4.8 Article

Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors

Journal

WATER RESEARCH
Volume 189, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2020.116657

Keywords

Machine Learning; Artificial neural networks; Adaptive Neuro-Fuzzy Inference Systems; Support Vector Regression; Aerobic granular sludge; Sequencing Batch Reactors

Funding

  1. National Science and Engineering Research Council of Canada

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Machine learning models were developed to simulate the AGS process using data collected from lab-based reactors for 475 days. Inputs were selected and model structure was optimized to successfully predict key parameters for treatment reactors.
Machine learning models provide an adaptive tool to predict the performance of treatment reactors under varying operational and influent conditions. Aerobic granular sludge (AGS) is still an emerging technology and does not have a long history of full-scale application. There is, therefore, a scarcity of long-term data in this field, which impacted the development of data-driven models. In this study, a machine learning model was developed for simulating the AGS process using 475 days of data collected from three lab-based reactors. Inputs were selected based on RReliefF ranking after multicollinearity reduction. A five-stage model structure was adopted in which each parameter was predicted using separate models for the preceding parameters as inputs. An ensemble of artificial neural networks, support vector regression and adaptive neuro-fuzzy inference systems was used to improve the models' performance. The developed model was able to predict the MLSS, MLVSS, SVI5, SVI30, granule size, and effluent COD, NH4-N, and PO43- with average R-2, nRMSE and sMAPE of 95.7%, 0.032 and 3.7% respectively. (C) 2020 Elsevier Ltd. All rights reserved.

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