Biogas Generator Temperature Monitoring Based on Time Series Algorithm

Zuo Xun Wang; Ying Chun Zhang; Gui Juan Wang; Rong Ai Meng

doi:10.4028/www.scientific.net/AMM.182-183.1230

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 182-183Biogas Generator Temperature Monitoring Based on...

Biogas Generator Temperature Monitoring Based on Time Series Algorithm

Abstract:

The biogas generator temperature control is very important to the temperature of the biogas generator for effective control, So the temperature must be monitored. Pre-estimated by measuring the temperature and fast temperature control is the most effective means. In this paper, using time series analysis method, we establish three prediction models, which are namely autoregressive model, moving average model, hybrid model. By comparison of three models, the final choice to predict the temperature of the biogas generator is mixed model analysis of time series. The test error is very small, indicating that the method has some practical value.

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Periodical:

Applied Mechanics and Materials (Volumes 182-183)

Pages:

1230-1235

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.182-183.1230

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Online since:

June 2012

Authors:

Zuo Xun Wang, Ying Chun Zhang, Gui Juan Wang, Rong Ai Meng

Keywords:

Biogas Generator, Temperature Monitoring, Time Series Analysis

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References

[1] Ran Guowei, Zhang Rukun, Feng Aiguo. Biogas power generation technology the current situation and the development of Analysis [J]. Agricultural Research, 2009, 3 (2) : 109-114.

Google Scholar

[2] Yin Zhiguang, Zhong Tao, Li Dequan. Dynamic data system strategy time series modeling [J]. Chongqing University, 2008, 31 (5) : 558-562.

Google Scholar

[3] Kalpakis K, Gada D. Distance measures for effective clustering ofarima timeseries [C]/Proceedings of the 2001 IEEE InternationalConference on Data Mining, San Jose, CA, 2007: 273- 280.

DOI: 10.1109/icdm.2001.989529

Google Scholar

[4] Mellit A, Benghanem M, Kalogirou S A. An adaptive wavelet - networkmodel for forecasting daily totalsolar- radiation[J]. Applied Energy, 2006, 83: 705- 722.

DOI: 10.1016/j.apenergy.2005.06.003

Google Scholar

[5] Wang Rongxin. Random process [M]. Xi'an: Xi'an Jiaotong University Press, (1987).

Google Scholar

[6] Tianzheng. Dynamic data processing theory and methods - time series analysis [M]. Xi'an: Northwest Industry University Press, (1995).

Google Scholar

[7] Luo Hang, Hou Jun, Long bing. Based on loose, wavelet neural network time series prediction [J]. Computer Engineering and Applications, 2008, 44 (8) : 16-19.

Google Scholar