| Autor: |
Zhang, Chunlei Zhou, Donghai Xuan, Yuhan Miao, Xiaohu Luo, Wensi Liu, Yihong |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Energies; Volume 16; Issue 11; Pages: 4453 |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en16114453 |
| Popis: |
Since the cement industry acts as a significant contributor to carbon emissions in China, China’s national emission trading system has announced that it should be included in the system soon. However, current cement carbon accounting methods require high-resolution data from various processes on the production line, making it a cumbersome and costly process. To address this issue, this study explores the feasibility and reliability of using machine learning algorithms to develop electricity–carbon models. These models estimate carbon emissions based solely on electricity data, enabling faster and more cost-effective accounting of carbon in cement production. This study investigates the correlations between electricity data and carbon emissions for a large cement manufacturer in southern China. It compares the performance of models based on the supply of electricity (purchased electricity and waste heat electricity) with those based on the consumption of electricity (electricity used on the grinding machines in the production lines) to identify the key factor for carbon emission calculations. The identified best performing model showed high accuracy, with an R2 of 0.96, an RMSPE of 3.88%, and a MAPE of 2.56%. Based on this, the novel electricity–carbon model has the potential to act as one of the optional methods for carbon emissions accounting in the cement industry and to support carbon emissions data promotion within China’s national emission trading systems. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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