연구진흥

창의적인 신지식 창출과 산업계와의 협력적 네트워크 구축

금주의 우수논문

SCI-E Article
Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process
김문일
  1. 성명
    김문일
  2. 소속
    공학대학 건설환경공학과
  3. 캠퍼스
  4. 우수선정주
    2023년 11월 3째주
Author
김문일 (Dept Civil & Environm Engn) corresponding author; Cui, Fenghao (Ctr Creat Convergence Educ);
Corresponding Author Info
Cui, F(해당 저자), Hanyang Univ, Ctr Creat Convergence Educ, 55 Hanyangdaehak Ro, Ansan 426791, Kyeonggido, South Korea.
E-mail
이메일moonilkim@hanyang.ac.kr
Document Type
Article
Source
JOURNAL OF ENVIRONMENTAL MANAGEMENT Volume:347 Issue: Pages:- Published:2023
Times Cited
0
External Information
http://dx.doi.org/10.1016/j.jenvman.2023.119153
Abstract
When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.
Web of Science Categories
Environmental Sciences
Funding
National Research Foundation of Korea (NRF) - Korean government [2020R1F1A1063562]
Language
English
attached file
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