Sistema de blogs Diarium
Universidad de Salamanca
Supervision and Process Control Group
Computing and Automation Department. University of Salamanca, Spain
 
Diapositiva7

After 2010

  • Aponte-Rengifo, O., Francisco, M., Vilanova, R., Vega, P., Revollar, S. Intelligent control for wastewater treatment plants based on model-free deep reinforcement learning. Processes, 11(8), 2269. JCR 2019 Q2 (2023).
  • Cembellín, A., Francisco, M., Vega, P. Optimal operation of a benchmark simulation model for sewer networks using a qualitative distributed model predictive control algorithm. Processes, 11(8), 1528. JCR 2019 Q2 (2023).
  • Aponte-Rengifo, O., Vega, P., Francisco, M. Deep reinforcement learning for negotiation in multi-agent cooperative distributed control. Applied Sciences, 13(4), 2432. JCR 2021 Q2 (39/92) (2023).
  • Vallejo P. M., Vega, P. Integración de la estrategia FMBPC en una estructura de control predictivo en lazo cerrado. Aplicación al control de fangos activados. Revista Iberoamericana de Automática e Informática industrial, 19(1), 13-26 (2022).
  • Sánchez, A., Castellano, E., Martín, M., Vega, P. Evaluating ammonia as green fuel for power generation: A thermo-chemical perspective. Applied Energy, 293, 116956. JCR 2020 Q1 (6/143) (2021).
  • Vallejo Llamas, P. M., Vega, P. Practical Computational Approach for the Stability Analysis of Fuzzy Model-Based Predictive Control of Substrate and Biomass in Activated Sludge Processes. Processes, 9(3), 531. JCR 2019 Q2 (2021).
  • Masero, E., Francisco, M., Maestre, J.M., Revollar, S., Vega, P. Robust coalitional model predictive control with predicted topology transitions. IEEE Transactions on Control of Network Systems, 8(4), 1869-1880. JCR 2019 Q2 (24/63) (2021).
  • Masero, E., Francisco, M., Maestre, J.M., Revollar, S., Vega, P. Hierarchical distributed model predictive control based on fuzzy negotiation. Expert Systems with applications, 176, 114836. JCR 2019 Q1 (2/83) (2021).
  • Revollar, S., Meneses, M., Vilanova, R., Vega, P., Francisco, M. Eco-Efficiency assessment of control actions in wastewater treatment plants. Water, 13, 612. JCR 2019 Q2 (31/94) (2021)
  • Cembellín, A., Francisco, M., Vega, P. Distributed model predictive control of a sewer system. Processes, 8 (12), 1595. JCR 2018 Q2 (69/138) (2020)
  • Morales-Rodelo, K., Francisco, M., Álvarez, H., Vega, P., Revollar, S. Collaborative control applied to BSM1 for wastewater treatment plants. Processes, 8 (11), 1465. JCR 2018 Q2 (69/138) (2020)
  • Revollar, S., Meneses, M., Vilanova, R., Vega, P., Francisco, M. Quantifying the benefit of a Dynamic Performance Assessment of WWTP. Processes, 8, 206. JCR 2018 Q2 (105/251) (2020)
  • Revollar, S., Vilanova, R., Vega, P., Francisco, M., Meneses, M. Wastewater treatment plant operation: Simple control schemes with a holistic perspective. Sustainability, 12, 768. JCR 2018 Q2 (105/251) (2020)
  • Sánchez, A., Martín, M., Vega, P., Biomass based sustainable Ammonia production: Digestion vs Gasification. ACS Sustainable Chemistry & Engineering Design. JCR Q1 (2019).
  • Francisco, M., Mezquita, Y., Revollar, S., Vega, P., De Paz, Juan F. Multi-agent distributed model predictive control with fuzzy negotiation. Expert Systems with applications, pp. 68-83. Vol. 129. JCR Q1 (2019).
  • Vallejo Llamas, P.M., Vega, P., Analytical Fuzzy Predictive Control applied to wastewater treatment biological systems biological processes. Complexity DOI: 10.1155/2019/5720185, JCR Q1 (2018)
  • El bahja, H., Vega, P., Revollar, S., Francisco, M. One Layer Nonlinear Economic Closed-Loop Generalized Predictive Control for a Wastewater Treatment Plant. Applied Sciences, 8, 657, JCR Q3 (2018).
  • El bahja, H., Vega, P., Tadeo, F., Francisco, M., A constrained closed loop MPC based on positive invariance concept for a wastewater treatment plant. International Journal of Systems Science, pp. 2101-2115. Vol. 49, Issue 10. JCR Q1 (2018)
  • Revollar, S.; Vega, P.; Vilanova, R.; Francisco, M., Optimal Control of Wastewater Treatment Plants Using Economic-Oriented Model Predictive Dynamic Strategies. Applied Sciences, 7(8):813. JCR Q3 (2017)
  • De Villeros, P., Botero, H. Alvarez, H. State observer design for biomass and ethanol estimation in bioreactors using cybernetic models. DYNA, Revista de la Facultad de Minas.  No. 198. pp 119-127.(2016).
  • Hoyos, E., López, D., Alvarez, H., A phenomenologically based material flow model for friction stir welding. Material and Design 111. 321-330. 2016. (2016).
  • Francisco, M., Skogestad, S.,  Vega, P.  Model predictive control for the self-optimised operation in Wastewater treatment plants: Analysis of dynamic issues. Computers and Chemical Engineering vol 82, 259-272 (2015).
  • P. Vega, S. Revollar, M. Francisco y J.M. Martín. Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Computers and Chemical Engineering, 68, 78-95 (2014).
  • El Bahja, H., Veja, P. Nonlinear Feedback Control Based on Positive Invariance for A Nutrient Removal  Biological Plant. International Journal of Innovative Computing, Information and Control, Vol.10, Issue 3 (2014).
  • Vega P., Lamanna R., Revollar S., Francisco, M. Simultaneous design and control of chemical processes – part I : revision and classification. Computers and Chemical Engineering. vol: 71. pp: 602- 617. (2014).
  • Vega P., Lamanna R., Revollar S., M. Francisco.  Simultaneous design and control of chemical processes – part II: An illustrative example. Computers and Chemical Engineering vol: 71. pp:618- 635 (2014).
  • Monsalve, G., Moscoso, M., Alvarez, H.  Scale-up of batch reactors using phenomenological-based models. Industrial & Engineering Chemistry Research. DOI 10.1021/ie500058r. April 24, (2014).
  • Moscoso-Vásquez, H.M., Monsalve-Bravo, G.M. and Alvarez, H. Model-based supervisory control structure for plantwide control of a Reactor-Separator-Recycle plant. Industrial & Engineering Chemistry Research. December 3, (2014).
  • Gómez, L., Botero, H., Alvarez, H. y di Sciascio,F. Análisis de la controlabilidad de estado de sistemas irreversibles mediante teoría de conjuntos. Revista Iberoamericana de Automática e Informática Industrial (RIAI) (2014).
  • Gómez-Pérez, C.A., Gómez, L.M. and Alvarez, H. Reference trayectory design using state controllability for batch processes. Industrial & Engineering Chemistry Research. March 17, 2015. Vol. 54 (15) (2014).
  • Botero, H., Alvarez, H. y Gómez, L. Estimación de estado y control de un gasificador de carbón en lecho fluidizado presurizado. Revista Iberoamericana de Automática e Informática Industrial (RIAI), Vol. 10. No. 3. Madrid, (2013).
  • Botero, H. and Alvarez, H. Non Linear State and Parameters Estimation in Chemical Processes: Analysis and Improvement of Three Estimation Structures Applied to a CSTR. International Journal of Chemical Reactor Engineering. Vol. 9: A6. January (2011).
  • H. Elbahja, P. Vega y O. Bakka. Investigation of Different Control Strategies for the Wastewater Treatment Plant. Remote Sensing of Biomass: Principles and Applications, 179-194 Book 3, ISBN 978-953-307-491-7 (2011).
  • H. Álvarez, R. Lamanna, P. Vega y S. Revollar. Purificación y clarificación del jugo de caña de azúcar. Libro blanco del control automático en la industria de la caña de azúcar. ISBN: 978-84-614-8008-1, Depósito legal: VA-385-2011 (2011).
  •  M. Francisco, P. Vega y H. Alvarez.Robust Integrated Design of Processes with terminal penalty model predictive controllers. Chemical Engineering Research and Design, vol.89, 1011-1024 (2011).
  • S. Revollar, M. Francisco, R. Lamanna y P. Vega. Stochastic Optimization for the Simultaneous Synthesis and Control System Design of an Activated Sludge Process. Latin American Applied Research, 40, 137-146. ISSN: 0327-0793 (2010).
  • M. Francisco y P. Vega. Automatic tuning of model based predictive controllers based on Multiobjective Optimization. Latin American Applied Research, 40, 255-265. ISSN: 0327-0793 (2010).
  • A. Gajate, R. Haber, R. del Toro y P. Vega. Tool Wear Monitoring Using Neuro-Fuzzy Techniques. A Comparative Study in a Turning Process. Journal of Intelligent Manufacturing DOI: 10.1007/s10845-010-0443-y. Versión on-line (2010).
  • A. Gajate, R. Haber y P. Vega. A Transductive Neuro-Fuzzy Control. An Application to a Drilling Process. IEEE Transactions on Neural Networks. Vol 21 Issue:7, pp: 1158 – 1167 ISSN: 1045-9227. Digital Object Identifier: 10.1109/TNN.2010.2050602.
Política de privacidad
Studii Salmantini. Campus de excelencia internacional