Search

The simultaneous regulation of production efficiency and equipment maintenance in intelligent production lines poses a challenging problem. Existing approaches addressing this issue often separate the regulation of equipment maintenance and load balancing, lacking dynamic indicators to characterize the operational status and equipment workload. Inspired by the cardiac electrical activity recorded from human electrocardiogram (ECG), the electric drive signal of the equipment is proposed as an analogous measure to monitor equipment performance and workload variations. Thereby, the implementation mechanism and working characteristics of equipment ECG (EECG) are put forward for reconfigurable mixed-model assembly. Moreover, the monitoring of equipment performance based on deep learning is explored, leveraging the EECG features combined with multi-source heterogeneous data. The variations of equipment workload are tracked through the construction of a population difference hash analysis of the ECG data flow, along with the characterization of equipment power through electric signals. Additionally, an EECG-driven synchronous mapping approach is proposed to address steady disturbance, considering both workload imbalance and the degeneracy effect of the equipment. The reconfigurability of the intelligent production line enables the proposed mechanism of similarity matching of EECG features through the reconfiguration of the software manufacturing system and hardware physical equipment. Finally, the EECG-based solution is validated on a laboratory-level prototype platform, demonstrating that the robust running of the assembly process can be ensured even in the presence of internal and external disturbances. [ABSTRACT FROM AUTHOR]

Energy saving and emissions reduction in the iron and steel industry is a significant challenge to achieve carbon neutrality and sustainable development. Many studies focus on the optimization of materials, energy and carbon emissions but lack of optimization strategies in the iron and steel industry. A comprehensive and effective system model is still needed to optimize the CO2 emissions to face the production planning of a company in the future. This article applies industrial metabolism to develop a practical optimization model for improving materials consumption and energy consumption in coking, sintering, pelleting, ironmaking and steelmaking, and the minimum carbon emission is set as the optimization objective. The results show that carbon emissions reduce 148.65 kg/ton crude steel compared with the original data with a yield of 8.4 Mt crude steel. In addition, the optimum material and energy consumption of each production unit under different production plans is studied. If breakthrough technologies are not applied to long-route processes, improving scrap steel ratio is the most promising approach for low-carbon manufacturing in the iron and steel industry in the future. [ABSTRACT FROM AUTHOR]

Although graphene oxide (GO) has been used in multidisciplinary areas due to its excellent physicochemical properties, its environmental behavior and fate are still largely unclear. In this study, batch experiments, spectroscopy analysis and theoretical calculations were addressed to promote a more comprehensive understanding toward the coagulation behavior of GO onto TiO2 under various environmental conditions (pH, co-existing ions, temperature, etc.). The results indicated that neutral pH was beneficial to the removal of GO due to the electrostatic interaction. The presence of cations accelerated GO coagulation significantly owing to the influence of electrical double layer compression. On the contrary, the presence of anions improved the stability of GO primarily because of electrostatic repulsion and steric hindrance. Results of XRD, FTIR and XPS analysis indicated that the coagulation of GO on TiO2 was mainly dominated by electrostatic interactions and hydrogen bonds, which were further evidenced by DFT calculations. The high binding energy further indicated the stability of GO + TiO2 system, suggesting that TiO2 can be used as an effective coagulant for the efficient elimination and coagulation of GO from aqueous solutions. These findings might likely lead to a better understanding of the migration and transformation of carbon nanomaterials in the natural environment. [ABSTRACT FROM AUTHOR]