Víctor González-Alvarez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Víctor González-Alvarez has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Victor Alcaraz-Gonzalez
Departamento de Ingeniería Química, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara 44430, Jalisco, Mexico
2 shared publications - Fabián Azael Fregoso-Sánchez
Universidad de Guadalajara—CUCEI, M. García Barragán 1421, Guadalajara 44430, Mexico
1 shared publication - Hugo Oscar Mendez Acosta
Universidad de Guadalajara
1 shared publication - Jean-Philippe Steyer
INRAE, Univ Montpellier, LBE, Narbonne, France
1 shared publication - R. Salazar-Peña
Chemical Engineering Department, Universidad de Guadalajara, CUCEI , Calz. Marcelino García Barragán 1421, 44430, Guadalajara, Jalisco, Mexico,
1 shared publication - Raúl Snell-Castro
Universidad de Guadalajara Departamento de Ingeniería Química, CUCEI, . Blvd. Marcelino García Barragán, 1421. Colonia Olímpica, C.P. 44430, Guadalajara, Jalisco
1 shared publication
Latest Publications
- Open Access
Multivariable Robust Regulation of Alkalinities in Continuous Anaerobic Digestion Processes: Experimental Validation
A multivariable adaptive feedback control for highly uncertain continuous anaerobic digestion processes is proposed to regulate the volatile fatty acids (VFA) concentration, the strong ions concentrations, and the total and intermediate alkalinities. The multivariable control scheme includes a Luenberger observer to estimate both the unmeasured variables (i.e., VFA) and unknown microbial growth kinetics. The control approach is designed using an exponential Lyapunov function to resemble the typical exponential biological growth of the involved microbial consortia. Taking into account physicochemical equilibrium, alkalinities are represented as a function of the state variables. As a result, the control problem becomes a regulation problem on alkalinities, and in turn, a tracking control problem on the state variables, with two manipulated variables—the dilution rate and the feed rate of a strong alkali solution—while the state variables’ set-points are given as a function of pH. The implementation of this multivariable control scheme was experimentally tested and validated in a 0.982 m3 pilot plant treating agro-industrial wastewater and demonstrated to be robust in the face of unknown microbial growth kinetics. Results showed the potential for practical application and optimization of industrial digesters.
Open AccessMultivariable Robust Regulation of Alkalinities in Continuous Anaerobic Digestion Processes: Experimental Validation
A multivariable adaptive feedback control for highly uncertain continuous anaerobic digestion processes is proposed to regulate the volatile fatty acids (VFA) concentration, the strong ions concentrations, and the total and intermediate alkalinities. The multivariable control scheme includes a Luenberger observer to estimate both the unmeasured variables (i.e., VFA) and unknown microbial growth kinetics. The control approach is designed using an exponential Lyapunov function to resemble the typical exponential biological growth of the involved microbial consortia. Taking into account physicochemical equilibrium, alkalinities are represented as a function of the state variables. As a result, the control problem becomes a regulation problem on alkalinities, and in turn, a tracking control problem on the state variables, with two manipulated variables—the dilution rate and the feed rate of a strong alkali solution—while the state variables’ set-points are given as a function of pH. The implementation of this multivariable control scheme was experimentally tested and validated in a 0.982 m3 pilot plant treating agro-industrial wastewater and demonstrated to be robust in the face of unknown microbial growth kinetics. Results showed the potential for practical application and optimization of industrial digesters.
- 22 October 2013
- Published by Springer Nature in Bioprocess and Biosystems Engineering
- Vol. 37 (6) , 1031-1042
Neural network modeling of the light profile in a novel photobioreactor
An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.
- 22 October 2013
- Published by Springer Nature in Bioprocess and Biosystems Engineering
- Vol. 37 (6) , 1031-1042
Neural network modeling of the light profile in a novel photobioreactor
An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.