China accounts for more than 50% of the global total cement production, the emission of pollutants in the cement industry occupies a large proportion of the industries. Some provinces have first carried out in-depth treatment of the cement industry, and the national level will also introduce an implementation plan for ultra-low emission transformation of the cement industry to promote continuous improvement of China's air quality. In this work, a pollutant emission inventory covering the full life cycle of the cement industry, including organized emission, unorganized emission and transportation, was established based on pollutant discharge permit, Continuous Emission Monitoring System (CEMS) database and field investigation. And, the emission reduction potential of particulate matter (PM), sulfur dioxide (SO2) and nitrogen oxide (NOx) after the implementation of ultra-low emission transformation in cement industry was evaluated. The results show that the existing dedusting, desulphurization and denitration technologies can control the concentrations of PM, SO2 and NOx below 10 mg/m3, 35 mg/m3 and 50 mg/m3, respectively. The emission reduction potential of PM, SO2 and NOx in cement industry is 259.54 kt, 14.98 kt and 607.04 kt, corresponding emission reduction ratio is 37%, 24% and 64%, respectively. In addition, this work reveals the spatial characteristics of pollutant emission level and emission reduction potential of the cement industry, which is conducive to the rational layout of ultra-low emission transformation, and push forward the transformation task by area and stage. 

Full life cycle emission reduction potential of ultra-low emission transformation in China's cement industry

Conservation and efficient use of water resources are key to combating global climate change. The development of low-carbon industries plays an important role in promoting the protection of water resources. Based on this, this paper combines the measures of green production and consumption, and uses the concept of low-carbon packaging design to study the impact of water resource protection. First of all, this paper studies the impact of the packaging industry on the ecological environment, and combines the concept of global low-carbon development to implement a low-carbon packaging industry structure. Secondly, adhering to the concept of sustainable development, from green packaging design to green packaging consumption, this paper uses the water resources protection analysis impact model to empirically analyze the impact of the low-carbon packaging industry on water resources protection. The experimental results showed that after the implementation of the low-carbon packaging industry structure model in Zhejiang Province, China, the impact efficiency of the region on water resources was as high as 90%, and the sewage discharge showed a decreasing and stable trend, sewage discharge in the light industrial zone was reduced from 120,000 to 80,000 cubic meters, and two water pollutants, chemical oxygen demand (COD) and biochemical oxygen demand (BOD), were reduced by 4,600 and 2,200 tons respectively. Studies have shown that low-carbon packaging design can reduce carbon emissions from packaging production to a certain extent and reduce pollutant emissions, thereby improving the efficiency of water resource protection and management. 

Investigation on the impact of low carbon packaging design on water resource protection

Lithium-ion battery state of health (SOH) estimation remains a significant challenge in battery management systems due to the sophisticated electrochemical processes within the battery. As a model-free method, data-driven-based method has shown great potential in battery SOH estimation. However, the existing data-driven approach requires a large dataset and shows low model adaptability in SOH estimation among different battery samples. To address the issues, this paper proposes a transfer learning (TL)-based technique coupled with the multi-layer perceptron (MLP) and Spearman analysis to realise battery SOH estimation. Firstly, it extracts health features using Spearman analysis based on early-age data of the battery. Next, it builds the basic MLP model relying on the extracted features. Then, the TL model is developed by retraining the MLP model based on the partial data from the target battery. Finally, the retrained model is used to estimate the battery SOH in the rest of the aging cycles. The results demonstrate the high accuracy performance of the proposed method in the battery SOH estimation with an R_score of 0.9733 and RMSE value of 0.53 % in a full-charge stage, implying the prospect of battery SOH estimation using the TL technique.

A MLP-Based Transfer Learning Model Using EIS Health Features for State of Health Estimation of Lithium-Ion Battery

Preparation and characterizations of reduced graphene oxide reinforced silicon composites

Innovative synthesis and comprehensive electrochemical evaluation of FeVO4 for enhanced sodium-ion battery performance

Research on the utilization of ultra-long carbon nanotubes in lithium-ion batteries based on an environment-friendly society

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Research on the utilization of ultra-long carbon nanotubes in lithium-ion batteries based on an environment-friendly society