A Study on CNN based Production Yield Prediction Algorithm for Increasing Process Efficiency of Biogas Plant

  • Shin, Jaekwon (Fivetek Co., Ltd.) ;
  • Kim, Jintae (Fivetek Co., Ltd.) ;
  • Lee, Beomhee (Dept. of Media IT Engineering, Seoul National Univ. of Science and Technology) ;
  • Lee, Junghoon (Dept. of Electrical Information Control, Dongseoul Univ) ;
  • Lee, Jisung (Intelligent Robot System Research Group, ETRI) ;
  • Jeong, Seongyeob (Dept. of Environmental Energy Engineering, Kyonggi Univ.) ;
  • Chang, Soonwoong (Dept. of Environmental Energy Engineering, Kyonggi Univ.)
  • Received : 2018.02.10
  • Accepted : 2018.03.05
  • Published : 2018.03.31


Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.


Biogas;Biogas plant;CNN;Production yield


Grant : Development of Biogas plant process and Integrated Safety Inspection Solution based on facility IoT

Supported by : Institute for Information & communications Technology Promotion(IITP)


  1. Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu, "3D Convolutional Neural Networks for Human Action Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 1, pp. 221-231, January 2013.
  2. J. Schmidhuber, "Deep learning in neural networks: An overview", Neural Networks, Vol.61, pp. 85-117, 2015.
  3. J. Hoffmann., O. Navarro, F. Kastner, B.Janben, M.Hubner., "A Survey on CNN and RNN Implementation", IARIA, PESARO : The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications, pp. 33-39, 2017.
  4. Richard Arthur, Martina Francisca Baidoo, Edward Antwi, "Biogas as a potential renewable energy source: A Ghanaian case study", Renewable Energy 36, Vol. 36, No. 5, pp. 1510-1516, May 2011.
  5. Joonpyo Lee, Soonchul Park, Ho Kang, Changkeun Wang and Jaehyuk Hyun, 2017, "Economic Analysis of Food Waste Biogas Plants," New & Renewable Energy, Vol. 13, No. 3, pp. 65-72. September 2017.
  6. Korea Biogas Inc,
  7. Martina Poschl, Shane Ward, Philip Owende, "Evaluation of energy efficiency of various biogas production and utilization pathways", Applied Energy, Vol. 87, No. 11, pp. 3305-3321, November 2010.