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 Chiu

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationStructural Health Monitoring - 8th Asia Pacific Workshop on Structural Health Monitoring, 8APWSHM 2020, proceedings
EditorsN. Rajic, M. Veidt, A. Mita, N. Takeda, W.K. Chiu
PublisherAssociation of American Publishers
Number of pages10
ISBN (Print)9781644901304
Publication statusPublished - 2021
EventAsia-Pacific Workshop on Structural Health Monitoring 2020 - Cairns, Australia
Duration: 9 Dec 202011 Dec 2020
Conference number: 8th
https://www.monash.edu/engineering/shm (Website)

Publication series

NameMaterials Research Proceedings
ISSN (Print)2474-3941
ISSN (Electronic)2474-395X


ConferenceAsia-Pacific Workshop on Structural Health Monitoring 2020
Abbreviated titleAPWSHM 2020
Internet address


  • Anaerobic Reactor
  • Artificial Neural Network
  • Data Analysis
  • Data Preparation
  • Long Short-Term Memory
  • Machine Learning

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