Hydrate decomposition

During gas hydrate extraction, insufficient heating or depressurization can lead to incomplete hydrate decomposition, resultingin lower-than-expected actual gas production. In this paper, a method for assessing the degree of hydrate decomposition based on an electronic nosesystem is proposed. First, the electronic nose system was developed tocollect the information of gas produced by gas hydrate decompositionunder different humidity conditions. Then, a Convolutional NeuralNetwork combined with Domain Adaptive Compensation featureextractor (CNN-DAC) was used to extract moisture-insensitive features, which can improve the humidity generalization ability of the assessment model. Finally, a CNN-DAC-RF model by combining CNN-DAC withrandom forest (RF) was proposed, which can accurately assess the hydrate decomposition degree level. The experimental results show that the accuracy of the model reached 98.67%. In the comparison experiments with other feature extraction methods, the classification accuracy ofCNN-DAC-RF was improved by 1.32% in the source domain (high humidity data), and by 42.49% in the target domain (lowhumidity data). In summary, the combination of CNN-DAC-RF and electronic nose provides a reliable technical means for the assessment of the degree of hydrate decomposition during hydrate mining.