A Machine Learning Approach for Anaerobic Reactor Performance Prediction Using Long Short-Term Memory Recurrent Neural Network
Benjamin Steven Vien, Leslie Wong, Thomas Kuen, L. R. Francis Rose, Wing Kong Chiudownload PDF
Abstract. Predictive models are important to help manage high-value assets and to ensure optimal and safe operations. Recently, advanced machine learning algorithms have been applied to solve practical and complex problems, and are of significant interest due to their ability to adaptively ‘learn’ in response to changing environments. This paper reports on the data preparation strategies and the development and predictive capability of a Long Short-Term Memory recurrent neural network model for anaerobic reactors employed at Melbourne Water’s Western Treatment Plant for sewage treatment that includes biogas harvesting. The results show rapid training and higher accuracy in predicting biogas production when historical data, which include significant outliers, are preprocessed with z-score standardisation in comparison to those with max-min normalisation. Furthermore, a trained model with a reduced number of input variables via the feature selection technique based on Pearson’s correlation coefficient is found to yield good performance given sufficient dataset training. It is shown that the overall best performance model comprises the reduced input variables and data processed with z-score standardisation. This initial study provides a useful guide for the implementation of machine learning techniques to develop smarter structures and management towards Industry 4.0 concepts.
Machine Learning, Data Analysis, Anaerobic Reactor, Data Preparation, Long Short-Term Memory, Artificial Neural Network
Published online 2/20/2021, 10 pages
Copyright © 2021 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Benjamin Steven Vien, Leslie Wong, Thomas Kuen, L. R. Francis Rose, Wing Kong Chiu, A Machine Learning Approach for Anaerobic Reactor Performance Prediction Using Long Short-Term Memory Recurrent Neural Network, Materials Research Proceedings, Vol. 18, pp 61-70, 2021
The article was published as article 8 of the book Structural Health Monitoring
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 Melbourne Water. Western Treatment Plant Virtual Tour. 2020 30 September 2020 [cited 2020 30 November]; Available from: https://www.melbournewater.com.au/water-data-and-education/learning-resources/water-and-sewage-treatment-plants/western-treatment-0.
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