Intitulé de l’équipe de recherche : Phénomènes de transport dans les matériaux poreux & environnement
Acronyme et code : PTMPE C0560101
Domaine : SI/ Génie des procédés et environnement
COMPOSANTE HUMAINE :
Chef d’équipe : Pr. KHAOUANE Latifa
Description scientifique du programme de recherche
C’est l’étude par approches cognitives des phénomènes de transport. Cette thématique revendique plusieurs aspects de l’ingénierie.
Elle concerne d’une part et notamment la pharmacocinétique des médicaments, l’analyse des opérations unitaires, l’extraction des huiles essentielles et le traitement des effluents pharmaceutiques en font aussi partie, par une modélisation globaliste et mettant en évidence les comportements et la sensibilité aux paramètres pour une aide à la décision. Plusieurs méthodologies sont utilisées, le RNA principalement pour l’optimisation des procédés suite à la détermination des corrélations adéquates sur la base de données aussi vastes que possible.
D’autre part, la modélisation et simulation des procédés divers impliquant les énergies renouvelables sont aussi développées, en focalisant sur l’énergie solaire, liée à la réfrigération, au séchage ou encore au pompage.…
| N° | Nom et Prénom | Grade | Qualité | Etablissement |
| 1 | KHAOUANE Latifa | Pr. | Chef d’équipe | Fac. Technologie / UYFM |
| 2 | HANINI Salah | Pr. | Membre | Fac. Technologie / UYFM |
| 3 | SI MOUSSA Cherif | Pr. | Membre | Fac. Technologie / UYFM |
| 4 | BELHADJ Abdelmouneim | Pr. | Membre | Fac. Technologie / UYFM |
| 5 | AMMI Yamina | MCA | Membre | Fac. Technologie / UYFM |
| 6 | GHALEM Naima | MCB | Membre | Fac. Sciences / UYFM |
| 7 | SEDIRI Meriem | MCB | Membre | Fac. Sciences / UYFM |
| 8 | BELGHAIT Aicha | MCB | Membre | Fac. Sciences / UYFM |
| 9 | EULDJI Amel | MAA | Membre | Fac. Sciences / UYFM |
| 10 | KHAOUANE Affaf | MAB | Membre | Fac. Sciences / UYFM |
publications :
| N | Nom et prénom des auteurs |
Titre de l’article | Journal | Catégorie A+/A/B/C |
Lien sur net ou DOI |
Année de publication |
| 1 | Y Ammi, L Khaouane, S Hanini | A comparison of “neural networks and multiple linear regressions” models to describe the rejection of micropollutants by membranes | Kemija u industriji: Časopis kemičara i kemijskih inženjera Hrvatske | B | https://hrcak.srce.hr/clanak/342677 | 2020 |
| 2 | N Melzi, L Khaouane, S Hanini, M Laidi, Y Ammi, H Zentou | Optimization methodology of artificial neural network models for predicting molecular diffusion coefficients for polar and non-polar binary gases | Journal of Applied Mechanics and Technical Physics | B | https://link.springer.com/article/10.1134/S0021894420020066 | 2020 |
| 3 | H Maouz, L Khaouane, S Hanini, Y Ammi, M Hamadache, M Laidi | QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR | Kemija u industriji: Časopis kemičara i kemijskih inženjera Hrvatske | B | https://hrcak.srce.hr/clanak/338337 | 2020 |
| 4 | H Maouz, L Khaouane, S Hanini, Y Ammi, M Hamadache, M Laidi | QSPR studies of carbonyl, hydroxyl, polyene indices, and viscosity average molecular weight of polymers under photostabilization using ANN and MLR approaches | Kem. Ind | B | http://silverstripe.fkit.hr/kui/assets/Uploads/1-1-16.pdf | 2020 |
| 5 | H Maouz, L Khaouane, S Hanini, Y Ammi, M Laidi, H Benimam | The prediction of carbonyl groups during photo-thermal and thermal aging of polymers using artificial neural networks | Algerian Journal of Environmental Science and Technology | https://www.aljest.net/index.php/aljest/article/view/333 | 2020 | |
| 6 | S Belmadani, S Hanini, M Laidi, C Si-Moussa, M Hamadache | Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis. | Kemija u Industriji | B | http://silverstripe.fkit.hr/kui/assets/Uploads/1-355-364.pdf | 2020 |
| 7 | H Benimam, C Si-Moussa, M Hentabli, S Hanini, M Laidi | Dragonfly-support vector machine for regression modeling of the activity coefficient at infinite dilution of solutes in imidazolium ionic liquids using σ-profile descriptors | Journal of Chemical & Engineering Data | A | https://pubs.acs.org/doi/abs/10.1021/acs.jced.0c00168 | 2020 |
| 8 | Y Ammi, L Khaouane, S Hanini | Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds | Neural Computing and Applications | B | https://link.springer.com/article/10.1007/s00521-021-05876-0 | 2021 |
| 9 | Y Ammi, S Hanini, L Khaouane | An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine | Comptes Rendus. Chimie | B | https://comptes-rendus.academie-sciences.fr/chimie/item/CRCHIM_2021__24_2_243_0/ | 2021 |
| 10 | M Laidi, HA Abdallah, C Si-Moussa, O Benkortebi, M Hentabli, S Hanini | CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA | Chemical Industry and Chemical Engineering Quarterly | B | https://doiserbia.nb.rs/Article.aspx?id=1451-93722000048L | 2021 |
| 11 | W BENMOULOUD, C SI-MOUSSA, O BENKORTBI | Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ-profile descriptors | International Journal of Quantum Chemistry | B | https://onlinelibrary.wiley.com/doi/abs/10.1002/qua.27026 | 2022 |
| 12 | I EULDJI, C SI-MOUSSA, M HAMADACHE, O BENKORTBI | QSPR Modelling of The Solubility of Drug and Drug-Like Compounds in Supercritical Carbon Dioxide | Molecular Informatics | B | https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.202200026 | 2022 |
| 13 | EA Saleh, L Khaouane, S Hanini, M Laidi | Development of Novel Dimensionless Parameters for Accurate Estimation of Properties in Fluidized Beds | Iranian Journal of Chemistry and Chemical Engineering | B | https://www.ijcce.ac.ir/article_709257.html | 2023 |
| 14 | A Khaouane, L Khaouane, S Ferhat, S Hanini | Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs | AAPS PharmSciTech | B | https://link.springer.com/article/10.1208/s12249-023-02686-6 | 2023 |
| 15 | Faiza Omari, Latifa Khaouane, Maamar Laidi, Abdellah Ibrir, Mohamed Roubehie Fissa, Mohamed Hentabli, Salah Hanini | Dragonfly algorithm–support vector machine approach for prediction the optical properties of blood | Computer Methods in Biomechanics and Biomedical Engineering | B | https://www.tandfonline.com/doi/abs/10.1080/10255842.2023.2228957 | 2023 |
| 16 | MR Fissa, Y Lahiouel, L Khaouane, S Hanini | Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons | Journal of Molecular Graphics and Modelling | A | https://www.sciencedirect.com/science/article/abs/pii/S1093326323000487 | 2023 |
| 17 | A Dahmani, Y Ammi, S Hanini | A Novel Non-Linear Model Based on Bootstrapped Aggregated Support Vector Machine for the Prediction of Hourly Global Solar Radiation | Smart Grids and Sustainable Energy | B | https://link.springer.com/article/10.1007/s40866-023-00179-w | 2023 |
| 18 | Y Ammi, C Si-Moussa, S Hanini | Machine Learning and Neural Networks for Modelling the Retention of PPhACs by NF/RO | Kemija u industriji: Časopis kemičara i kemijskih inženjera Hrvatske | B | https://hrcak.srce.hr/309800 | 2023 |
| 19 | F Kratbi, Y Ammi, S Hanini | Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules. | Kemija u Industriji | B | http://silverstripe.fkit.hr/kui/assets/Uploads/1-417-431-KUI-7-8-2023.pdf | 2023 |
| 20 | A Dahmani, Y Ammi, S Hanini, M Redha Yaiche, H Zentou | Prediction of hourly global solar radiation: comparison of neural networks/bootstrap aggregating | Kemija u industriji: Časopis kemičara i kemijskih inženjera Hrvatske | B | https://hrcak.srce.hr/295720 | 2023 |
| 21 | Abdennasser Dahmani, Yamina Ammi, Nadjem Bailek, Alban Kuriqi, Nadhir Al-Ansari, Salah Hanini, Ilhami Colak, Laith Abualigah, El-Sayed M El-Kenawy | Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction | Computers, Materials and Continua | B | https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1824733&dswid=988 | 2023 |
| 22 | I Euldji, A Belghait, C Si-Moussa, O Benkortbi, A Amrane | A new hybrid quantitative structure property relationships-support vector regression (QSPR-SVR) approach for predicting the solubility of drug compounds in supercritical carbon | AIChE Journal | A | https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.18115 | 2023 |
| 23 | S Tared, L Khaouane, S Hanini, A Khaouane, M Roubehie Fissa | Enhancing lung cancer prediction through crow search, artificial bee colony algorithms, and support vector machine | International Journal of Information Technology | B | https://link.springer.com/article/10.1007/s41870-024-01770-9 | 2024 |
| 24 | A Bouzidi, Y Ammi, N Baaka, M Hentabli, H Maouz, M Laidi, S Hanini | Artificial Neural Network Approach to Predict the Colour Yield of Wool Fabric Dyed with Limoniastrum monopetalum Stems | Chemistry Africa | B | https://link.springer.com/article/10.1007/s42250-023-00755-8 | 2024 |
| 25 | I Euldji, W Benmouloud, K Paduszyński, C Si-Moussa, O Benkortbi | Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ-Profile Descriptors | Journal of Chemical Information and Modeling | A | https://pubs.acs.org/doi/abs/10.1021/acs.jcim.3c01876 | 2024 |