4.7 Article

Research on synchronous optimization effect of working fluid components and node parameters of organic Rankine cycle (ORC)

Journal

CASE STUDIES IN THERMAL ENGINEERING
Volume 30, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.csite.2021.101742

Keywords

Organic rankine cycle; Genetic algorithm; Optimization effect; Nested method

Categories

Funding

  1. National Natural Science Foundation of China [51906092]
  2. Jiangsu University of Science and Technology
  3. Scientific Research Foundation [1142921903]

Ask authors/readers for more resources

In this paper, a new numerical optimization algorithm is proposed to simultaneously optimize the components and operating parameters of the organic Rankine cycle. By applying it to single-stage and two-stage organic Rankine cycles, the optimization effect of the proposed numerical method can reach over 99.99% of the optimal effect, compared to the Nested method. Additionally, the optimization time is significantly decreased. After considering the working medium, the best performance reaches 99.52%. The results suggest that the optimization effect of the system decreases slowly as the complexity of the system and the number of independent variables increase. Compared to previous publications, the overall performance is improved by 1.68% with the new numerical optimization algorithm.
In this paper, a new numerical optimization algorithm which can simultaneously optimize the components and operating parameters of organic Rankine cycle is proposed and applied to single-stage organic Rankine cycle and two-stage organic Rankine cycle. Compare with the Nested method, the optimization effect of the numerical method proposed can reach more than 99.99% of the optimal effect if the working medium had not been considered in the two-stage organic Rankine cycle. The optimization time is only about 0.02% of the Nested method. 99.52% of the best performance after the working medium being considered. The results indicate that when the complexity of the system and the number of independent variables increase, the optimization effect of the system decreases slowly. Compared with the previous publication, the overall performance is finally improved 1.68% by the new numerical optimization algorithm.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available