Data-based analysis of energy system in papermaking process. Issue 7 (19th May 2018)

• Record Type: Journal Article
• Authors: Zhang, Yanzhong; Hong, Mengna; Li, Jigeng; Liu, Huanbin
• Is Part Of: Drying technology. Volume 36:Issue 7(2018)
• Page Start: 879
• Page End: 890
• Publication Date: 2018-05-19
• Subjects: Data-driven -- DBSCAN -- energy analysis -- energy-saving potential -- papermaking process; Drying -- Periodicals; Desiccation; 660.28426
• DOI: 10.1080/07373937.2017.1365081
• Languages: English
• ISSNs: 0737-3937

Abstract

ABSTRACT: Papermaking process is highly energy-intensive, complicated, and influenced by multiple unit processes, which plays an important role in the energy consumption, especially of thermal energy and electricity energy. An energy management system (EMS) was built to acquire the systematic, consistent, and accurate online information of the production process in a typical coated paperboard mill. The EMS first fulfilled the online energy information calculation from the process data acquisition and integration, second provided the massive historical data to find out the internal variation of production process for inferring decisions. In a case study, the online energy flow analysis and the estimation of specific energy consumption (SEC) were carried out. Based on data-driven techniques, three different operation conditions were recognized by adopting the kernel principal component analysis and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) when the basis weight was 220 g/m 2 . The energy consumption was described in each cluster. Particular attention needed to be paid to LP steam in the high-speed production process with the change in environmental temperature and humidity in cluster 3. A novel indicator was proposed to appraise the level of energy consumption in different paper machine speeds. The benchmarking SEC was extracted from the massive historical data for the estimation of energy-saving potential. With the designed capacity of 300, 000 ton/year, the potential economic benefits will be 20127 thousand RMB/year.