{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T02:43:15Z","timestamp":1785984195087,"version":"3.56.0"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030304928","type":"print"},{"value":"9783030304935","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Anaerobic digestion is a natural process that transforms organic substrates to methane and other products. Under controlled conditions the process has been widely applied to manage organic wastes. Improvements in process control are expected to lead to improvements in the technical and economic efficiency of the process. This paper presents and compares 3 different neural network model architectures for use as anaerobic digestion process predictive models. The models predict the future biogas production trend from measured physical and chemical parameters. The first model features an LSTM layer, the second model features a 1-D convolutional layer and the third model combines 2 separate inputs and parallel treatment using LSTM and 1-D convolutional layers followed by merging to produce a single prediction. The predictions can be used to adaptively adjust the substrate feeding rate in accordance with the transient state of the digestion process as defined by liquid feeding rate, the organic acid and ammonium ion concentrations and the pH of the digester liquid phase. The training and testing data were obtained during 1 year of continuous operation of a pilot-plant treating restaurant wastes. PLS regression and ICA were used to select the most relevant process parameters from the data. The 1-D Convolutional based model comprising 272 trainable parameters predicted the future biogas flow rate changes with accuracy as high as 89% and an average accuracy of 58% . The work-flow can be applied to optimize the control of the study digester and to control bioreactors in general.<\/jats:p>","DOI":"10.1007\/978-3-030-30493-5_65","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"725-736","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["LSTM and 1-D Convolutional Neural Networks for Predictive Monitoring of the Anaerobic Digestion Process"],"prefix":"10.1007","author":[{"given":"Mark","family":"McCormick","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro E. P.","family":"Villa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"65_CR1","unstructured":"Stenmarck, \u00c5., et al.: FUSIONS: estimates of European food waste levels, Stockholm (2016). ISBN 978-91-88319-01-2"},{"key":"65_CR2","unstructured":"Capaccioli, S., et al.: Socio-economic benefits of small anaerobic digestion systems. In: Proceeding of the 24th European Biomass Conference and Exhibition, pp. 1530\u20131534, Amsterdam (2016)"},{"issue":"6","key":"65_CR3","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s12649-016-9758-z","volume":"9","author":"WRM Leite","year":"2016","unstructured":"Leite, W.R.M., Belli Filho, P., Gottardo, M., Pavan, P., Bolzonella, D.: Monitoring and control improvement of single and two stage thermophilic sludge digestion through multivariate analysis. Waste Biomass Valorization 9(6), 985\u2013994 (2016)","journal-title":"Waste Biomass Valorization"},{"issue":"21","key":"65_CR4","doi-asserted-by":"publisher","first-page":"21149","DOI":"10.1007\/s11356-018-2224-7","volume":"25","author":"L Alejo","year":"2018","unstructured":"Alejo, L., Atkinson, J., Guzm\u00e1n-Fierro, V., Roeckel, M.: Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res. 25(21), 21149\u201321163 (2018)","journal-title":"Environ. Sci. Pollut. Res."},{"issue":"5","key":"65_CR5","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.2166\/wst.2012.286","volume":"66","author":"D Gaida","year":"2012","unstructured":"Gaida, D., et al.: State estimation for anaerobic digesters using the ADM1. Water Sci. Technol. 66(5), 1088\u20131095 (2012)","journal-title":"Water Sci. Technol."},{"key":"65_CR6","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv: e1803.01271 (2018)"},{"key":"65_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.ast.2018.02.026","volume":"77","author":"Y Dong","year":"2018","unstructured":"Dong, Y.: An application of deep neural networks to the in-flight parameter identification for detection and characterization of aircraft icing. Aerosp. Sci. Technol. 77, 34\u201349 (2018)","journal-title":"Aerosp. Sci. Technol."},{"issue":"Oct","key":"65_CR8","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:54:51Z","timestamp":1710348891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_65","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}