Artificial Intelligence Recommendation System for Optimizing Steam Power Plant Heat Rate: A Conceptual Design
A Conceptual Design
-
Lulu Ardiansyah
*
Institut Teknologi PLN, Jakarta,
Indonesia
- Hetty Rohayani Muhammadiyah Jambi University, Jambi, Indonesia (*) Corresponding Author
Abstract
Steam power plants are one of the major electricity generation units in many countries around the world. The thermal efficiency of power plants is primarily dependent on decision making by the operator on real time process parameters. This decision-making process currently utilizes human expertise, in conjunction with static setpoints and operating procedures. However, variability in human operator performance and plant operating conditions often leads to non-optimal heat rate values. The purpose of this paper is to develop a conceptual framework for an artificial intelligence-based operator decision-support system for real-time heat rate optimization, integrating Model-Based Design (MBD) and Design Science Research (DSR) principles. The framework presented in this paper is informed by past high efficiency operational experience and machine learning methodology to describe the necessary steps in generating actionable, explainable recommendations for process parameter adjustments. The conceptual framework presented, which incorporates both predictive capabilities as well as domain expertise, is intended to bridge the gap between the development of predictive models and their eventual deployment as prescriptive operational support systems by providing a high-level blueprint of a system design that is expected to lead to more robust and consistent decision making. The key functional components of the framework include data capture, preprocessing, inference modeling and, ultimately, presentation of recommendations on a human-machine interface. An initial, theoretical appraisal of the proposed framework suggests promising potential for improving operational efficiency, reducing fuel consumption, and lowering emissions, and it is expected to serve as a useful reference for ongoing and future development efforts.
Downloads
References
P. Biswas, A. Rashid, A. Biswas, M. A. Al Nasim, K. D. Gupta, and R. George, “AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey,” Jun. 2024, [Online]. Available: http://arxiv.org/abs/2406.15732
S. Chantasiriwan, “Optimization of steam parameters in double-pressure heat recovery steam generators,” Case Studies in Thermal Engineering, vol. 61, Sep. 2024, doi: 10.1016/j.csite.2024.104851.
J. W. Burnett and L. L. Kiesling, “Power plant heat-rate efficiency as a regulatory mechanism: Implications for emission rates and levels,” Energy Policy, vol. 134, Nov. 2019, doi: 10.1016/j.enpol.2019.110980.
E. Martelli, F. Alobaid, and C. Elsido, “Design Optimization and Dynamic Simulation of Steam Cycle Power Plants: A Review,” Jul. 02, 2021, Frontiers Media S.A. doi: 10.3389/fenrg.2021.676969.
S. Liu and J. Shen, “Modeling of Large-Scale Thermal Power Plants for Performance Prediction in Deep Peak Shaving,” Energies (Basel), vol. 15, no. 9, May 2022, doi: 10.3390/en15093171.
I. Antonopoulos et al., “Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review,” Sep. 01, 2020, Elsevier Ltd. doi: 10.1016/j.rser.2020.109899.
N. O. Adelakun and S. A. Omolola, “Predictive Maintenance for Energy Systems in Built Environments Using Deep Learning Models,” in Proceedings of the 2nd International Facilities Engineering & Management Conference, Nov. 2024. doi: 10.5281/zenodo.14849013.
Shedrack Onwusinkwue et al., “Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization,” World Journal of Advanced Research and Reviews, vol. 21, no. 1, pp. 2487–2799, Jan. 2024, doi: 10.30574/wjarr.2024.21.1.0347.
Y. D. Arferiandi, W. Caesarendra, and H. Nugraha, “Heat rate prediction of combined cycle power plant using an artificial neural network (Ann) method,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–16, Feb. 2021, doi: 10.3390/s21041022.
Y. Ding, J. Wong, S. Patel, D. Mallapragada, G. Zang, and R. Stoner, “A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning,” Sep. 2024, [Online]. Available: http://arxiv.org/abs/2410.00016
M. A. Nemitallah et al., “Artificial intelligence for control and optimization of boilers’ performance and emissions: A review,” Sep. 10, 2023, Elsevier Ltd. doi: 10.1016/j.jclepro.2023.138109.
C. Bisset, P. V. Z. Venter, and R. Coetzer, “A systematic literature review on machine learning applications at coal-fired thermal power plants for improved energy efficiency,” International Journal of Sustainable Energy, vol. 42, no. 1, pp. 845–872, 2023, doi: 10.1080/14786451.2023.2244618.
M. A. I. Malik et al., “Enhancing peak performance forecasting in steam power plants through innovative AI-driven exergy-energy analysis,” Energy Conversion and Management: X, vol. 26, Apr. 2025, doi: 10.1016/j.ecmx.2025.101025.
W. M. Ashraf et al., “Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector,” ACS Omega, vol. 8, no. 24, pp. 21709–21725, Jun. 2023, doi: 10.1021/acsomega.3c01227.
W. Xu and P. Zhang, “Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder,” Nov. 2024. doi: https://doi.org/10.48550/arXiv.2402.07933.
X. Zhan, H. Xu, Y. Zhang, X. Zhu, H. Yin, and Y. Zheng, “DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning,” Feb. 2021, [Online]. Available: http://arxiv.org/abs/2102.11492
J. H. Park, H. S. Jo, S. H. Lee, S. W. Oh, and M. G. Na, “A reliable intelligent diagnostic assistant for nuclear power plants using explainable artificial intelligence of GRU-AE, LightGBM and SHAP,” Nuclear Engineering and Technology, vol. 54, no. 4, pp. 1271–1287, Apr. 2022, doi: 10.1016/j.net.2021.10.024.
F. Heymann, H. Quest, T. Lopez Garcia, C. Ballif, and M. Galus, “Reviewing 40 years of artificial intelligence applied to power systems – A taxonomic perspective,” Energy and AI, vol. 15, Jan. 2024, doi: 10.1016/j.egyai.2023.100322.
X. Zhu, S. Chen, X. Liang, X. Jin, and Z. Du, “Next-generation generalist energy artificial intelligence for navigating smart energy,” Cell Rep Phys Sci, p. 102192, Sep. 2024, doi: 10.1016/j.xcrp.2024.102192.
A. Hevner, S. T. March, J. Park, and S. Ram, “Design Science in Information Systems Research,” 2004.
L. Petersen, F. Iov, and G. C. Tarnowski, “A model-based design approach for stability assessment, control tuning and verification in off-grid hybrid power plants,” Energies (Basel), vol. 13, no. 1, Dec. 2019, doi: 10.3390/en13010049.
M. Truss and M. Schmitt, “Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations,” Jun. 2024. doi: https://doi.org/10.48550/arXiv.2402.07933.
A. Hevner, “A Three Cycle View of Design Science Research,” 2014. [Online]. Available: https://www.researchgate.net/publication/254804390
M. Liao and Y. Yao, “Applications of artificial intelligence-based modeling for bioenergy systems: A review,” May 01, 2021, Blackwell Publishing Ltd. doi: 10.1111/gcbb.12816.
D. Bork, S. J. Ali, and B. Roelens, “Conceptual Modeling and Artificial Intelligence: A Systematic Mapping Study,” in 28th Text REtrieval Conference, TREC 2019 - Proceedings, National Institute of Standards and Technology (NIST), 2019. doi: 10.1145/1122445.1122456.
A. Mohammed, M. Al-Mansour, A. M. Ghaithan, and A. Alshibani, “An optimization approach for improving steam production of heat recovery steam generator,” Sci Rep, vol. 15, no. 1, p. 3860, Dec. 2025, doi: 10.1038/s41598-025-87715-z.
A. Rezaie, G. Tsatsaronis, and U. Hellwig, “Thermal design and optimization of a heat recovery steam generator in a combined-cycle power plant by applying a genetic algorithm,” Energy, vol. 168, pp. 346–357, Feb. 2019, doi: 10.1016/j.energy.2018.11.047.
A. Ghaffari, R. Ahmadi, and M. Eyvazkhani, “Modeling and optimization of finless and finned tube heat recovery steam generators for cogeneration plants,” Engineering Reports, vol. 2, no. 11, Nov. 2020, doi: 10.1002/eng2.12262.
M. A. Ehyaei, A. Ahmadi, M. A. Rosen, and A. Davarpanah, “Thermodynamic optimization of a geothermal power plant with a genetic algorithm in two stages,” Processes, vol. 8, no. 10, pp. 1–16, Oct. 2020, doi: 10.3390/pr8101277.
S. Hussain, M. Al-Hitmi, S. Khaliq, A. Hussain, and M. A. Saqib, “Implementation and comparison of particle swarm optimization and genetic algorithm techniques in combined economic emission dispatch of an independent power plant,” Energies (Basel), vol. 12, no. 11, 2019, doi: 10.3390/en12112037.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Artificial Intelligence Recommendation System for Optimizing Steam Power Plant Heat Rate: A Conceptual Design
Pages: 177-188
Copyright (c) 2025 Lulu Ardiansyah, Hetty Rohayani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
