Energy system optimization model for tissue papermaking process. (March 2021)

• Record Type: Journal Article
• Authors: Zhang, Yang; Hong, Mengna; Li, Jigeng; Ren, Jingzheng; Man, Yi
• Is Part Of: Computers & chemical engineering. Volume 146(2021)
• Publication Date: 2021-03
• Subjects: Tissue paper -- Drying process -- Process simulation -- Energy system optimization; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107220
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A mathematical model is proposed for tissue paper drying process. The model realizes the accurate simulation of key parameters in the drying process. The model provides global optimization scheme for key operating parameters. The model improves the competitiveness by reducing 8.72% of the drying cost. Abstract: The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%.