Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression

M. A. Prawira Negara; E. Cornelissen; A. K. Geurkink; G. J. W. Euverink; B. Jayawardhana

doi:10.1016/j.ifacol.2019.11.006

Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression

M. A. Prawira Negara, E. Cornelissen, A. K. Geurkink, G. J. W. Euverink, B. Jayawardhana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

129 Downloads (Pure)

Abstract

In this paper, we analyze next generation sequencing (NGS) data of wastewater treatment plant (WWTP) in the North Water facility for revealing the role of 1236 different genera of microorganisms in the aeration basin to the measured process data. Both the time-series data of NGS and process parameters are pre-processed and analyzed using support vector regression technique and is compared with the deep neural network approach. Local sensitivity analysis is performed on the resulting models. Both machine learning analyses show the importance of a subset of genera to the WWTP process and can be used to enrich / to adapt the well-studied activated sludge model (ASM).

Original language	English
Title of host publication	Proceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems
Publisher	IFAC-PapersOnLine
Pages	37-42
Number of pages	6
DOIs	https://doi.org/10.1016/j.ifacol.2019.11.006
Publication status	Published - 2019
Event	1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19) - Delft, Netherlands Duration: 19-Sept-2019 → 20-Sept-2019

Publication series

Name	IFAC-PapersOnLine
Number	23
Volume	52
ISSN (Print)	2405-8963

Conference

Conference	1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19)
Country/Territory	Netherlands
City	Delft
Period	19/09/2019 → 20/09/2019

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10.1016/j.ifacol.2019.11.006

Next Generation Sequencing Analysis of Wastewater Treatment Plant ProcessFinal publisher's version, 1.73 MBLicence: Taverne

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Prawira Negara, M. A., Cornelissen, E., Geurkink, A. K., Euverink, G. J. W., & Jayawardhana, B. (2019). Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression. In Proceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems (pp. 37-42). (IFAC-PapersOnLine; Vol. 52, No. 23). IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2019.11.006

@inproceedings{ff7e45179acb4c9ba1203dc41d2f0b58,

title = "Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression",

abstract = "In this paper, we analyze next generation sequencing (NGS) data of wastewater treatment plant (WWTP) in the North Water facility for revealing the role of 1236 different genera of microorganisms in the aeration basin to the measured process data. Both the time-series data of NGS and process parameters are pre-processed and analyzed using support vector regression technique and is compared with the deep neural network approach. Local sensitivity analysis is performed on the resulting models. Both machine learning analyses show the importance of a subset of genera to the WWTP process and can be used to enrich / to adapt the well-studied activated sludge model (ASM). ",

author = "{Prawira Negara}, {M. A.} and E. Cornelissen and Geurkink, {A. K.} and Euverink, {G. J. W.} and B. Jayawardhana",

year = "2019",

doi = "10.1016/j.ifacol.2019.11.006",

language = "English",

series = "IFAC-PapersOnLine",

publisher = "IFAC-PapersOnLine",

number = "23",

pages = "37--42",

booktitle = "Proceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems",

note = "1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19) ; Conference date: 19-09-2019 Through 20-09-2019",

}

Prawira Negara, MA, Cornelissen, E, Geurkink, AK, Euverink, GJW & Jayawardhana, B 2019, Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression. in Proceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems. IFAC-PapersOnLine, no. 23, vol. 52, IFAC-PapersOnLine, pp. 37-42, 1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19), Delft, Netherlands, 19/09/2019. https://doi.org/10.1016/j.ifacol.2019.11.006

Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression. / Prawira Negara, M. A.; Cornelissen, E.; Geurkink, A. K. et al.
Proceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems. IFAC-PapersOnLine, 2019. p. 37-42 (IFAC-PapersOnLine; Vol. 52, No. 23).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression

AU - Prawira Negara, M. A.

AU - Cornelissen, E.

AU - Geurkink, A. K.

AU - Euverink, G. J. W.

AU - Jayawardhana, B.

PY - 2019

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AB - In this paper, we analyze next generation sequencing (NGS) data of wastewater treatment plant (WWTP) in the North Water facility for revealing the role of 1236 different genera of microorganisms in the aeration basin to the measured process data. Both the time-series data of NGS and process parameters are pre-processed and analyzed using support vector regression technique and is compared with the deep neural network approach. Local sensitivity analysis is performed on the resulting models. Both machine learning analyses show the importance of a subset of genera to the WWTP process and can be used to enrich / to adapt the well-studied activated sludge model (ASM).

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DO - 10.1016/j.ifacol.2019.11.006

M3 - Conference contribution

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BT - Proceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems

PB - IFAC-PapersOnLine

T2 - 1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19)

Y2 - 19 September 2019 through 20 September 2019

ER -

Next Generation Sequencing Analysis of Wastewater Treatment Plant Process Via Support Vector Regression

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