Certeza, L. V. R. D. T., Purnama, A. R., Ahsan, A., Low, J. S. C., & Lu, W. F. (2025). Modelling and economic assessment of organic Rankine cycle integration into an inter-plant heat exchanger network. Applied Energy, 394, 126126. https://doi.org/10.1016/j.apenergy.2025.126126
Abstract:
Establishing energy-based industrial symbiosis networks (EISNs) through inter-plant heat integration is a collaborative energy efficiency initiative to reduce industrial energy consumption. Its benefits can be improved through organic Rankine cycle (ORC) integration into an inter-plant heat exchanger network (HEN) to generate electricity. In this study, a novel nonlinear programming (NLP) model is formulated for inter-plant HEN-ORC integration optimization. It improves the computational performance of conventional mixed-integer nonlinear programming (MINLP) models and the accuracy of total EISN cost computation by using chemical engineering plant cost indices (CEPCIs) to adjust capital cost values to a common time. Furthermore, this study also proposes an EISN economic viability assessment methodology based on Shapley value computation and based on social welfare, Rawlsian welfare, and Nash allocation schemes to determine the new individual cost of each plant if it joins an EISN. The model and methodology have been applied to two case studies. Results reveal that Nash allocation can minimize the total EISN cost while maximizing the savings attained by each plant, thereby making it the superior cost allocation method among the four. Furthermore, results show that inter-plant HEN-ORC integration can increase an EISN's economic viability. However, this depends on whether there is a significant difference between the cold and hot utility prices. Lastly, results indicate that limiting the number of superstructure stages to just one can result in a more realistically implementable EISN configuration. Overall, the proposed model and methodology have been demonstrated to yield pragmatic insights on the cost-effectiveness of inter-plant HEN-ORC integration.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research is supported by the Agency for Science, Technology, and Research (A*STAR), Singapore through the Singapore International Graduate Award.