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Muhammad Yaqub
Muhammad Yaqub
Kumoh National Instituite of Technology
在 kumoh.ac.kr 的电子邮件经过验证
标题
引用次数
引用次数
年份
Zero-liquid discharge (ZLD) technology for resource recovery from wastewater: A review
M Yaqub, W Lee
Science of the total environment 681, 551-563, 2019
3082019
Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network
M Yaqub, H Asif, S Kim, W Lee
Journal of Water Process Engineering 37, 2020
832020
Environmental-, social-, and governance-related factors for business investment and sustainability: A scientometric review of global trends
H Ahmad, M Yaqub, SH Lee
Environment, Development and Sustainability 26 (2), 2965-2987, 2024
802024
Heavy metals removal from aqueous solution through micellar enhanced ultrafiltration: A review
M Yaqub, SH Lee
Environmental Engineering Research 24 (3), 363-375, 2019
482019
Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: Experimental and artificial neural network modeling
M Yaqub, SH Lee
Journal of Water Process Engineering 33, 101046, 2020
442020
Electrolyzed water as a disinfectant: A systematic review of factors affecting the production and efficiency of hypochlorous acid
Rita E. Ampiaw, M Yaqub, W Lee
Journal of Water Process Engineering 43, https://doi.org/10.1016/j.jwpe.2021 …, 2021
43*2021
Comprehensive review on machine learning methodologies for modeling dye removal processes in wastewater
SK Bhagat, KE Pilario, OE Babalola, T Tiyasha, M Yaqub, CE Onu, ...
Journal of Cleaner Production 385, 135522, 2023
392023
Treating reverse osmosis concentrate to address scaling and fouling problems in zero-liquid discharge systems: A scientometric review of global trends
M Yaqub, MN Nguyen, W Lee
Science of The Total Environment 844, 157081, 2022
372022
Modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models
M Yaqub, W Lee
Journal of Water Process Engineering 46, 102521, 2022
282022
Experimental and neural network modeling of micellar enhanced ultrafiltration for arsenic removal from aqueous solution
M Yaqub, SH Lee
Environmental Engineering Research 26 (1), 2021
262021
Soft computing techniques in prediction Cr (VI) removal efficiency of polymer inclusion membranes
M Yaqub, B Eren, V Eyupoglu
KOREAN SOC ENVIRONMENTAL ENGINEERS, 2020
262020
Flood causes, consequences and protection measures in Pakistan
M Yaqub, B Eren, E Doğan
Disaster Science and Engineering 1 (1), 8-16, 2015
242015
Investigating micellar-enhanced ultrafiltration (MEUF) of mercury and arsenic from aqueous solution using response surface methodology and gene expression programming
M Yaqub, S H Lee, W Lee
Separation and Purification Technology 281 (15), 2021
23*2021
Environmental Consciousness Survey of University Students
B EREN, M Yaqub
ISITES2015 Valencia -Spain, 2015
192015
Optimization of cesium adsorption by Prussian blue using experiments and gene expression modeling
MN Nguyen, M Yaqub, S Kim, W Lee
Journal of Water Process Engineering 41, 102084, 2021
182021
Assessment of long-term nutrient effective waste-derived growth media for ornamental nurseries
S Ozdemir, OH Dede, M Yaqub
Waste and Biomass Valorization 8, 2663-2671, 2017
182017
Assessment of neural network training algorithms for the prediction of polymeric inclusion membranes efficiency
M Yaqub, B Eren, V Eyüpoğlu
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi 20 (3), 533-542, 2016
17*2016
A comparative study of artificial neural network models for the prediction of Cd removal efficiency of polymer inclusion membranes
B Eren, M Yaqub, V Eyupoglu
Desalination Water Treat 143, 48-58, 2019
152019
Optimization of hypochlorous acid generation by HCl electrolysis through response surface methodology and artificial neural networks
M Yaqub, C Woo, W Lee
Journal of Environmental Chemical Engineering 9 (5), 105826, 2021
142021
Adsorption of Microcystin onto Activated Carbon: A Review
RE Ampiaw, M Yaqub, W Lee
Membrane Water Treatment 10 (6), 405-415, 2019
142019
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