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Liu J, Zhou X, Yang G, Zhao H, Zhang Z, Feng X, Yan H, Liu Y, Chen X, Yang C. Conceptual carbon-reduction process design and quantitative sustainable assessment for concentrating high purity ethylene from wasted refinery gas. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2022.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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2
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Shen W, Tian Z, Zhao L, Qian F. Life Cycle Assessment and Multiobjective Optimization for Steam Cracking Process in Ethylene Plant. ACS OMEGA 2022; 7:15507-15517. [PMID: 35571839 PMCID: PMC9096952 DOI: 10.1021/acsomega.2c00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/14/2022] [Indexed: 06/15/2023]
Abstract
With energy savings and emission reduction becoming national policies in recent years, the environmental impacts of industrial production are more and more critical. Most of the studies have concentrated on the environmental effects of the industrial production process. Little attention has been paid to the energy consumption and pollution emission in extracting, processing, and transporting the feedstock and other secondary materials. An integrated multiobjective optimization framework is proposed for the steam cracking process on the basis of a life cycle assessment and data-driven modeling methods. A multiobjective economic-environmental optimization model is developed on the basis of industrial and simulated data. A multiobjective optimization model combined with energy cost is also developed for comparative study. The nondominated sorting genetic algorithm-II is utilized to solve the problems, and the Pareto front is obtained. An industrial case study is carried out to indicate the effectiveness of the proposed method. The results show that the LCA-based method can better represent the environmental impacts in comparison with the standard energy cost model. Therefore, the proposed method can achieve a better tradeoff between economic benefits and environmental impacts for guiding ethylene production.
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Energy and Environmental Assessment of Steam Management Optimization in an Ethylene Plant. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182212267. [PMID: 34832022 PMCID: PMC8625353 DOI: 10.3390/ijerph182212267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/18/2021] [Accepted: 11/20/2021] [Indexed: 11/18/2022]
Abstract
Steam crackers (ethylene plants) belong to the most complex industrial plants and offer significant potential for energy-saving translated into the reduction of greenhouse gas emissions. Steam export to or import from adjacent units or complexes can boost the associated financial benefit, but its energy and environmental impact are questionable. A study was carried out on a medium-capacity ethylene plant using field data to: 1. Estimate the energy savings potential achievable by optimizing internal steam management and optimizing steam export/import; 2. Quantify the associated change in air pollutant emissions; 3. Analyze the impact of the increasing carbon price on the measures adopted. Internal steam management optimization yielded steam let-down rate minimization and resulted in a 5% (87 TJ/year) reduction in steam cracker’s steam boiler fuel consumption and the associated cut of CO2 emissions by almost 4900 t/year and that of NOx emissions by more than 5 t/year. Steam import to the ethylene plant from the refinery proved to be purely economic-driven, as it increased the net fuel consumption of the ethylene plant and the refinery complex by 12 TJ/year and resulted in an increase of net emissions of nearly all considered air pollutants (more than 7000 t/year of CO2, over 15 t/year of NOx, over 18 t/year of SOx) except for CO, where the net change was almost zero. The effect of external emissions change due to the associated backpressure electricity production surplus (over 11 GWh/year) was too low to compensate for this increase unless fossil fuel-based electricity production was considered. The increase of carbon price impact on the internal steam management optimization economics was favorable, while a switch to steam export from the ethylene plant, instead of steam import, might be feasible if the carbon price increased to over 100 €/tCO2.
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Zhang S, Jiang J, Wang H, Li F, Hua T, Wang W. A review of microbial electrosynthesis applied to carbon dioxide capture and conversion: The basic principles, electrode materials, and bioproducts. J CO2 UTIL 2021. [DOI: 10.1016/j.jcou.2021.101640] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Khudoyberdiev A, Ullah I, Kim D. Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-200618] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.
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Affiliation(s)
| | - Israr Ullah
- Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan
| | - DoHyeun Kim
- Department of Computer Engineering, Jeju National University, Jeju, Korea
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Zhang XH, Xu Y, He YL, Zhu QX. Novel manifold learning based virtual sample generation for optimizing soft sensor with small data. ISA TRANSACTIONS 2021; 109:229-241. [PMID: 33070985 DOI: 10.1016/j.isatra.2020.10.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/03/2020] [Accepted: 10/03/2020] [Indexed: 06/11/2023]
Abstract
Due to the extremely complex mechanism and strong non-linear characteristics of industrial processes, data-driven soft sensor technologies play a key role in the intelligent measurement of process industries. However, the information of the collected process data in the steady stage is quite limited and unreliable, causing the small sample problem. As a result, it becomes an intractable challenge to catch the nature of the process and build accurate soft sensor models. To solve this problem, this paper proposes a novel manifold learning based virtual sample generation method (Isomap-VSG) to generate feasible virtual samples in the information gaps for supplementing the original small sample space. To find data sparse regions reasonably, one kind of manifold learning methods called Isomap is used to visualize process data with high dimension. Then virtual samples can be generated by the interpolation method and extreme learning machine. The simulation results on a standard dataset and a real-world application demonstrate that, compared with other advanced methods, the proposed Isomap-VSG method can achieve better performance in terms of generating feasible virtual samples and improving the accuracy of soft sensor models using limited samples.
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Affiliation(s)
- Xiao-Han Zhang
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yuan Xu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
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Sun W, Huang C. A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 266:115216. [PMID: 32763723 DOI: 10.1016/j.envpol.2020.115216] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/29/2020] [Accepted: 07/07/2020] [Indexed: 06/11/2023]
Abstract
Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO2 and SO2. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R2 and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R2 and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R2 can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored.
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Affiliation(s)
- Wei Sun
- Department of Economic Management, North China Electric Power University, Baoding 071000, PR China
| | - Chenchen Huang
- Department of Economic Management, North China Electric Power University, Baoding 071000, PR China.
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Tian Y, He YL, Zhu QX. Soft Sensor Development Using Improved Whale Optimization and Regularization-Based Functional Link Neural Network. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03839] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ye Tian
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
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Cao J, He Y, Zhu Q. Feedstock Scheduling Optimization Based on Novel Extensible P-Graph Reasoning in Ethylene Production. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03563] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jian Cao
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yanlin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Qunxiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
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Cao J, He Y, Zhu Q. An ontology‐based procedure knowledge framework for the process industry. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23873] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Jian Cao
- College of Information Science & Technology Beijing University of Chemical Technology Beijing China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China Beijing China
| | - Yan‐Lin He
- College of Information Science & Technology Beijing University of Chemical Technology Beijing China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China Beijing China
| | - QunXiong Zhu
- College of Information Science & Technology Beijing University of Chemical Technology Beijing China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China Beijing China
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Zhu QX, Zhang XH, He YL. Novel Virtual Sample Generation Based on Locally Linear Embedding for Optimizing the Small Sample Problem: Case of Soft Sensor Applications. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01942] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing, 100029, China
| | - Xiao-Han Zhang
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing, 100029, China
| | - Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing, 100029, China
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He YL, Ma Y, Xu Y, Zhu QX. Fault Diagnosis Using Novel Class-Specific Distributed Monitoring Weighted Naı̈ve Bayes: Applications to Process Industry. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01071] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yongchao Ma
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yuan Xu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
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13
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Luo Y, Gopaluni B, Xu Y, Cao L, Zhu QX. A Novel Approach to Alarm Causality Analysis Using Active Dynamic Transfer Entropy. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06262] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yi Luo
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Bhushan Gopaluni
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Yuan Xu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Liang Cao
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
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