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| Study on Interval Forecasting Model of Carbon Dioxide Emission from Papermaking Process Based on Hybrid Method |
| Received:November 21, 2024 |
| DOI：10.11980/j.issn.0254-508X.2025.02.007 |
| Key Words:papermaking process carbon dioxide emissions interval forecasting model modeling and simulation |
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| Abstract:The papermaking industry is a major source of carbon emissions in China. Accurate carbon dioxide （CO₂） emission forecasting is crucial for developing process optimization models aimed at minimizing emissions， thereby promoting green and sustainable development. Given the significant uncertainty and high volatility in carbon emissions of papermaking process， this study proposed a CO₂ interval forecasting model based on Variational Mode Decomposition （VMD）， Bayesian Optimization-Back Propagation Neural Network （BO-BPNN）， and Quantile Regression （QR）. First， VMD was applied to decompose the raw data signals. Then， a BO-BPNN based model was developed for CO₂ emission forecasting， followed by the construction of the interval forecasting model using QR. To evaluate the model’s performance， actual production data from a paper mill were collected， and a comparative model based on VMD-BO-Least Squares Support Vector Machine （LSSVM）-QR was established. The results indicated that the proposed model achieves superior accuracy， with R² of 0.993 6 and a prediction interval confidence probability of 0.892 4， outperforming the comparison model. This model demonstrated significant practical value for papermaking industry. |
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