Document Type
Article
Journal/Book Title/Conference
Energy and AI
Volume
15
Publisher
Elsevier BV
Publication Date
10-21-2023
Journal Article Version
Version of Record
First Page
1
Last Page
11
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Abstract
In recent years, various types of surrogate optimization models have been proposed to reduce the computational time and to improve the emulation accuracy. In this study, by leveraging an ANN surrogate model developed earlier, a comprehensive and efficient optimization algorithm is conceived for the global optimal design of an integrated regenerative methanol transcritical cycle. It combines a unique converging/diverging classifier model into the surrogate model to form a surrogate-based model, which significantly improves the prediction accuracy of the objective function. Six binary classifiers are explored and the multi-layer feed-forward (MLF) neural network classifier is selected. In addition, within the five global optimizers being explored, the basin-hopping (BH) and dual-annealing (DA) are selected. The optimal surrogate-based model and global optimizers are then combined to form a unique surrogate-optimizer model. The surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results, the time consumption of the surrogate-optimizer model during the optimization searching process is 99% less than that of the physics-based model. As the results, the surrogate-optimizer model is slightly outperformed by the physics-based model in terms of the optimization results, where the Levelized Cost of Energy (LCOE) of the Surrogate-DA and Surrogate-BH models are 77.912 and 78.876 $/MWh, respectively, compared to the 77.190 $/MWh of the Baseline model with fairly close penalties between them. In the meantime, the time consumption of the surrogate-optimizer model during the optimization searching process is 99% less than that of the physics-based model.
Recommended Citation
Zhang Yili, Bryan Jacob, Richards Geordie, Wang Hailei, Development of surrogate-optimization models for a novel transcritical power cycle integrated with a small modular reactor, Energy and AI, 15 (2023), https://doi.org/10.1016/j.egyai.2023.100311.