Author, as appears in the article.: Kazemi P; Steyer JP; Bengoa C; Font J; Giralt J
Department: Enginyeria Química
URV's Author/s: Bengoa, Christophe José / Font Capafons, José / Giralt Marcé, Jaume / KAZEMI, PEZHMAN
Keywords: Waste-water treatment Unit Soft sensor Prediction Performance Optimization Operation Neural-network Neural network Genetic programming Data driven Benchmark simulation-model Anaerobic digestion
Abstract: © 2019 by the authors. The concentration of volatile fatty acids (VFAs) is one of the most important measurements for evaluating the performance of anaerobic digestion (AD) processes. In real-time applications, VFAs can be measured by dedicated sensors, which are still currently expensive and very sensitive to harsh environmental conditions. Moreover, sensors usually have a delay that is undesirable for real-time monitoring. Due to these problems, data-driven soft sensors are very attractive alternatives. This study proposes different data-driven methods for estimating reliable VFA values. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained fromthe international water association (IWA) Benchmark Simulation Model No. 2 (BSM2). The organic load to the AD in BSM2 was modified to simulate the behavior of an anaerobic co-digestion process. The prediction and generalization performances of the different models were also compared. This comparison showed that the GP soft sensor is more precise than the other soft sensors. In addition, the model robustness was assessed to determine the performance of each model under different process states. It is also shown that, in addition to their robustness, GP soft sensors are easy to implement and provide useful insights into the process by providing explicit equations.
Thematic Areas: Process chemistry and technology Engineering, chemical Engenharias ii Ciências biológicas ii Chemical engineering (miscellaneous) Bioengineering
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 22279717
Author's mail: jaume.giralt@urv.cat jose.font@urv.cat christophe.bengoa@urv.cat
Author identifier: 0000-0001-5917-8741 0000-0002-4007-7905 0000-0001-9160-5010
Record's date: 2023-02-22
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2227-9717/8/1/67
Papper original source: Processes. 8 (1):
APA: Kazemi P; Steyer JP; Bengoa C; Font J; Giralt J (2020). Robust data-driven soft sensors for online monitoring of volatile fatty acids in anaerobic digestion processes. Processes, 8(1), -. DOI: 10.3390/pr8010067
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.3390/pr8010067
Entity: Universitat Rovira i Virgili
Journal publication year: 2020
Publication Type: Journal Publications