지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.4
- 수록면
- 147 - 156 (10page)
이용수
초록· 키워드
오류제보하기
Estimating the liquid level in an anaerobic digester can be disturbed by its closedness, bubbles and scum formation, and the inhomogeneity of the digestate. In our previous study, a soft-sensor approach using seven pressure meters has been proposed as an alternative for real-time liquid level estimation. Here, machine learning techniques were used to improve the estimation accuracy and optimize the number of sensors required in this approach. Four algorithms, multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) with radial basis function kernel were compared for this purpose. All models outperformed the cubic model developed in the previous study, among which the ANN and RF models performed the best. Variable importance analysis suggested that the pressure readings from the top (in the headspace) were the most significant, while the other pressure meters showed varying significance levels depending on the model type. The sensor that experienced both headspace and liquid phases depending on the level variation incurred a higher error than other sensors. The results showed that the ML techniques can provide an effective tool to estimate digester liquid levels by optimizing the number of sensors and reducing the error rate.
목차
ABSTRACT
1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
References
참고문헌 (33)
참고문헌 신청
Shin J, Cho S-K, Lee J, et al. Performance and microbial community dynamics in anaerobic digestion of waste activated sludge: Impact of immigration. Energies 2019;12. https://doi.org/10.3390/en12030573.
Santhosh KV, Joy B, Rao S. Design of an instrument for liquid level measurement and concentration analysis using multisensor data fusion. J. Sens. 2020;2020:1-13. https://doi.org/10.1155/2020/4259509.
Corona F, Mulas M, Haimi H, Sundell L, Heinonen M, Vahala R. Monitoring nitrate concentrations in the denitrifying post-filtration unit of a municipal wastewater treatment plant. J. Process Control 2013;23:158-170. https://doi.org/10.1016/j.jprocont.2012.
Jimenez J, Latrille E, Harmand J, et al. Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Rev. Environ. Sci. Biotechnol. 2015;14:615-648. https://doi.org/10.1007/s11157-015-9382-6.
Kawai M, Nagao N, Kawasaki N, Imai A, Toda T. Improvement of COD removal by controlling the substrate degradability during the anaerobic digestion of recalcitrant wastewater. J. Environ. Manage. 2016;181:838-846. https://doi.org/10.1016/j.jenvman.2016.06.
Santhosh KV, Joy B, Rao S. Design of an instrument for liquid level measurement and concentration analysis using multisensor data fusion. J. Sens. 2020;2020:1-13. https://doi.org/10.1155/2020/4259509.
Corona F, Mulas M, Haimi H, Sundell L, Heinonen M, Vahala R. Monitoring nitrate concentrations in the denitrifying post-filtration unit of a municipal wastewater treatment plant. J. Process Control 2013;23:158-170. https://doi.org/10.1016/j.jprocont.2012.
Jimenez J, Latrille E, Harmand J, et al. Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Rev. Environ. Sci. Biotechnol. 2015;14:615-648. https://doi.org/10.1007/s11157-015-9382-6.
Kawai M, Nagao N, Kawasaki N, Imai A, Toda T. Improvement of COD removal by controlling the substrate degradability during the anaerobic digestion of recalcitrant wastewater. J. Environ. Manage. 2016;181:838-846. https://doi.org/10.1016/j.jenvman.2016.06.
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UCI(KEPA) : I410-ECN-0101-2022-539-001344854