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Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess

Fisher, Oliver; Watson, Nicholas J.; Porcu, Laura; Bacon, Darren; Rigley, Martin; Gomes, Rachel L.

Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess Thumbnail


Authors

OLIVER FISHER OLIVER.FISHER2@NOTTINGHAM.AC.UK
Assistant Professor in Chemical and Environmental Engineering

Nicholas J. Watson

Laura Porcu

Darren Bacon

Martin Rigley

RACHEL GOMES rachel.gomes@nottingham.ac.uk
Professor of Water & Resource Processing



Abstract

Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing models that captures the underlying biological nature of the bioprocesses challenging. In this study, a framework for developing data-driven models of bioprocesses is proposed and evaluated by modelling an industrial bioprocess, which treats industrial or agrifood wastewaters whilst simultaneously generating bioenergy. Six models were developed to predict the reduction in chemical oxygen demand from the wastewater by the bioprocess and statistically evaluated using both testing data (randomly partitioned data from the model development) and unseen data (new data not used during the model development). The statistical error metrics employed were the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The stacked neural network model was best able to model the bioprocess, having the highest accuracy on the testing data (R2: 0.98; RMSE: 1.29; MAE: 2.27; MAPE: 4.08) and the unseen data (R2: 0.82; RMSE: 2.57; MAE: 1.75; MAPE: 3.68). Data visualisation is used to observe (or confirm) whether new data points are within the model boundaries, helping to increase confidence in the model’s predictions on future data.

Citation

Fisher, O., Watson, N. J., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. (2022). Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess. Biochemical Engineering Journal, 185, Article 108499. https://doi.org/10.1016/j.bej.2022.108499

Journal Article Type Article
Acceptance Date May 27, 2022
Online Publication Date May 30, 2022
Publication Date 2022-07
Deposit Date Jun 30, 2022
Publicly Available Date Jul 7, 2022
Journal Biochemical Engineering Journal
Print ISSN 1369-703X
Electronic ISSN 1873-295X
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 185
Article Number 108499
DOI https://doi.org/10.1016/j.bej.2022.108499
Keywords Biomedical Engineering; Environmental Engineering; Bioengineering; Biotechnology
Public URL https://nottingham-repository.worktribe.com/output/8226160
Publisher URL https://www.sciencedirect.com/science/article/pii/S1369703X22001681

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