Wassila Ajbar

@unam.mx

Institute of Engineering, UNAM
National Autonomous University of Mexico (UNAM)

Wassila Ajbar


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https://researchid.co/wassila
Dr. Wassila Ajbar is a postdoctoral researcher at École des Mines de Saint-Étienne, France, participating in a European project funded by ERAMIN. She previously completed a postdoctoral fellowship at the National Autonomous University of Mexico’s Institute of Engineering, funded by DGAPA. She holds a PhD in Engineering from the Center for Research in Engineering and Applied Sciences (CIICAp-UAEM), Mexico, specializing in “Thermal Performance Improvement of Parabolic Trough Solar Collector Systems through Artificial She also holds two Master's degrees, one in Environmental Engineering and Industrial Management from the Faculty of Sciences and Techniques of Tangier, Morocco, and the other in the same field from CIICAp-UAEM. Her expertise encompasses artificial intelligence, ANN, CNN, RNN, multivariable optimization, numerical simulation, and sensitivity analysis. Dr. Ajbar is a recognized member of Mexico's National System of Researchers (SNII).

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Artificial Intelligence, Computer Engineering, Computer Science Applications
10

Scopus Publications

315

Scholar Citations

7

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Expert system for the parabolic trough collector control through classical and conformable transfer functions in ANNi-PSO
    Wassila Ajbar, Marisol Cervantes-Bobadilla, José Alfredo Hernández–Pérez, Jesús Emmanuel Solis-Perez, José Francisco Gómez-Aguilar, Jarniel García-Morales, Arianna Parrales-Bahena
    Expert Systems with Applications, 2025
  • Optimization and performance enhancement of parabolic trough collectors using hybrid nanofluids and ANN modeling
    Santosh Kumar Singh, Arun Kumar Tiwari, Wassila Ajbar
    Journal of the Taiwan Institute of Chemical Engineers, 2025
    Background Enhancing the performance of renewable energy systems is pivotal to overcoming global energy challenges, with parabolic trough collectors (PTC) emerging as a promising technology for harnessing solar energy. This study focuses on the numerical modeling with various nanofluids of the PTC. Many previously reported studies on hybrid nanofluids have not adequately considered the appropriate Reynolds, Prandtl numbers and temperature, raising concerns about the reliability. This study ensures proper Reynolds numbers and inlet temperature for the investigated heat transfer fluids. Methods The model is tested with mono (Al 2 O 3 , TiO 2 ) and hybrid (Al 2 O 3 +TiO 2 ) nanofluids, with thermal analysis mathematically modelled. ANN and ANNi modeling are applied in to optimize governing parameters for the collector with working fluids. Findings Turbulence boosts heat transfer coefficient (HTC), with the hybrid nanofluid exhibiting the highest increase (86.88 %). Energy efficiency peaks at lower temperatures and fluid Reynolds numbers, while exergy efficiency rises with increasing inlet temperature and Reynolds number. The optimal performance of ANN model is achieved with four neurons in the hidden layer and a 6-4-1 architecture, with highest value of R 2 (0.999513) and lowest RMSE (0.000186). GA optimization shows that lower inlet fluid temperature and Reynolds number enhance thermal efficiency.
  • Development of artificial neural networks for the prediction of the pressure field along a horizontal pipe conveying high-viscosity two-phase flow
    W. Ajbar, L. Torres, J.E.V. Guzmán, J. Hernández-García, A. Palacio-Pérez
    Flow Measurement and Instrumentation, 2024
  • Outlet Pressure Regulation in a High-Viscosity Two-Phase Flow Horizontal Pipeline Using Inverse ANN and PSO
    Wassila Ajbar, Lizeth Torres, José Enrique Guzmán Vázquez, Marisol Cervantes-Bobadilla
    10th 2024 International Conference on Control Decision and Information Technologies Codit 2024, 2024
    This article presents the development of two models based on artificial neural networks (ANN) aimed at estimating the pressure at a position near the output of a horizontal pipeline that transports a two-phase flow of glycerin-air, which is characterized by its high viscosity. These models consist of an input layer that includes variables such as the air flow rate (Q<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</inf>), the glycerin flow rate (Q<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</inf>), and the pressure at three spatial points located before the desired pressure prediction point. In the hidden layer of the first model, the nonlinear activation function Logsig is employed, while in the second model, the Tansig function is used. In both cases, a linear function is used in the output layer. In order to determine the air and glycerin flow rates that were injected to the pipeline, ANN models ares inverted by using the particle swarm optimization (PSO) algorithm. From these inverted models, the design of an open-loop control to regulate the output pressure in the horizontal pipeline is presented. This control allows simultaneous manipulation of the Q<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</inf> and Q<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</inf> variables, or a single Q<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</inf> variable, enabling precise regulation.
  • Improvement of the classical artificial neural network simulation model of the parabolic trough solar collector outlet temperature and thermal efficiency using the conformable activation functions
    W. Ajbar, J.E. Solís-Pérez, E. Viera-Martin, A. Parrales, J.F. Gómez-Aguilar, J.A. Hernández
    Sustainable Energy Grids and Networks, 2023
  • Thermal efficiency improvement of parabolic trough solar collector using different kinds of hybrid nanofluids
    Wassila Ajbar, J.A. Hernández, A. Parrales, Lizeth Torres
    Case Studies in Thermal Engineering, 2023
    In this paper, the use of eight hybrid nanofluids to improve the thermal efficiency of a parabolic trough solar collector (PTSC) is investigated. The analysis is performed with the thermal model developed and validated with Sandia National Laboratory’s experimental data using pure Syltherm 800. The developed model results prove a good agreement with the experimental study with an average error of 1.92% and 2.34% to calculate the outlet temperature and thermal efficiency. The simulation results showed that PTSC thermal efficiency could achieve its maximum improvement of 2.8% using hybrid nanofluids evaluated and an average improvement of PTSC thermal efficiency of 1.6% under the operating conditions of the examined tests compared to Syltherm 800. All hybrid nanofluids achieve a better PTSC’s thermal efficiency than the base fluid, and the difference between them is insignificant due to keeping the total concentration constant (ϕ=3%) for all of them. This improvement of PTSC’s thermal efficiency using hybrid nanofluid is explained by the most significant increase and improvement of heat transfer coefficient and Nusselt number compared to pure Syltherm 800. This paper is beneficial to researchers focused on applying and improving the PTSC’s thermal performance based on the improvement of heat transfer fluids.
  • Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review
    Wassila Ajbar, A. Parrales, A. Huicochea, J.A. Hernández
    Renewable and Sustainable Energy Reviews, 2022
  • Artificial neural network applied to the renewable energy system performance
    A. Parrales, E.D. Reyes-Téllez, W. Ajbar, J.A. Hernández
    Artificial Neural Networks for Renewable Energy Systems and Real World Applications, 2022
  • Identification of the relevant input variables for predicting the parabolic trough solar collector's outlet temperature using an artificial neural network and a multiple linear regression model
    Wassila Ajbar, A. Parrales, S. Silva-Martínez, A. Bassam, O. A. Jaramillo, J. A. Hernández
    Journal of Renewable and Sustainable Energy, 2021
    The main objective of this study is to present the most influencing input variables for a parabolic trough solar collector (PTSC) outlet temperature through prediction and optimization. Six artificial neural network (ANN) and four multiple linear regression (MLR) models were proposed, validated, and compared in detail. Temperature, wind speed, rim angle, flow rate, and solar radiation were used as input variables. The simulation showed that ANN-1 and MLR with Second-Order Equation (SOE) are the models that yielded the best results with R2 = 0.9984 and R2 = 0.9958 and with an RMSE = 0.7708 and 1.6031, respectively. The sensitivity analysis results of the ANN-1 model trained, with and without biases, showed that the inlet temperature was the most significant parameter influencing the PTSC outlet temperature. Both models yielding the best results were inverted to estimate the optimal input parameter using the trust-region reflective algorithm optimization method. The optimization results showed that ANNi and MLR-SOEi estimated the input temperature with an error &amp;lt; 4.008% and had a very short-elapsed prediction time &amp;lt;0.2277 s. Due to high accuracy and short computing time, ANN-1 and ANNi are more suitable than MLR-SOE for simulating and optimizing the PTSC outlet temperature. Likewise, the MLR-SOE method proved to be a simpler and cheaper alternative than the ANN method.
  • The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector
    Wassila Ajbar, A. Parrales, U. Cruz-Jacobo, R.A. Conde-Gutiérrez, A. Bassam, O.A. Jaramillo, J.A. Hernández
    Applied Thermal Engineering, 2021

RECENT SCHOLAR PUBLICATIONS

  • Expert system for the parabolic trough collector control through classical and conformable transfer functions in ANNi-PSO
    W Ajbar, M Cervantes-Bobadilla, JA Hernández–Pérez, JE Solis-Perez, ...
    Expert Systems with Applications 280, 127343 , 2025
    2025.0
    Citations: 1
  • Optimization and performance enhancement of parabolic trough collectors using hybrid nanofluids and ANN modeling
    SK Singh, AK Tiwari, W Ajbar
    Journal of the Taiwan Institute of Chemical Engineers 169, 105984 , 2025
    2025.0
    Citations: 11
  • Outlet Pressure Regulation in a High-Viscosity Two-Phase Flow Horizontal Pipeline Using Inverse ANN and PSO
    W Ajbar, L Torres, JEG Vázquez, M Cervantes-Bobadilla
    2024 10th International Conference on Control, Decision and Information … , 2024
    2024.0
  • Development of artificial neural networks for the prediction of the pressure field along a horizontal pipe conveying high-viscosity two-phase flow
    W Ajbar, L Torres, JEV Guzmán, J Hernández-García, A Palacio-Pérez
    Flow Measurement and Instrumentation 96, 102541 , 2024
    2024.0
    Citations: 16
  • Improvement of the classical artificial neural network simulation model of the parabolic trough solar collector outlet temperature and thermal efficiency using the conformable …
    W Ajbar, JE Solís-Pérez, E Viera-Martin, A Parrales, JF Gómez-Aguilar, ...
    Sustainable Energy, Grids and Networks 36, 101200 , 2023
    2023.0
    Citations: 20
  • Thermal efficiency improvement of parabolic trough solar collector using different kinds of hybrid nanofluids
    W Ajbar, JA Hernández, A Parrales, L Torres
    Case Studies in Thermal Engineering 42, 102759 , 2023
    2023.0
    Citations: 69
  • Inteligencia artificial aplicada a colectores solares para el calentamiento de agua de uso doméstico
    JAH PEREZ, W AJBAR, AP Bahena, AH RODRIGUEZ, DJ ROMERO
    Inventio, la génesis de la cultura universitaria en Morelos , 2023
    2023.0
  • Mejoramiento del rendimiento térmico del sistema de los colectores solares de canal parabólico mediante la inteligencia artificial
    W AJBAR
    El autor , 2022
    2022.0
  • Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review
    W Ajbar, A Parrales, A Huicochea, JA Hernández
    Renewable and Sustainable Energy Reviews 156, 111947 , 2022
    2022.0
    Citations: 87
  • Inteligencia artificial aplicada a colectores solares para el calentamiento de agua de uso doméstico
    W Ajbar, JAH Pérez, AP Bahena, A Huicochea, D Juárez-Romero
    Inventio 18 (45), 1-10 , 2022
    2022.0
    Citations: 1
  • Artificial neural network applied to the renewable energy system performance
    A Parrales, ED Reyes-Téllez, W Ajbar, JA Hernández
    Artificial Neural Networks for Renewable Energy Systems and Real-World … , 2022
    2022.0
    Citations: 5
  • Identification of the relevant input variables for predicting the parabolic trough solar collector's outlet temperature using an artificial neural network and a multiple linear …
    W Ajbar, A Parrales, S Silva-Martínez, A Bassam, OA Jaramillo, ...
    Journal of Renewable and Sustainable Energy 13 (4) , 2021
    2021.0
    Citations: 21
  • The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector
    W Ajbar, A Parrales, U Cruz-Jacobo, RA Conde-Gutiérrez, A Bassam, ...
    Applied Thermal Engineering 189, 116651 , 2021
    2021.0
    Citations: 81
  • Sistema de concentradores solares de canal parabólico para la generación de calor de proceso: diseño, construcción y modelado matemático
    W AJBAR
    El autor , 2019
    2019.0
    Citations: 3
  • Artificial Neural Networks for Predicting Pressure in High-Viscosity Two-Phase Flow: A Comparative Analysis
    W Ajbar, L Torres, JEV Guzmán, A Palacio-Pérez

MOST CITED SCHOLAR PUBLICATIONS

  • Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review
    W Ajbar, A Parrales, A Huicochea, JA Hernández
    Renewable and Sustainable Energy Reviews 156, 111947 , 2022
    2022.0
    Citations: 87
  • The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector
    W Ajbar, A Parrales, U Cruz-Jacobo, RA Conde-Gutiérrez, A Bassam, ...
    Applied Thermal Engineering 189, 116651 , 2021
    2021.0
    Citations: 81
  • Thermal efficiency improvement of parabolic trough solar collector using different kinds of hybrid nanofluids
    W Ajbar, JA Hernández, A Parrales, L Torres
    Case Studies in Thermal Engineering 42, 102759 , 2023
    2023.0
    Citations: 69
  • Identification of the relevant input variables for predicting the parabolic trough solar collector's outlet temperature using an artificial neural network and a multiple linear …
    W Ajbar, A Parrales, S Silva-Martínez, A Bassam, OA Jaramillo, ...
    Journal of Renewable and Sustainable Energy 13 (4) , 2021
    2021.0
    Citations: 21
  • Improvement of the classical artificial neural network simulation model of the parabolic trough solar collector outlet temperature and thermal efficiency using the conformable …
    W Ajbar, JE Solís-Pérez, E Viera-Martin, A Parrales, JF Gómez-Aguilar, ...
    Sustainable Energy, Grids and Networks 36, 101200 , 2023
    2023.0
    Citations: 20
  • Development of artificial neural networks for the prediction of the pressure field along a horizontal pipe conveying high-viscosity two-phase flow
    W Ajbar, L Torres, JEV Guzmán, J Hernández-García, A Palacio-Pérez
    Flow Measurement and Instrumentation 96, 102541 , 2024
    2024.0
    Citations: 16
  • Optimization and performance enhancement of parabolic trough collectors using hybrid nanofluids and ANN modeling
    SK Singh, AK Tiwari, W Ajbar
    Journal of the Taiwan Institute of Chemical Engineers 169, 105984 , 2025
    2025.0
    Citations: 11
  • Artificial neural network applied to the renewable energy system performance
    A Parrales, ED Reyes-Téllez, W Ajbar, JA Hernández
    Artificial Neural Networks for Renewable Energy Systems and Real-World … , 2022
    2022.0
    Citations: 5
  • Sistema de concentradores solares de canal parabólico para la generación de calor de proceso: diseño, construcción y modelado matemático
    W AJBAR
    El autor , 2019
    2019.0
    Citations: 3
  • Expert system for the parabolic trough collector control through classical and conformable transfer functions in ANNi-PSO
    W Ajbar, M Cervantes-Bobadilla, JA Hernández–Pérez, JE Solis-Perez, ...
    Expert Systems with Applications 280, 127343 , 2025
    2025.0
    Citations: 1
  • Inteligencia artificial aplicada a colectores solares para el calentamiento de agua de uso doméstico
    W Ajbar, JAH Pérez, AP Bahena, A Huicochea, D Juárez-Romero
    Inventio 18 (45), 1-10 , 2022
    2022.0
    Citations: 1
  • Outlet Pressure Regulation in a High-Viscosity Two-Phase Flow Horizontal Pipeline Using Inverse ANN and PSO
    W Ajbar, L Torres, JEG Vázquez, M Cervantes-Bobadilla
    2024 10th International Conference on Control, Decision and Information … , 2024
    2024.0
  • Inteligencia artificial aplicada a colectores solares para el calentamiento de agua de uso doméstico
    JAH PEREZ, W AJBAR, AP Bahena, AH RODRIGUEZ, DJ ROMERO
    Inventio, la génesis de la cultura universitaria en Morelos , 2023
    2023.0
  • Mejoramiento del rendimiento térmico del sistema de los colectores solares de canal parabólico mediante la inteligencia artificial
    W AJBAR
    El autor , 2022
    2022.0
  • Artificial Neural Networks for Predicting Pressure in High-Viscosity Two-Phase Flow: A Comparative Analysis
    W Ajbar, L Torres, JEV Guzmán, A Palacio-Pérez