In the present work, artificial neural network (ANN)-based machine learning models are developed to predict biomass pyrolysis kinetics. Data sets of thermogravimetric analysis and feedstock characterization from a diverse range of biomasses were used to build and test the networks. The composition of the raw biomass material was classified and used as input parameters of ANN models. Three models, which use ultimate analysis, proximate analysis, and three components as input parameters, were developed in this study. A total of 32 types of biomass raw materials were used, and 270 sets of kinetic data were obtained according to different pyrolysis conversion rates ranging from 0.1 to 0.9. Results show that increasing the number of neurons can improve the prediction accuracy. The optimized neuron number is 7-11. The largest relative deviation between experimental and modeling results for the three models are 20.80%, 14.06% and 12.85%, respectively, which proves that using cellulose, hemicellulose, and lignin as input parameters of the neural network model can better predict the activation energy of pyrolysis at each reaction stage. The particle swarm optimization algorithm could significantly improve the prediction accuracy of the BP-ANN model. The largest deviation for activated energy prediction decreases from 12.85% to 6.72%.

A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry.