Calcium sulfate (CaSO 4 ) is a promising oxygen carrier for chemical-looping combustion system. The release of sulfurous gas in the circulating of Ca-based oxygen carrier is a crucial problem. In this paper, it is found that the released amount of sulfurous gas is greatly affected by the partial pressure of reductive gas. If the partial pressure of the mixed reductive gases, composed of H 2 and CO, maintains higher than 40 kPa, the sulfurous gas can be completely eliminated even at the reacting temperature higher than 1,000°C. Therefore, a new chemical-looping combustion system based on CaSO 4 and NiO oxygen carrier, without any sulfur evolution, is simulated using Aspen Plus software. In the system, the syngas is generated from the steam gasification of coal and reacts with CaSO 4 and NiO consecutively. The sulfur release occurred in the circulating of Ca-based oxygen carriers can be prevented because the concentrations of CO and H 2 keep higher than 45% in the presence of Cabased oxygen carriers. The suitable operating regime, in which auto-thermally operating of the system, zero-sulfur release and easy sequestration of CO 2 from the flue gas are realized, is proposed in this paper. The effects of fuel reactor temperature, air reactor temperature and the recycle rate of oxygen carrier on the released heat from the system and the concentration of CO 2 and H 2 O are also studied in the paper.

Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.

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