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 proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 1st IFAC Workshop on Control Methods for Water Resource Systems
PublisherIFAC-PapersOnLine
Pages37-42
Number of pages6
DOIs
Publication statusPublished - 2019
Event1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19) - Delft, Netherlands
Duration: 19-Sep-201920-Sep-2019

Publication series

NameIFAC-PapersOnLine
Number23
Volume52
ISSN (Print)2405-8963

Conference

Conference1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS19)
CountryNetherlands
CityDelft
Period19/09/201920/09/2019

Cite this