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Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

Author

Listed:
  • Alrbai, Mohammad
  • Al-Dahidi, Sameer
  • Alahmer, Hussein
  • Al-Ghussain, Loiy
  • Al-Rbaihat, Raed
  • Hayajneh, Hassan
  • Alahmer, Ali

Abstract

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. The Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93 % and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

Suggested Citation

  • Alrbai, Mohammad & Al-Dahidi, Sameer & Alahmer, Hussein & Al-Ghussain, Loiy & Al-Rbaihat, Raed & Hayajneh, Hassan & Alahmer, Ali, 2024. "Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224026033
    DOI: 10.1016/j.energy.2024.132829
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    References listed on IDEAS

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    2. Raed Al-Rbaihat, 2025. "Sensitivity Analysis of a Hybrid PV-WT Hydrogen Production System via an Electrolyzer and Fuel Cell Using TRNSYS in Coastal Regions: A Case Study in Perth, Australia," Energies, MDPI, vol. 18(12), pages 1-32, June.
    3. Wang, Hai-Xiao & Lei, Biao & Wu, Yu-Ting & Zhang, Ye-Qiang & Du, Yan-Jun & Zhang, Xiao-Ming & Yang, Pei-Hong, 2025. "Performance improvement and multi-objective optimization of energy, economic, and environmental factors in organic Rankine cycle using machine learning-driven quasi-two-stage single screw expander," Energy, Elsevier, vol. 324(C).
    4. Zhao, Chunhao & Cheng, Chao & Lan, Pengfei & Liu, Ran & Zhang, Heng & Gao, Dan & Liu, Jizhen, 2026. "A dual-steam extraction strategy for molten salt heat storage system enabling thermo-electric decoupling in a 660 MW CHP plant," Energy, Elsevier, vol. 344(C).
    5. Ha, Byeongmin & Lee, Hyeonjeong & Hwangbo, Soonho, 2025. "Towards sustainable energy efficiency: Data-driven optimization in large-scale plants using machine learning applications," Energy, Elsevier, vol. 331(C).

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