Hydrogen yield prediction for supercritical water gasification based on generative adversarial network data augmentation

Machine learning methods can accurately predict the hydrogen yield of supercritical water gasification (SCWG), which can provide a reference for SCWG optimization. However, the time cost of obtaining SCWG data by experiments is high, and the prediction accuracy of the SCWG model is low when the sample number is small. For this problem, a novel sample generation method based on the generative adversarial network (GAN) and the hybrid strategy is proposed to achieve data augmentation for SCWG hydrogen yield prediction. The hybrid strategy that combines the cross-validation and looping iteration methods can reduce the randomness of the GAN, thereby improving the generated sample quality. In addition, the least squares support vector regression is adopted to construct a hydrogen yield prediction model. A case study was conducted on a small sample SCWG dataset of Yimin lignite with reaction temperatures in the range of 650–850 °C. The results show that after expanding the small sample dataset by the single GAN, the average RMSE and MAE of the prediction model are reduced by 20.43% and 11.58%, respectively. The proposed sample generation method is applied to the SCWG hydrogen yield prediction. The average RMSE and MAE are as low as 1.1737 mol/kg and 1.0171 mol/kg, respectively, while the average R2 reaches 0.9457. The average RMSE and MAE are further reduced by 18.44% and 15.29% based on the single GAN model, respectively. It indicates that the hybrid strategy improves the quality and reliability of the generated samples, and the proposed method can effectively achieve data augmentation for SCWG hydrogen yield prediction, which can provide ideas for the small sample SCWG modeling.