A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant
Author
Abstract
Suggested Citation
DOI: 10.1016/j.energy.2023.129239
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "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," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
- Pier Francesco Orrù & Andrea Zoccheddu & Lorenzo Sassu & Carmine Mattia & Riccardo Cozza & Simone Arena, 2020. "Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
- Beisheim, Benedikt & Rahimi-Adli, Keivan & Krämer, Stefan & Engell, Sebastian, 2019. "Energy performance analysis of continuous processes using surrogate models," Energy, Elsevier, vol. 183(C), pages 776-787.
- Geng, Zhiqiang & Zeng, Rongfu & Han, Yongming & Zhong, Yanhua & Fu, Hua, 2019. "Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries," Energy, Elsevier, vol. 179(C), pages 863-875.
- He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
- Geng, ZhiQiang & Dong, JunGen & Han, YongMing & Zhu, QunXiong, 2017. "Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes," Applied Energy, Elsevier, vol. 205(C), pages 465-476.
- Geng, Zhiqiang & Zhang, Yanhui & Li, Chengfei & Han, Yongming & Cui, Yunfei & Yu, Bin, 2020. "Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature," Energy, Elsevier, vol. 194(C).
- Broniszewski, Mariusz & Werle, Sebastian, 2020. "CO2 reduction methods and evaluation of proposed energy efficiency improvements in Poland’s large industrial plant," Energy, Elsevier, vol. 202(C).
- Zhao, G.Y. & Liu, Z.Y. & He, Y. & Cao, H.J. & Guo, Y.B., 2017. "Energy consumption in machining: Classification, prediction, and reduction strategy," Energy, Elsevier, vol. 133(C), pages 142-157.
- Zeng, Zhiqiang & Hong, Mengna & Li, Jigeng & Man, Yi & Liu, Huanbin & Li, Zeeman & Zhang, Huanhuan, 2018. "Integrating process optimization with energy-efficiency scheduling to save energy for paper mills," Applied Energy, Elsevier, vol. 225(C), pages 542-558.
- Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
- Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).
- Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
- Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
- Hongyi Wu & Xuanyi Wang & Xiaolei Deng & Hongyao Shen & Xinhua Yao, 2024. "Review on Design Research in CNC Machine Tools Based on Energy Consumption," Sustainability, MDPI, vol. 16(2), pages 1-20, January.
- Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
- Mafakheri, Aso & Sulaimany, Sadegh & Mohammadi, Sara, 2023. "Predicting the establishment and removal of global trade relations for import and export of petrochemical products," Energy, Elsevier, vol. 269(C).
- Azarpour, Abbas & Mohamadi-Baghmolaei, Mohamad & Hajizadeh, Abdollah & Zendehboudi, Sohrab, 2022. "Systematic energy and exergy assessment of a hydropurification process: Theoretical and practical insights," Energy, Elsevier, vol. 239(PC).
- Jiang, Zhibin & Chen, Ling & Zhang, Wenguang & Chen, Shiyu & Jian, Xiying & Liu, Xiang & Chen, Hongyu & Guo, Chunlei & Li, Weishan, 2021. "Sandwich-like NOCC@S8/rGO composite as cathode for high energy lithium-sulfur batteries," Energy, Elsevier, vol. 220(C).
- Han, Yongming & Liu, Shuang & Geng, Zhiqiang & Gu, Hengchang & Qu, Yixin, 2021. "Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model," Energy, Elsevier, vol. 218(C).
- Marta Daroń & Monika Górska, 2023. "Relationships between Selected Quality Tools and Energy Efficiency in Production Processes," Energies, MDPI, vol. 16(13), pages 1-20, June.
- Zhu, Li & Li, Zhe & Chen, Junghui, 2021. "Evaluating and predicting energy efficiency using slow feature partial least squares method for large-scale chemical plants," Energy, Elsevier, vol. 230(C).
- Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
- Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
- Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
- Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
- Mustufa Haider Abidi & Usama Umer & Muneer Khan Mohammed & Mohamed K. Aboudaif & Hisham Alkhalefah, 2020. "Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization," Mathematics, MDPI, vol. 8(11), pages 1-33, November.
- Shailendra Pawanr & Kapil Gupta, 2024. "A Review on Recent Advances in the Energy Efficiency of Machining Processes for Sustainability," Energies, MDPI, vol. 17(15), pages 1-21, July.
- Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
- Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
- Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).
More about this item
Keywords
Data-driven model; Machine learning; Decision trees; Optimization; Genetic algorithm; Energy-saving; Industry;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026336. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.