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Chunhui He

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3 papers

1 author row

EAAI Journal 2026 Journal Article

A hybrid method for anomaly data detection and reconstruction in proton exchange membrane fuel cells to enhance life prediction accuracy

Donghai Hu
Yan Sun
Yinjie Xu
Yuan Li
Biaoyi Liu
Hua Ding
Jing Wang
Hongwei Liu

Life prediction of proton exchange membrane fuel cell (PEMFC) is highly dependent on high-quality data, so accurate detection and effective reconstruction of abnormal data are crucial. The existing research has problems such as a single abnormal data detection model, overly simple reconstruction methods, and insufficient linkage with life prediction. This poses challenges for abnormal detection, data reconstruction and life prediction. This paper proposes an abnormal detection and data reconstruction model based on Variational Autoencoder-Self-Attention Conditional Generative Adversarial Network (VAE-SACGAN). A closed-loop evaluation framework of “detection-reconstruction-lifespan prediction” has been constructed and compared with benchmark models under different traffic and data conditions. The results of abnormal data detection show that the true positive rate of different types of abnormal data exceeds 90%. The results of abnormal data reconstruction show that the Root Mean Square Error (RMSE)/Mean Absolute Error (MAE) is significantly reduced compared with the Generative Adversarial Networks model. The air inlet pressure is reduced from 10. 85/8. 72 to 1. 96/1. 60, and the hydrogen inlet temperature is reduced from 1. 86/1. 54 to 0. 52/0. 42. The results of life prediction show that under congested traffic conditions, compared with abnormal data, the RMSE/MAE of the reconstructed life prediction are significantly reduced. The air inlet pressure is reduced from 3. 42/2. 48 to 1. 84/1. 29, and the hydrogen inlet temperature is reduced from 2. 70/2. 00 to 1. 84/1. 28. The results validate the combined advantages of the model in terms of abnormal detection, data reconstruction and life prediction stability.

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EAAI Journal 2025 Journal Article

An integrated exergy efficiency and machine learning method for optimizing organic solid waste gasification process

Wenni Chen
Xianan Xiang
Sha Liu
Jun Guo
Tao Li
Xuehua Zhou
Deyong Peng
Zhiya Deng

Organic solid waste (OSW) gasification is a critical pathway toward sustainable energy utilization. This study develops an integrated prediction model by combining exergy efficiency-based analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE) with machine learning techniques. The model aims to select the optimal gasifier type and operational parameters based on OSW characteristics and processing capacities. Exergy efficiency derived from experimental data is used to construct AHP-FCE scores, which are then predicted using eight machine learning algorithms. Gradient boosting decision tree (GBDT) achieves the best performance. The prediction model is applied to three practical cases. For a project with an annual processing capacity of 2000 tons of refuse-derived fuel (RDF), the model consistently recommends the downdraft fixed-bed gasifier (DBG). In a corn straw gasification project processing 11, 000 tons per year, the bubbling fluidized-bed gasifier (BBG) is identified as the optimal choice. For a bamboo chip gasification project with an annual capacity of 150, 000 tons, the model suggests using the circulating fluidized-bed gasifier (CFBG) for reduction objectives and the dual fluidized-bed gasifier (DFBG) for hydrogen production goals. Additionally, the model shows significant potential. It can also be applied to optimize other complex systems that require balancing multiple influencing factors.

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EAAI Journal 2025 Journal Article

Generalized deep neural network for seismic site response prediction with transfer learning

Lin Li
Feng Jin
Duruo Huang
Chunhui He
Fulong Ma

Accurate prediction of site-specific seismic responses plays a pivotal role in evaluating earthquake effects on infrastructure. Traditional physics-based methods suffer from inherent model assumptions, significant parameter uncertainty, and high computational costs. This study proposes a generalized deep neural network that integrates seismic motion data and site information to predict three-directional seismic responses across various site types. Trained on an extensive dataset of recorded data from Kiban Kyoshin Network in Japan, the model demonstrated excellent performance on the test set, with correlation coefficients reaching 97 % between the predicted and target results. Utilizing transfer learning techniques, it was adapted to seismic response prediction at new sites not included in the training set. Compared to the state-of-the-art finite element method, the retrained model significantly improved prediction accuracy, with an overall average error reduction of approximately 50 %. Additionally, the model effectively captured the nonlinear response characteristics of a site during strong seismic events without any strong motion data to retrain. The proposed model demonstrated superior prediction accuracy, higher computational efficiency, and stronger generalization capabilities compared to traditional physics-based models.

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