NDLI: Research on energy consumption analysis of beer brewing process

Content Provider	IEEE Xplore Digital Library
Author	Jing Bai Tiecheng Pu Jisheng Xing Guocheng Niu Shuran Zhang Qiang Liu
Copyright Year	2011
Description	Author affiliation: Key Laboratory of Integrated, Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, China (Qiang Liu) \|\| Shenyang Energy Saving Supervision, Center Shenyang, China (Shuran Zhang) \|\| College of Electrical and Information Engineering, Beihua University, Jilin, China (Jing Bai; Tiecheng Pu; Jisheng Xing; Guocheng Niu)
Abstract	Because there are many coterminous workshop sections in beer brewing process and the reaction mechanism is very complex, it is difficult to analyze the energy consumption. Aiming at the problem, the analysis method of energy consumption is proposed based on the production data. First, energy consumption of beer brewing process is analyzed using the data envelopment analysis (DEA). The relative efficient productive batches are obtained. Secondly, the less dimensional production data are obtained using the principal component analysis (PCA) which depresses the correlation among the variables. Finally, the energy consumption of brewing process is modeled using radial basis function neural network (RBFNN), and the energy consumption model with the minimum error is also built by adjusting the width of the radial basis function. The simulation result shows that the model can be used to analyze and predict the energy consumption of the beer brewing process effectively.
Starting Page	182
Ending Page	185
File Size	1010115
Page Count	4
File Format	PDF
ISBN	9781612840871
e-ISBN	9781612840888
DOI	10.1109/EMEIT.2011.6022892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption Analytical models Conferences Production Data models Neural network Biological neural networks Principal component analysis Data envelopment analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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