A Machine Learning Approach for Anaerobic Reactor Performance Prediction Using Long Short-Term Memory Recurrent Neural Network Article Swipe
Thomas Kuen
,
L.R.F. Rose
,
Weilong Kong
,
Benjamin Steven Vien
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21741/9781644901311-8
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21741/9781644901311-8
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21741/9781644901311-8
- https://www.mrforum.com/wp-content/uploads/open_access/9781644901311/8.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 21
- Related Works
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W3157179110Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21741/9781644901311-8Digital Object Identifier
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A Machine Learning Approach for Anaerobic Reactor Performance Prediction Using Long Short-Term Memory Recurrent Neural NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
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2021-02-16Full publication date if available
- Authors
-
Thomas Kuen, L.R.F. Rose, Weilong Kong, Benjamin Steven VienList of authors in order
- Landing page
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https://doi.org/10.21741/9781644901311-8Publisher landing page
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https://www.mrforum.com/wp-content/uploads/open_access/9781644901311/8.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.mrforum.com/wp-content/uploads/open_access/9781644901311/8.pdfDirect OA link when available
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Artificial neural network, Computer science, Machine learning, Artificial intelligence, Outlier, Recurrent neural network, Feature selection, Predictive modelling, Feature (linguistics), Term (time), Philosophy, Physics, Linguistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 2, 2023: 5, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper | 49 |
| abstract_inverted_index.rapid | 91 |
| abstract_inverted_index.shown | 152 |
| abstract_inverted_index.solve | 26 |
| abstract_inverted_index.study | 172 |
| abstract_inverted_index.their | 38 |
| abstract_inverted_index.those | 115 |
| abstract_inverted_index.which | 103 |
| abstract_inverted_index.yield | 143 |
| abstract_inverted_index.Memory | 66 |
| abstract_inverted_index.assets | 9 |
| abstract_inverted_index.biogas | 86, 98 |
| abstract_inverted_index.ensure | 12 |
| abstract_inverted_index.higher | 94 |
| abstract_inverted_index.manage | 7 |
| abstract_inverted_index.models | 2 |
| abstract_inverted_index.neural | 68 |
| abstract_inverted_index.number | 126 |
| abstract_inverted_index.sewage | 82 |
| abstract_inverted_index.useful | 175 |
| abstract_inverted_index.Western | 78 |
| abstract_inverted_index.ability | 39 |
| abstract_inverted_index.applied | 24 |
| abstract_inverted_index.complex | 29 |
| abstract_inverted_index.dataset | 148 |
| abstract_inverted_index.develop | 185 |
| abstract_inverted_index.feature | 132 |
| abstract_inverted_index.include | 104 |
| abstract_inverted_index.initial | 171 |
| abstract_inverted_index.machine | 19, 181 |
| abstract_inverted_index.max-min | 117 |
| abstract_inverted_index.network | 69 |
| abstract_inverted_index.optimal | 13 |
| abstract_inverted_index.overall | 155 |
| abstract_inverted_index.reduced | 125, 161 |
| abstract_inverted_index.reports | 50 |
| abstract_inverted_index.results | 89 |
| abstract_inverted_index.smarter | 186 |
| abstract_inverted_index.towards | 190 |
| abstract_inverted_index.trained | 121 |
| abstract_inverted_index.z-score | 110, 168 |
| abstract_inverted_index.Industry | 191 |
| abstract_inverted_index.accuracy | 95 |
| abstract_inverted_index.advanced | 18 |
| abstract_inverted_index.changing | 46 |
| abstract_inverted_index.employed | 74 |
| abstract_inverted_index.includes | 85 |
| abstract_inverted_index.interest | 35 |
| abstract_inverted_index.learning | 20, 182 |
| abstract_inverted_index.provides | 173 |
| abstract_inverted_index.reactors | 73 |
| abstract_inverted_index.response | 44 |
| abstract_inverted_index.training | 92 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Melbourne | 76 |
| abstract_inverted_index.Recently, | 17 |
| abstract_inverted_index.Treatment | 79 |
| abstract_inverted_index.Water’s | 77 |
| abstract_inverted_index.anaerobic | 72 |
| abstract_inverted_index.comprises | 159 |
| abstract_inverted_index.concepts. | 193 |
| abstract_inverted_index.important | 4 |
| abstract_inverted_index.outliers, | 106 |
| abstract_inverted_index.practical | 27 |
| abstract_inverted_index.problems, | 30 |
| abstract_inverted_index.processed | 166 |
| abstract_inverted_index.recurrent | 67 |
| abstract_inverted_index.selection | 133 |
| abstract_inverted_index.technique | 134 |
| abstract_inverted_index.training. | 149 |
| abstract_inverted_index.treatment | 83 |
| abstract_inverted_index.variables | 129, 163 |
| abstract_inverted_index.Predictive | 1 |
| abstract_inverted_index.Short-Term | 65 |
| abstract_inverted_index.adaptively | 41 |
| abstract_inverted_index.algorithms | 21 |
| abstract_inverted_index.capability | 61 |
| abstract_inverted_index.comparison | 113 |
| abstract_inverted_index.high-value | 8 |
| abstract_inverted_index.historical | 101 |
| abstract_inverted_index.management | 189 |
| abstract_inverted_index.predicting | 97 |
| abstract_inverted_index.predictive | 60 |
| abstract_inverted_index.production | 99 |
| abstract_inverted_index.strategies | 55 |
| abstract_inverted_index.structures | 187 |
| abstract_inverted_index.sufficient | 147 |
| abstract_inverted_index.techniques | 183 |
| abstract_inverted_index.Pearson’s | 137 |
| abstract_inverted_index.coefficient | 139 |
| abstract_inverted_index.correlation | 138 |
| abstract_inverted_index.development | 58 |
| abstract_inverted_index.harvesting. | 87 |
| abstract_inverted_index.operations. | 16 |
| abstract_inverted_index.performance | 145, 157 |
| abstract_inverted_index.preparation | 54 |
| abstract_inverted_index.significant | 34, 105 |
| abstract_inverted_index.‘learn’ | 42 |
| abstract_inverted_index.Furthermore, | 119 |
| abstract_inverted_index.preprocessed | 108 |
| abstract_inverted_index.environments. | 47 |
| abstract_inverted_index.implementation | 179 |
| abstract_inverted_index.normalisation. | 118 |
| abstract_inverted_index.standardisation | 111 |
| abstract_inverted_index.standardisation. | 169 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.69672886 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |