Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle

Abstract

Agricultural resource management is essential in present-day society. The hydrothermal carbonization (HTC) is a promising technology for recovering valuable resources from wastes. However, there is a paucity of efficient approaches to predict and comprehend the agricultural waste HTC process. Machine learning modeling approaches have emerged as an important tool for simulating thermochemical reaction processes such as HTC. However, previous studies all limit to predicting products properties of single-component HTC, which are contrary to the complex composition of raw materials, and demonstrate poor generalization performance. Herein, the homogeneity graph data is employed to represent the HTC data with variable components. Then, based on graph encoder-decoder transformer method, a bi-directional prediction model is established to model the HTC process of agroforestry and livestock wastes. In the case of the forward prediction, the model can predict the composition and yield of hydrochar according to the given HTC conditions. The comparisons with the experimental data other than the training and validation datasets show that the prediction accuracy (R2) of ash, carbon and nitrogen contents of the hydrochar are 0.895, 0.908 and 0.840, respectively, which proves the good generalizability of the model. Moreover, owing to the embedding vector, which connects the reactant data with the product data in HTC, the model can also achieve backward prediction of HTC reaction conditions such as raw material composition and reaction temperature. Furthermore, by dimensionality reduction analysis of the embedding vector in the model, the similarity of any two HTC reactions can be quantified. The established HTC-graph neural network (GNN) model shows both high accuracy and good generalization ability in bi-directional prediction of HTC process. Based on the effectiveness of the proposed model and the given evaluation indexes, the corresponding HTC parameters can be determined, which greatly benefits to the resources saving and energy consumption reduction.