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Many industrial processes belong to complex thermal system coupled by multiple heat sources, in which the energy distribution and transmission for each heat source is closely related to its adjacent neighbors, and the thermal dynamics have obvious nonlinear time-series characteristics, these factors limit the modeling and predict accuracy for this type of systems in actual practice. In this paper, a convolution and memory network-based spatiotemporal modeling approach is proposed to model the nonlinear spatial features and temporal dynamics of these heating systems. First, a deformation convolutional neural network (D-CNN) strategy is developed to extract the spatial features, it designs a deformable kernel matrix for convolution layer to obtain the spatial multi-scale context information of the DPS, and the feature order is reduced by pooling operation. Then, a cascade long-short time memory network (LSTM) model is constructed to model the temporal dynamics of the time-series data. In this model, the sliding window method is used to obtain the sequential dataset for several sampling time, which is taken as the input of each LSTM unit to reconstruct the nonlinear temporal dynamics in this type of systems. The effect of the proposed model for spatiotemporal dynamics is verified by the experiment of serial-connected lithium batteries (S-LIBs), and the result shows that the proposed model has smaller modeling errors for spatiotemporal dynamics than the other two methods. • A convolution and memory network-based modeling approach is proposed to model the spatiotemporal dynamics in thermal processes. • A deformation convolutional neural network strategy is developed to extract the spatial features using a deformable kernel matrix. • A cascade long-short time memory network model is constructed for the temporal dynamics of the time-series data. • The effect of the modeling approach is verified by the experiment of serial-connected lithium batteries (S-LIBs). [ABSTRACT FROM AUTHOR]

Abstract: Lithium/organic and lithium/sulfur batteries have attracted great interest lately due to their high theoretical specific capacity and potential low cost. However, two major roadblocks currently prevent industrial development of these kind of batteries: (i) the progressive dissolution of active material in the electrolyte which hinders cyclability of the devices and; (ii) the electrical insulating nature of organic or sulfur materials. In this work, we show an elegant solution to solve both, the conductivity and dissolution issues. We covalently functionalized multi‐walled carbon nanotubes (MWNTs) with electrochemically active thiolated molecules. The MWNTs insure a well distributed electronic conductivity inside the positive electrode and serve as a support for a covalent immobilization of the thiolated active species. Compared to electrodes formed by simply mixing carbon nanotubes with thiol‐containing molecules, covalently functionalized MWNT materials present an excellent stability over prolonged cycling and a promising specific capacity, in the range of 100 mAh gelectrode−1, i. e., including carbon and current collector masses. [ABSTRACT FROM AUTHOR]