene (1-MCP) extends storage quality of pears but interferes their ultimate ripening capacity. The purpose of this work was to evaluate the effect of 1-MCP on over-mature ‘d’Anjou’ pears. Fruit from two orchards at fruit firmness (FF) 55.6 and 56.0 N were treated with 0.15 μL L−1 1-MCP, held at − 1.1 °C for up to 7 months. 1-MCP treatment significantly inhibited ethylene synthesis and respiration rate, reduced superficial scald, and maintained high storage quality of over-mature pears from two orchards during 7 months of storage. After 7 months of storage at − 1.1 °C, some of the pears from Orchard 1 recovered ripening capacity after 5 days at 20 °C. The development of superficial scald in over-mature ‘Anjou’ pears was associated with the accumulation of α-farnesene and conjugated trienols (CTols). 1-MCP significantly inhibited the development of scald, perhaps by alleviating membrane lipid peroxidation and enhancing total antioxidant capacity, antioxidant metabolites (i.e., total polyphenols (TP) and total flavonoids (TF)), and related enzyme activities (i.e., superoxide dismutase (SOD), catalase (CAT)) in pear skin. Overall, this study provides the preliminary basis for commercial application of 1-MCP in over-mature pears and demonstrates its potential to maintain fruit quality and reduce the incidence of superficial scald.

A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry

An accurate energy prediction and optimization model plays a very important role in the petrochemical industries. Due to the imbalanced and uncompleted characteristics of complex petrochemical small data, it is a big challenge to build accurate prediction and optimization models for energy analysis. In order to solve this problem, a nonlinear interpolation virtual sample generation method integrated with extreme learning machine is proposed. Well virtual input and output variables can be generated through interpolation of the hidden layer outputs of extreme learning machine. The generated virtual samples are put together with the original samples to train models for enhancing accuracy performance. To validate the effectiveness of the proposed nonlinear interpolation virtual sample generation method, a standard function is firstly selected, and then the proposed nonlinear interpolation virtual sample generation method is applied to developing a model of energy analysis for ethylene production systems. Simulation results showed that the prediction accuracy could be significantly improved, which provided helpful guidance for production departments and government to achieve the goal of energy management of petrochemical industries.

A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems

Abstract Single layer feed-forward network (SLFN) is well applied to find mapping relationships between the input data and the output data. However, the SLFN has two obvious shortcomings of the indetermination structure and parameters. Therefore, this paper proposes a novel self-organizing cosine similarity learning network (SO-CSLN), which can obtain a stable structure and suitable parameters. The hidden layer nodes of the SO-CSLN are determined by the rank of the sample covariance matrix based on the central limit theorem. And then the weights are obtained by the entropy theory and the cosine similarity theory. Moreover, compared with the SLFN, the proposed algorithm can overcome the shortcomings of the SLFN and provide better performance with faster convergence and smaller generalization error through different UCI data sets. Finally, the proposed method is applied to building the production prediction model of the ethylene production system in petrochemical industries. The experiment results show that the effectiveness and the practicality of the proposed method. Meanwhile, it can guide ethylene production and improve the energy efficiency.

A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries

Due to the imbalanced and uncompleted characteristics of complex petrochemical small datasets, it is a challenge to build an accurate prediction and optimization model of energy consumption of petrochemical systems. Therefore, this paper proposes a novel virtual sample generation (VSG) approach based on the Monte Carlo (MC) and Particle Swarm Optimization (PSO) algorithms to improve the accuracy of the energy efficiency analysis on small data set problems. The proposed approach utilizes the MC and PSO algorithms to generate appropriate virtual samples based on the underlying information extracted from the small datasets. An accurate prediction model is presented using the extreme machine learning (ELM) in view of the synthetic data. The performance of the proposed model is validated via an application using a purified Terephthalic acid (PTA) solvent system and an ethylene production system. The experiment results demonstrate that the accuracy of the prediction model can be improved, and guidance for the production department to improve the energy efficiency, energy savings and emission reduction is provided under the small data circumstance.

Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP

Extreme learning machine (ELM), which is a simple single-hidden-layer feed-forward neural network with fast implementation, has been widely applied in many engineering fields. However, it is difficult to enhance the modeling ability of extreme learning in disposing the high-dimensional noisy data. And the predictive modeling method based on the ELM integrated fuzzy C-Means integrating analytic hierarchy process (FAHP) (FAHP-ELM) is proposed. The fuzzy C-Means algorithm is used to cluster the input attributes of the high-dimensional data. The Analytic Hierarchy Process (AHP) based on the entropy weights is proposed to filter the redundant information and extracts characteristic components. Then, the fusion data is used as the input of the ELM. Compared with the back-propagation (BP) neural network and the ELM, the proposed model has better performance in terms of the speed of convergence, generalization and modeling accuracy based on University of California Irvine (UCI) benchmark datasets. Finally, the proposed method was applied to build the energy saving and predictive model of the purified terephthalic acid (PTA) solvent system and the ethylene production system. The experimental results demonstrated the validity of the proposed method. Meanwhile, it could enhance the efficiency of energy utilization and achieve energy conservation and emission reduction.

Performance analysis and optimal temperature selection of ethylene cracking furnaces: A data envelopment analysis cross-model integrated analytic hierarchy process

Ethylene is an important raw material of industrial products, and the performance analysis of ethylene cracking furnaces in the petrochemical industry is affected mainly by the temperature. Therefore, this paper presents a performance analysis and optimal temperature selection method of ethylene cracking furnaces using the data envelopment analysis cross-model (DEACM) integrated analytic hierarchy process (AHP) (DEACM-AHP). The DEACM avoiding the unreasonable weight distribution of input-output factors can accurately identify and estimate the performance status and the optimal temperature of ethylene cracking furnaces. Meanwhile, the improved AHP based on the entropy weight can comprehensively consider the reasonable weight allocation of each input-output index and obtain the consistent fusion result of ethylene cracking furnaces with different temperatures. And then the optimal production benchmark with different temperatures is obtained based on the DEACM again. Finally, the proposed approach is applied to the performance analysis and optimal temperature selection of the SL-I type naphtha cracker model in the petrochemical field. The experimental result shows the proposed method can guide the ethylene production system to reduce energy consumption and improve energy efficiency.

Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

Since the complex petrochemical data have characteristics of multi-dimension, uncertainty and noise, it is difficult to accurately optimize and predict the energy usage of complex petrochemical systems. Therefore, this paper proposes a data envelopment analysis (DEA) integrated artificial neural network (ANN) approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA-ANN prediction model is effectively verified by executing a linear comparison between all DMUs and the effective DMUs through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex ethylene production system of China petrochemical industry. The optimization result and the prediction value are obtained to reduce energy consumption of the ethylene production system, guide ethylene production and improve energy efficiency.

Understanding Variability To Reduce the Energy and GHG Footprints of U.S. Ethylene Production.

Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industry's energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industry's footprints moving forward.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model (ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

The petrochemical industry evaluation is affected by numerous factors. Many previous studies proposed a use of data envelopment analysis (DEA) as a methodology for energy efficiency analysis in the petrochemical industry. However, excessive decision-making units (DMUs) of DEA model result in difficulties in evaluation and comparison of the different DMUs. In this paper, a new energy analysis framework of petrochemical industrial processes based on DEA integrated interpretative structural model (ISM) is proposed. The ISM method is brought up based on the partial correlation coefficient method to find the main factors and basic reasons that affect the energy consumption of the ethylene production system, which serve as the inputs of the DEA. Meanwhile, ethylene, propylene and C4 productions of the ethylene production system sever as the outputs of the DEA. Then the fractional DEA model is solved by using the linear programming method. The proposed evaluation method can overcome the shortcomings of the DEA model mentioned above, and also is able to reflect the effectiveness of the DMUs and guide the improvement directions of the ineffective DMUs based on slack variables. Our approach is applied in the energy efficiency analysis of Chinese ethylene industry in the petrochemical field. The empirical results show that the proposed energy consumption analysis method is valid and efficient in improvements of energy efficiency in ethylene production systems.

Solar energy footprint of ethylene processes

Sustainable development is of prime importance to the chemical industry. Emergy analysis is a method for evaluating process sustainability. In this method all the resources utilized are evaluated on the common basis of solar energy. Therefore, accounting for both natural and economic inputs can be presented employing the same unit for sustainability evaluation and optimization. In this paper, four ethylene production systems were evaluated using emergy accounting techniques. Production of ethylene from naphtha by steam cracking, natural gas or pine residue based methanol to olefins (MTO) and switchgrass bioethanol process routes were analyzed. Several emergy based indices were employed to evaluate and compare the system performances.The emergy analysis showed large differences between the biomass and fossil based processes: The fossil based ethylene processes represent good emergy yield and investment ratios, implying good utilization of the emergy invested. On the other hand, they have high environmental loadings and a very low renewable percentage, which means bad environmental performance. Also, their emergy efficiency (specific emergy) is poor: on average they consume nearly 2.5 times as much biosphere resources (emergy/kg ethylene) as the renewable based processes.The biomass based processes present the opposite characteristics: lower specific emergy of products, moderate environmental loading, higher renewable percentage (approx. 20%) but worse emergy yield and investment ratios.In conclusion, the fossil based processes give a better return on the emergy invested in the short term but are very emergy inefficient and represent large environmental loadings. Therefore, the biomass based processes should be favored for long term sustainability. These consume only about 40% of biosphere resources per kg of ethylene compared to the fossil based processes. The ultimate choice depends on whether only ethylene is required or other olefins also; the switchgrass ethanol dehydration process produces ethylene with the best emergy efficiency. If other olefins are also required, the pine chips MTO represents the best specific emergy value for the whole pool of products. Still lower ethylene specific emergy could be achieved by replacing switchgrass by an uncultivated forest biomass for the production of the bioethanol required in the dehydration process.

Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry

DEA (data envelopment analysis) has been widely used for the efficiency analysis of industrial production process. However, the conventional DEA model is difficult to analyze the pros and cons of the multi DMUs (decision-making units). The DEACM (DEA cross-model) can distinguish the pros and cons of the effective DMUs, but it is unable to take the effect of the uncertainty data into account. This paper proposes an efficiency analysis method based on FDEACM (fuzzy DEA cross-model) with Fuzzy Data. The proposed method has better objectivity and resolving power for the decision-making. First we obtain the minimum, the median and the maximum values of the multi-criteria ethylene energy consumption data by the data fuzzification. On the basis of the multi-criteria fuzzy data, the benchmark of the effective production situations and the improvement directions of the ineffective of the ethylene plants under different production data configurations are obtained by the FDEACM. The experimental result shows that the proposed method can improve the ethylene production conditions and guide the efficiency of energy utilization during ethylene production process.

Negative feedback regulation of system-1 ethylene production by the tomato 1-aminocyclopropane-1-carboxylate synthase 6 gene promoter

In tomato, two distinct ethylene production systems, the autoinhibitory (system-1) and the autocatalytic (system-2), operate sequentially during fruit development. LE-ACS6 has been shown to be negatively regulated by endogenous and exogenous ethylene, and was thought to be involved in system-1 ethylene biosynthesis. To investigate the mechanism of the autoinhibitory regulation of LE-ACS6, we isolated its 5′-flanking sequence and examined the expression of a chimeric LE-ACS6 promoter (−1293 to +25):: β-glucuronidase ( GUS) fusion gene in transgenic tomato. According to computer-based prediction, the 1.3 kb promoter sequence contains putative cis-elements required for ethylene, auxin, abscisic acid (ABA), and wounding responses. In transgenic tomato, the cloned promoter was sufficient to modulate GUS in response to exogenous ethylene in a tissue-specific manner, and with similar transcript levels to the endogenous LE-ACS6. Histochemical staining illustrated GUS activity in the cotyledons, hypocotyls and roots of seedlings as well as in flowers, leaves, and fruits. Among all tested tissues, GUS activity was highest at the immature green stage in fruit. In flowers, GUS activity peaked at anthesis and decreased during senescence. Auxin but not ABA increased GUS activity and LE-ACS6 transcript levels in seedlings. Wounding significantly increased GUS activity and LE-ACS6 transcripts in the fruit pericarp but not in the leaves. Based on these results, we conclude that the autoinhibition of LE-ACS6 expression is mainly under the transcriptional control of the promoter region of the gene.