Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant

Offie, Ikechukwu, Piadeh, Farzad ORCID:, Behzadian, Kourosh ORCID:, Campos, Luiza C. and Yaman, Rokiah (2023) Development of an artificial intelligence-based framework for biogas generation from a micro anaerobic digestion plant. Waste Management, 158. pp. 66-75. ISSN 0956-053X

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Offie_et_al._2023_j.wasman._Development_of_an_artificial_intelligence-based_framework_for_biogas_generation_from_a_micro_anaerobic_digestion_plant.pdf - Accepted Version
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Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m3/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner.

Item Type: Article
Identifier: 10.1016/j.wasman.2022.12.034
Additional Information: 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 Micro AD and Dr Davide Poggio from the University of Sheffield for their great support to provide and analyse the data collected from the case study. The authors also wish to thank the editor and the three anonymous reviewers for making constructive comments which substantially improved the quality of the paper.
Keywords: Anaerobic digestion, Artificial intelligence framework, Biogas generation, Optimised operation strategy, Organic waste, Recurrent neural network
Subjects: Construction and engineering
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Depositing User: Kourosh Behzadian
Date Deposited: 20 Jan 2023 10:51
Last Modified: 06 Feb 2024 16:13


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