Real-time monitoring of decentralised Anaerobic Digestion using Artificial Intelligence-based framework

Offie, Ikechukwu, Piadeh, Farzad ORCID:, Behzadian, Kourosh ORCID:, Alani, Amir, Yaman, Rokiah and Campus, Luiza (2022) Real-time monitoring of decentralised Anaerobic Digestion using Artificial Intelligence-based framework. In: 2022 International Conference on Resource Sustainability (icRS 2022), 1-4 Aug 2022, Virtual. (Unpublished)

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This paper presents an Artificial Intelligence (AI)-based framework for real-time monitoring and improving the operation of an Anaerobic Digestion (AD) system in producing biogas. This was achieved using historic data obtained from a decentralised AD plant located in Camley-Central London to develop a recurrent neural network (RNN) model based on AI to predict biogas production with respect to lag time. The dataset obtained from the AD plant had a wide range of missing values, which hindered the accurate prediction of biogas. This study evaluates different data mining techniques for infilling missing data. The Recurrent Neural Network (RNN) Model was then developed for predicting biogas with respect to various lag times. The results show both Kriging and Linear Regression techniques have the best performance, and they were used to infill the missing data. The results also show biogas production can be accurately predicted in real-time operation using a NARX model based on the feed data including organic food composition such as oats, soaked liners, catering and water added.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Acknowledgement This work is supported by the Knowledge Exchange (KE) Seed Fund allocated to the fifth author (industrial partner) and the Fellowship allocated to the third author. The authors wish to acknowledge the KE seed fund supported by the University of West London and the Fellowship supported by the Royal Academy of Engineering under the Leverhulme Trust Research Fellowships scheme. The authors also wish to thank the Diego Vega from LEAP Ltd and Dr Davide Poggio from The University of Sheffield for their great support to provide and analyse the data collected from the case study
Keywords: Anaerobic Digestion, Biogas prediction, Neural Network Based State Estimation, Organic Waste, Recurrent Neural Network, Root Mean Square Error.
Subjects: Construction and engineering > Civil and environmental engineering
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Depositing User: Kourosh Behzadian
Date Deposited: 01 Sep 2022 21:17
Last Modified: 02 Aug 2023 10:37


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