Aliur Rahman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Aliur Rahman has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
A S Daiyan Ahmed
Department of Petroleum and Mining Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh
1 shared publicationAhsan Habib
Affiliation Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore, Bangladesh
1 shared publicationAmirul Islam
Department of Petroleum and Mining Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh
1 shared publicationAsadullah Munna
Affiliation Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore, Bangladesh
1 shared publicationBaytune Nahar Netema
Affiliation Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore, Bangladesh
1 shared publicationGopal Chandra Ghosh
Affiliation Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore, Bangladesh
1 shared publication
Latest Publications
- 27 February 2024
- Published by Springer Nature in Journal of Earth System Science
- Vol. 133 (1) , 1-17
Integration of geophysical, core and production data to evaluate gas reservoir performance: A study from Saldanadi field
- 27 February 2024
- Published by Springer Nature in Journal of Earth System Science
- Vol. 133 (1) , 1-17
Integration of geophysical, core and production data to evaluate gas reservoir performance: A study from Saldanadi field
- Open Access
- 23 August 2023
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 18 (8) , e0290471
Black carbon derived PET plastic bottle waste and rice straw for sorption of Acid Red 27 dye: Machine learning approaches, kinetics, isotherm and thermodynamic studies
This study focuses on the probable use of PET waste black carbon (PETWBC) and rice straw black carbon (RSBC) as an adsorbent for Acid Red 27 (AR 27) adsorption. The prepared adsorbent is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1–180 min), AR 27 concentration (5–70 mg/L), adsorbent dose (0.5–20 g/L), pH (2–10), and temperature (25–60°C). High coefficient value [PETWBC (R2 = 0.94), and RSBC (R2 = 0.97)] of process optimization model suggesting that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 99.88, and 99.89% for PETWBC, and RSBC at pH (2). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected value. Pseudo-second-order was the well-suited kinetics, where Freundlich isotherm could explain better equilibrium adsorption data. Thermodynamic study shows AR 27 adsorption is favourable, endothermic, and spontaneous. Environmental friendliness properties are confirmed by desorption studies and satisfactory results also attain from real wastewater experiments. Finally, this study indicates that PETWBC and RSBC could be potential candidates for the adsorption of AR 27 from wastewater.
- 23 August 2023
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 18 (8) , e0290471
Open AccessBlack carbon derived PET plastic bottle waste and rice straw for sorption of Acid Red 27 dye: Machine learning approaches, kinetics, isotherm and thermodynamic studies
This study focuses on the probable use of PET waste black carbon (PETWBC) and rice straw black carbon (RSBC) as an adsorbent for Acid Red 27 (AR 27) adsorption. The prepared adsorbent is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1–180 min), AR 27 concentration (5–70 mg/L), adsorbent dose (0.5–20 g/L), pH (2–10), and temperature (25–60°C). High coefficient value [PETWBC (R2 = 0.94), and RSBC (R2 = 0.97)] of process optimization model suggesting that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 99.88, and 99.89% for PETWBC, and RSBC at pH (2). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected value. Pseudo-second-order was the well-suited kinetics, where Freundlich isotherm could explain better equilibrium adsorption data. Thermodynamic study shows AR 27 adsorption is favourable, endothermic, and spontaneous. Environmental friendliness properties are confirmed by desorption studies and satisfactory results also attain from real wastewater experiments. Finally, this study indicates that PETWBC and RSBC could be potential candidates for the adsorption of AR 27 from wastewater.