IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v32y2007i11p2099-2109.html
   My bibliography  Save this article

Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden

Author

Listed:
  • De, S.
  • Kaiadi, M.
  • Fast, M.
  • Assadi, M.

Abstract

The development of a model for any energy system is required for proper design, operation or its monitoring. Models based on accurate mathematical expressions for physical processes are mostly useful to understand the actual operation of the plant. However, for large systems like combined heat and power (CHP) plants, such models are usually complex in nature. The estimation of output parameters using these physical models is generally time consuming, as these involve many iterative solutions. Moreover, the complete physical model for new equipment may not be available. However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed. Also, such trained ANN models have a fast response with respect to corresponding physical models and are useful for real-time monitoring of the plant. In this paper, the development of an ANN model for the biomass and coal cofired CHP plant of Västhamnsverket at Helsingborg, Sweden has been reported. The feed forward with back propagation ANN model was trained with data from this plant. The developed model is found to quickly predict the performance of the plant with good accuracy.

Suggested Citation

  • De, S. & Kaiadi, M. & Fast, M. & Assadi, M., 2007. "Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden," Energy, Elsevier, vol. 32(11), pages 2099-2109.
  • Handle: RePEc:eee:energy:v:32:y:2007:i:11:p:2099-2109
    DOI: 10.1016/j.energy.2007.04.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544207000722
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2007.04.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Ahmad Hassan Kamal & Muhammad Haider Khan & Awais Ahmad Khan & Hassan Afroze Ahmad & Fahad Ahmed & Noman Hafeez & Rana Muhammad Zawar Sami & Syed Muhammad , 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation," Energies, MDPI, vol. 13(21), pages 1-22, October.
    2. Bahadori, Alireza & Vuthaluru, Hari B., 2010. "A method for estimation of recoverable heat from blowdown systems during steam generation," Energy, Elsevier, vol. 35(8), pages 3501-3507.
    3. Keçebaş, Ali & Alkan, Mehmet Ali & Yabanova, İsmail & Yumurtacı, Mehmet, 2013. "Energetic and economic evaluations of geothermal district heating systems by using ANN," Energy Policy, Elsevier, vol. 56(C), pages 558-567.
    4. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    5. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    6. 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.
    7. Agha, Mujtaba H. & Thery, Raphaele & Hetreux, Gilles & Hait, Alain & Le Lann, Jean Marc, 2010. "Integrated production and utility system approach for optimizing industrial unit operations," Energy, Elsevier, vol. 35(2), pages 611-627.
    8. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
    9. Fast, M. & Assadi, M. & De, S., 2009. "Development and multi-utility of an ANN model for an industrial gas turbine," Applied Energy, Elsevier, vol. 86(1), pages 9-17, January.
    10. Bujak, Janusz, 2009. "Optimal control of energy losses in multi-boiler steam systems," Energy, Elsevier, vol. 34(9), pages 1260-1270.
    11. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
    12. Barelli, L. & Bidini, G. & Bonucci, F., 2009. "Development of the regulation mapping of 1Â MW internal combustion engine for diagnostic scopes," Applied Energy, Elsevier, vol. 86(7-8), pages 1087-1104, July.
    13. Kowalczyk-Juśko, Alina & Pochwatka, Patrycja & Zaborowicz, Maciej & Czekała, Wojciech & Mazurkiewicz, Jakub & Mazur, Andrzej & Janczak, Damian & Marczuk, Andrzej & Dach, Jacek, 2020. "Energy value estimation of silages for substrate in biogas plants using an artificial neural network," Energy, Elsevier, vol. 202(C).
    14. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    15. Colorado, D. & Ali, M.E. & García-Valladares, O. & Hernández, J.A., 2011. "Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution," Energy, Elsevier, vol. 36(2), pages 854-863.
    16. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
    17. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    18. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    19. Seijo, Sandra & del Campo, Inés & Echanobe, Javier & García-Sedano, Javier, 2016. "Modeling and multi-objective optimization of a complex CHP process," Applied Energy, Elsevier, vol. 161(C), pages 309-319.
    20. Suresh, M.V.J.J. & Reddy, K.S. & Kolar, Ajit Kumar, 2011. "ANN-GA based optimization of a high ash coal-fired supercritical power plant," Applied Energy, Elsevier, vol. 88(12), pages 4867-4873.
    21. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.

    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.
    1. Mehleri, E.D. & Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C., 2010. "A new neural network model for evaluating the performance of various hourly slope irradiation models: Implementation for the region of Athens," Renewable Energy, Elsevier, vol. 35(7), pages 1357-1362.
    2. Jorge E. De León-Ruiz & Ignacio Carvajal-Mariscal & Antonin Ponsich, 2019. "Feasibility Analysis and Performance Evaluation and Optimization of a DXSAHP Water Heater Based on the Thermal Capacity of the System: A Case Study," Energies, MDPI, vol. 12(20), pages 1-38, October.
    3. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    4. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    5. Abu Bakar, Nur Najihah & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah & Bandi, Masilah, 2015. "Energy efficiency index as an indicator for measuring building energy performance: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 1-11.
    6. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
    7. 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.
    8. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    9. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    10. Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    11. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    12. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
    13. Wang, Jiangfeng & Sun, Zhixin & Dai, Yiping & Ma, Shaolin, 2010. "Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network," Applied Energy, Elsevier, vol. 87(4), pages 1317-1324, April.
    14. Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    15. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    16. Jean-Laurent Duchaud & Cyril Voyant & Alexis Fouilloy & Gilles Notton & Marie-Laure Nivet, 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control," Energies, MDPI, vol. 13(14), pages 1-16, July.
    17. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    18. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
    19. Laimon, Mohamd & Mai, Thanh & Goh, Steven & Yusaf, Talal, 2022. "System dynamics modelling to assess the impact of renewable energy systems and energy efficiency on the performance of the energy sector," Renewable Energy, Elsevier, vol. 193(C), pages 1041-1048.
    20. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.

    Corrections

    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:32:y:2007:i:11:p:2099-2109. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.