E3S Web Conf.
Volume 118, 20192019 4th International Conference on Advances in Energy and Environment Research (ICAEER 2019)
|Number of page(s)||6|
|Section||Environment Engineering, Environmental Safety and Detection|
|Published online||04 October 2019|
Multi-Objective Optimization of Organic Rankine Cycle for Low-Grade Waste Heat Recovery
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
2 Beijing Huahang Shengshi Energy Technology Co., Ltd, Beijing, China
* Corresponding author: email@example.com
The organic Rankine cycle (ORC) is considered as one of the most viable technology to recover low-grade waste heat. A multi-objective optimization model is established to simultaneously derive the maximum exergy efficiency and the minimum electricity production cost (EPC) of a specific ORC system by employing the genetic algorithm (GA). Evaporation temperature and condensation temperature are selected as decision variables. At first, variations of exergy efficiency and EPC with evaporation temperature and condensation temperature are investigated respectively using R245fa, R245ca, R600, R600a, R601 and R601a as working fluids. Subsequently, a multi-objective optimization is performed and the Pareto frontiers for various working fluids are obtained. Results indicate that performance of the specific ORC system with R245fa as working fluid is better that with other working fluids.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.