Prof. Faramarz Doulati Ardejani | Hydrogeology | Best Researcher Award

1. Adsorption of Direct Red 80 dye from aqueous solution onto almond shells: Effect of pH, initial concentration and shell type

Citation:
F. Doulati Ardejani, K. Badii, N. Yousefi Limaee, S.Z. Shafaei, A.R. Mirhabibi. Journal of Hazardous Materials, 151(2), 730–737 (2008).
Cited by: 342
DOI: 10.1016/j.jhazmat.2007.06.039

Summary:
This study explores the effectiveness of raw and treated almond shells as low-cost adsorbents for removing Direct Red 80 dye from water. The authors investigate the effects of pH, initial dye concentration, and adsorbent type. The results demonstrate that dye removal is highly pH-dependent, with maximum adsorption occurring under acidic conditions. The isotherm and kinetic modeling indicate that the adsorption process follows the Langmuir model and pseudo-second-order kinetics.

2. Numerical modelling and laboratory studies on the removal of Direct Red 23 and Direct Red 80 dyes from textile effluents using orange peel, a low-cost adsorbent

Citation:
F. Doulati Ardejani, K. Badii, N. Yousefi Limaee, N.M. Mahmoodi, M. Arami, et al. Dyes and Pigments, 73(2), 178–185 (2007).
Cited by: 206
DOI: 10.1016/j.dyepig.2006.01.005

Summary:
This paper presents both experimental and numerical modeling of the adsorption of Direct Red 23 and Direct Red 80 dyes onto orange peel. Key operational parameters such as dye concentration, contact time, and temperature are analyzed. The study demonstrates orange peel’s promising capacity for dye removal, offering an environmentally friendly solution to textile wastewater pollution. The adsorption follows Langmuir isotherms and second-order kinetics.

3. Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data and artificial neural networks

Citation:
M. Raeesi, A. Moradzadeh, F. Doulati Ardejani, M. Rahimi. Journal of Petroleum Science and Engineering, 82, 151–165 (2012).
Cited by: 166
DOI: 10.1016/j.petrol.2012.01.008

Summary:
This work utilizes artificial neural networks (ANNs) integrated with seismic and well-log data to classify and characterize lithofacies in a hydrocarbon reservoir. The ANN approach outperforms traditional statistical methods, providing more accurate predictions of lithological and petrophysical variations, which are critical for optimizing oil and gas recovery strategies.

4. Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods

Citation:
V. Khosravi, F. Doulati Ardejani, S. Yousefi, A. Aryafar. Geoderma, 318, 29–41 (2018).
Cited by: 147
DOI: 10.1016/j.geoderma.2017.11.032

Summary:
The study integrates visible-near infrared spectroscopy with machine learning techniques—specifically Extreme Learning Machine (ELM), Support Vector Machine (SVM), and Random Forest (RF)—to estimate soil contamination by lead and zinc. The ELM model provides high accuracy, showing the potential of rapid and cost-effective soil monitoring for environmental and agricultural applications.

5. Decolorization and mineralization of textile dyes at solution bulk by heterogeneous nanophotocatalysis using immobilized nanoparticles of titanium dioxide

Citation:
N.M. Mahmoodi, M. Arami, N. Yousefi Limaee, K. Gharanjig, et al. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 290(1–3), 125–131 (2006).
Cited by: 133
DOI: 10.1016/j.colsurfa.2006.05.047

Summary:
This paper evaluates the photocatalytic degradation of various textile dyes using titanium dioxide (TiO₂) nanoparticles immobilized on glass beads. The study reveals efficient dye decolorization and mineralization under UV light, emphasizing the reusability and stability of the immobilized catalyst. It presents a sustainable method for wastewater treatment in textile industries.