Autor según el artículo: Kazemi P; Steyer JP; Bengoa C; Font J; Giralt J

Departamento: Enginyeria Química

Autor/es de la URV: Bengoa, Christophe José / Font Capafons, José / Giralt Marcé, Jaume / KAZEMI, PEZHMAN

Palabras clave: Waste-water treatment Unit Soft sensor Prediction Performance Optimization Operation Neural-network Neural network Genetic programming Data driven Benchmark simulation-model Anaerobic digestion

Resumen: © 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.

Áreas temáticas: Process chemistry and technology Engineering, chemical Engenharias ii Ciências biológicas ii Chemical engineering (miscellaneous) Bioengineering

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 22279717

Direcció de correo del autor: jaume.giralt@urv.cat jose.font@urv.cat christophe.bengoa@urv.cat

Identificador del autor: 0000-0001-5917-8741 0000-0002-4007-7905 0000-0001-9160-5010

Fecha de alta del registro: 2023-02-22

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.mdpi.com/2227-9717/8/1/67

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Processes. 8 (1):

Referencia de l'ítem segons les normes 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

DOI del artículo: 10.3390/pr8010067

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications