Output prediction of alpha-type Stirling engines using gradient boosted regression trees and corresponding heat recovery system optimization based on improved NSGA-II

Abstract

Climate change is becoming a pressing global concern, and the search for new energy and energy recovery technologies is becoming a worldwide research imperative. The broad adaptability of the Stirling engine to a wide variety of heat sources makes it a promising technology for industrial waste heat recovery and solar thermal generation. The operation of the Stirling engine involves a multi-physical coupled process of heat transfer and mechanics as well as non-linear losses due to mechanical friction and gas charge leaking. Therefore, accurate prediction of Stirling engine power output through theoretical analysis is complex and costly. Emerging machine learning algorithms like Gradient Boosted Regression Trees (GBRT) can offer new approaches to solve this problem. The GBRT model consists of multiple decision trees that branch by exhausting thresholds for all features under study to find the best split structure for data regression, and the principle of GBRT gives it the natural advantage of finding a wide range of distinguishing features and combinations, and a powerful generalization capability. A GBRT forecasting model is thus constructed to model the output power of Alpha-type Stirling engines. Test data from the General Motors 4L23 Stirling Engine are applied as the training and test set. Results from the random test set accounting for 25% of the total samples indicate that the GBRT model has a prediction accuracy of 96.23%. Furthermore, a regional microgrid containing Stirling engines, photovoltaic panels and batteries for industrial waste heat recovery is constructed and an evaluation system for energy supply performance is also established. Finally, based on the proposed power output model, multi-objective optimization based on improved NSGA-II is implemented, providing guidance for industrial application of Stirling engines.

Keywords

• Stirling engine
• Output prediction
• GBRT
• Heat recovery system
• Improved NSGA-II