Abuwatfa, W. H., Al-Muqbel, D., Al-Othman, A., Halalsheh, N., & Tawalbeh, M., 2021. Insights into the removal of microplastics from water using biochar in the era of COVID-19: A mini review. Case Studies Chem. Environ. Eng. 4, 100151.
https://doi.org/10.1016/j.cscee.2021.100151
Al Sharabati, M., Abokwiek, R., Al-Othman, A., Tawalbeh, M., Karaman, C., Orooji, Y., & Karimi, F., 2021. Biodegradable polymers and their nano-composites for the removal of endocrine-disrupting chemicals (EDCs) from wastewater: A review. Environ. Res. 202, 111694.
https://doi.org/10.1016/j.envres.2021.111694
Alam, G., Ihsanullah, I., Naushad, Mu., & Sillanpää, M., 2022. Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chem. Eng. J. 427, 130011.
https://doi.org/10.1016/j.cej.2021.130011
Al-Bsoul, A., Al-Shannag, M., Tawalbeh, M., Al-Taani, A. A., Lafi, W. K., Al-Othman, A., & Alsheyab, M. (2020). Optimal conditions for olive mill wastewater treatment using ultrasound and advanced oxidation processes. Sci. The Total Environ. 700, 134576.
https://doi.org/10.1016/j.scitotenv.2019.134576
Ali, A.M., Rønning, H.T., Alarif, W., Kallenborn, R., Al-Lihaibi, S.S., 2017. Occurrence of pharmaceuticals and personal care products in effluent-dominated Saudi Arabian coastal waters of the Red Sea. Chemosphere 175, 505–513.
https://doi.org/10.1016/j.chemosphere.2017.02.095
Al-Othman, A., Tawalbeh, M., Martis, R., Dhou, S., Orhan, M., Qasim, M., Ghani Olabi, A., 2022. Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects. Energy Conver. Manag. 253, 115154.
https://doi.org/10.1016/j.enconman.2021.115154
Al-Qodah, Z., Tawalbeh, M., Al-Shannag, M., Al-Anber, Z., Bani-Melhem, K., 2020. Combined electrocoagulation processes as a novel approach for enhanced pollutants removal: A state-of-the-art review. Sci. The Total Environ. 744, 140806.
https://doi.org/10.1016/j.scitotenv.2020.140806
Al-Rajab, A.J., Al Bratty, M., Hakami, O., Alhazmi, H.A., Sharma, M., Reddy, D.N., 2019. Investigation of the presence of pharmaceuticals and personal care products (PPCPs) in groundwater of Jazan area, Saudi Arabia. Tropical J. Pharma. Res. 17(10), 2061.
https://doi.org/10.4314/tjpr.v17i10.24
Arefi-Oskoui, S., Khataee, A., Vatanpour, V., 2017. Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid. ACS
Combin. Sci. 19(7), 464–477.
https://doi.org/10.1021/acscombsci.7b00046
Badrnezhad, R., Mirza, B., 2014. Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach. J. Ind. Eng. Chem. 20(2), 528–543.
https://doi.org/10.1016/j.jiec.2013.05.012
Bhattacharya, P., Mukherjee, D., Dey, S., Ghosh, S., Banerjee, S., 2019. Development and performance evaluation of a novel CuO/TiO2 ceramic ultrafiltration membrane for ciprofloxacin removal. Mater. Chem. Phys. 229, 106–116.
https://doi.org/10.1016/j.matchemphys.2019.02.094
Cabrera, P., Carta, J.A., González, J., Melián, G., 2017. Artificial neural networks applied to manage the variable operation of a simple seawater reverse osmosis plant. Desalination 416, 140–156.
https://doi.org/10.1016/j.desal.2017.04.032
Cevallos-Mendoza, J., Amorim, C.G., Rodríguez-Díaz, J.M., Montenegro, M. da C. B. S. M.,2022. Removal of Contaminants from Water by Membrane Filtration: A Review. Membranes 12(6), 570.
https://doi.org/10.3390/membranes12060570
Chew, C.M., Aroua, M.K., Hussain, M.A., 2017. A practical hybrid modelling approach for the prediction of potential fouling parameters in ultrafiltration membrane water treatment plant. J. Ind. Eng. Chem. 45, 145–155.
https://doi.org/10.1016/j.jiec.2016.09.017
Egea-Corbacho Lopera, A., Gutiérrez Ruiz, S., Quiroga Alonso, J.M., 2019. Removal of emerging contaminants from wastewater using reverse osmosis for its subsequent reuse: Pilot plant. J. Water Proc. Eng. 29, 100800.
https://doi.org/10.1016/j.jwpe.2019.100800
Gaya, M.S., Abba, S.I., Abdu, A.M., Tukur, A.I., Saleh, M.A., Esmaili, P., Wahab, N.A., 2020. Estimation of water quality index using artificial intelligence approaches and multi-linear regression. IAES Int. J. Artificial Intelligence (IJ-AI), 9(1), 126.
https://doi.org/10.11591/ijai.v9.i1.pp126-134
Guest, J.S., Skerlos, S.J., Barnard, J.L., Beck, M.B., Daigger, G.T., Hilger, H., Jackson, S.J., Karvazy, K., Kelly, L., Macpherson, L., Mihelcic, J.R., Pramanik, A., Raskin, L., Van Loosdrecht, M.C.M., Yeh, D., & Love, N.G., 2009. A New Planning and Design Paradigm to Achieve Sustainable Resource Recovery from Wastewater. Environ. Sci. Tech. 43(16), 6126–6130.
https://doi.org/10.1021/es9010515
Heger, M., Vashold, L., Palacios, A., Alahmadi, M., Bromhead, M.A., Acerbi, M., 2022. Blue Skies, Blue Seas: Air Pollution, Marine Plastics, and Coastal Erosion in the Middle East and North Africa. The World Bank.
https://doi.org/10.1596/978-1-4648-1812-7
Holdich, R., Dragosavac, M., Williams, B., Trotter, S., 2020. High throughput membrane emulsification using a single‐pass annular flow crossflow membrane. AIChE J. 66(6).
https://doi.org/10.1002/aic.16958
Holloway, R.W., Wait, A.S., Fernandes da Silva, A., Herron, J., Schutter, M.D., Lampi, K., Cath, T.Y., 2015. Long-term pilot scale investigation of novel hybrid ultrafiltration-osmotic membrane bioreactors. Desalination 363, 64–74.
https://doi.org/10.1016/j.desal.2014.05.040
Hu, J., Kim, C., Halasz, P., Kim, J.F., Kim, J., Szekely, G., 2021. Artificial intelligence for performance prediction of organic solvent nanofiltration membranes. J. Membr. Sci. 619, 118513.
https://doi.org/10.1016/j.memsci.2020.118513
Kacprzyńska-Gołacka, J., Łożyńska, M., Barszcz, W., Sowa, S., Wieciński, P., Woskowicz, E., 2020. Microfiltration Membranes Modified with Composition of Titanium Oxide and Silver Oxide by Magnetron Sputtering. Polymers 13(1), 141.
https://doi.org/10.3390/polym13010141
Khan, S., Naushad, Mu., Govarthanan, M., Iqbal, J., Alfadul, S.M., 2022. Emerging contaminants of high concern for the environment: Current trends and future research. Environ. Res. 207, 112609.
https://doi.org/10.1016/j.envres.2021.112609
Khaouane, L., Ammi, Y., Hanini, S., 2017. Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks. Arabian J. Sci. Eng. 42(4), 1443–1453.
https://doi.org/10.1007/s13369-016-2320-2
Lin, W., Jing, L., Zhu, Z., Cai, Q., Zhang, B., 2017. Removal of Heavy Metals from Mining Wastewater by Micellar-Enhanced Ultrafiltration (MEUF): Experimental Investigation and Monte Carlo-Based Artificial Neural Network Modeling. Water, Air, & Soil Pollution, 228(6), 206.
https://doi.org/10.1007/s11270-017-3386-5
Liu, L., Luo, X.B., Ding, L., Luo, S.L., 2019. Application of Nanotechnology in the Removal of Heavy Metal From Water. in: Nanomaterials for the Removal of Pollutants and Resource Reutilization (pp. 83–147). Elsevier.
https://doi.org/10.1016/B978-0-12-814837-2.00004-4
Modak, S., Mokarizadeh, H., Karbassiyazdi, E., Hosseinzadeh, A., Esfahani, M.R., 2022. The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment. in: Artificial Intelligence and Data Science in Environmental Sensing (pp. 211–244). Elsevier.
https://doi.org/10.1016/B978-0-323-90508-4.00005-8
Niu, C., Li, X., Dai, R., Wang, Z., 2022. Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review. Water Res. 216, 118299.
https://doi.org/10.1016/j.watres.2022.118299
Nur Adli Zakaria, M., Abdul Malek, M., Zolkepli, M., Najah Ahmed, A., 2021. Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia. Alexandria Eng. J. 60(4), 4015–4028.
https://doi.org/10.1016/j.aej.2021.02.046
Ouda, M., Kadadou, D., Swaidan, B., Al-Othman, A., Al-Asheh, S., Banat, F., Hasan, S.W., 2021. Emerging contaminants in the water bodies of the Middle East and North Africa (MENA): A critical review. Sci. The Total Environ. 754, 142177.
https://doi.org/10.1016/j.scitotenv.2020.142177
Park, H.B., Kamcev, J., Robeson, L.M., Elimelech, M., Freeman, B.D., 2017. Maximizing the right stuff: The trade-off between membrane permeability and selectivity. Science 356(6343).
https://doi.org/10.1126/science.aab0530
Park, S., Baek, S.-S., Pyo, J., Pachepsky, Y., Park, J., Cho, K.H., 2019. Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration. J. Membr. Sci. 587, 117164.
https://doi.org/10.1016/j.memsci.2019.06.004
Peleato, N.M., Legge, R.L., Andrews, R.C., 2017. Continuous Organic Characterization for Biological and Membrane Filter Performance Monitoring. J. American Water Works Assoc. 109, E86–E98.
https://doi.org/10.5942/jawwa.2017.109.0031
Pham, T.D., Vu, T.N., Nguyen, H.L., Le, P.H. P., Hoang, T.S., 2020. Adsorptive Removal of Antibiotic Ciprofloxacin from Aqueous Solution Using Protein-Modified Nanosilica. Polymers 12(1), 57.
https://doi.org/10.3390/polym12010057
Colston, R., Tait, S., Vaneeckhaute, C., Cruz, H., Pikaar, I., Seviour, T., Klok, J.B.M., Weijma, J., Dijkman, H., Buisman, C.J.N., Scattergood, S., Robles-Aguilar, A.A., Meers, E., Béline, F., Soares, A., Nutrient recovery from water and wastewater. Chapter 10, Resour. Recover. from Water Princ. Appl., (I. Pikaar, J. Guest, R. Ganigué, P. Jensen, K. Rabaey, T. Seviour, J. Trimmer, O. van der Kolk, C. Vaneeckhaute, W. Verstraete, Eds.) IWA Publishing; 245-293, 2022.
https://doi.org/10.2166/9781780409566_0245
Qalyoubi, L., Al-Othman, A., Al-Asheh, S., 2021. Recent progress and challenges on adsorptive membranes for the removal of pollutants from wastewater. Part I: Fundamentals and classification of membranes. Case Studies Chem. Environ. Eng. 3, 100086.
https://doi.org/10.1016/j.cscee.2021.100086
Qalyoubi, L., Al-Othman, A., Al-Asheh, S., 2022. Removal of ciprofloxacin antibiotic pollutants from wastewater using nano-composite adsorptive membranes. Environ. Res.
215, 114182.
https://doi.org/10.1016/j.envres.2022.114182
Qasim, M., Badrelzaman, M., Darwish, N.N., Darwish, N.A., Hilal, N., 2019. Reverse osmosis desalination: A state-of-the-art review. Desalination 459, 59–104.
Qiu, G., Law, Y.-M., Das, S., Ting, Y.-P., 2015. Direct and Complete Phosphorus Recovery from Municipal Wastewater Using a Hybrid Microfiltration-Forward Osmosis Membrane Bioreactor Process with Seawater Brine as Draw Solution. Environ. Sci. Tech. 49(10), 6156–6163.
https://doi.org/10.1021/es504554f
Roehl, E.A., Ladner, D.A., Daamen, R.C., Cook, J.B., Safarik, J., Phipps, D.W., Xie, P., 2018. Modeling fouling in a large RO system with artificial neural networks. J. Membr. Sci. 552, 95–106.
https://doi.org/10.1016/j.memsci.2018.01.064
Sarkar, B., Mandal, S., Tsang, Y.F., Vithanage, M., Biswas, J.K., Yi, H., Dou, X., Ok, Y.S., 2019. Sustainable sludge management by removing emerging contaminants from urban wastewater using carbon nanotubes. in: Ind. Municipal Sludge (pp. 553–571). Elsevier.
https://doi.org/10.1016/B978-0-12-815907-1.00024-6
Schwaller, C., Hoffmann, G., Hiller, C.X., Helmreich, B., Drewes, J.E., 2021. Inline dosing of powdered activated carbon and coagulant prior to ultrafiltration at pilot-scale – Effects on trace organic chemical removal and operational stability. Chem. Eng. J. 414, 128801.
https://doi.org/10.1016/j.cej.2021.128801
Shams Jalbani, N., Solangi, A.R., Memon, S., Junejo, R., Ali Bhatti, A., Lütfi Yola, M., Tawalbeh, M., Karimi-Maleh, H., 2021. Synthesis of new functionalized Calix[4]arene modified silica resin for the adsorption of metal ions: Equilibrium, thermodynamic and kinetic modeling studies. J. Molecular Liquids, 339, 116741.
https://doi.org/https://doi.org/10.1016/j.molliq.2021.116741
Sheng, A.L.K., Bilad, M.R., Osman, N.B., Arahman, N., 2017. Sequencing batch membrane photobioreactor for real secondary effluent polishing using native microalgae: Process performance and full-scale projection. J. Cleaner Produc. 168, 708–715.
https://doi.org/10.1016/j.jclepro.2017.09.083
Tawalbeh, M., Qalyoubi, L., Al-Othman, A., Qasim, M., Shirazi, M., 2023. Insights on the development of enhanced antifouling reverse osmosis membranes: Industrial applications and challenges. Desalination 553, 116460.
https://doi.org/10.1016/j.desal.2023.116460
Toczyłowska-Mamińska, R., Mamiński, M.Ł., 2022. Wastewater as a Renewable Energy Source—Utilisation of Microbial Fuel Cell Technology. Energies 15(19), 6928.
https://doi.org/10.3390/en15196928
Viet, N.D., Jang, A., 2023. Comparative mathematical and data-driven models for simulating the performance of forward osmosis membrane under different draw solutions. Desalination 549, 116346.
https://doi.org/10.1016/j.desal.2022.116346
Wang, X., Li, B., Zhang, T., Li, X., 2015. Performance of nanofiltration membrane in rejecting trace organic compounds: Experiment and model prediction. Desalination 370, 7–16.
https://doi.org/10.1016/j.desal.2015.05.010
Ye, Y., Ngo, H.H., Guo, W., Chang, S.W., Nguyen, D.D., Zhang, X., Zhang, J., Liang, S., 2020. Nutrient recovery from wastewater: From technology to economy. Bioresource Technol. Reports, 11, 100425.
https://doi.org/10.1016/j.biteb.2020.100425
Yousefi, M., Gholami, M., Oskoei, V., Mohammadi, A.A., Baziar, M., Esrafili, A., 2021. Comparison of LSSVM and RSM in simulating the removal of ciprofloxacin from aqueous solutions using magnetization of functionalized multi-walled carbon nanotubes: Process optimization using GA and RSM techniques. J. Environ. Chem. Eng. 9(4), 105677.
https://doi.org/10.1016/j.jece.2021.105677
Zahid, M., Rashid, A., Akram, S., Rehan, Z.A., & Razzaq, W., 2018. A Comprehensive Review on Polymeric Nano-Composite Membranes for Water Treatment. J. Membr. Sci. Tech. 08(01).
https://doi.org/10.4172/2155-9589.1000179
Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., Yang, Y., 2020. Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Proc. Safety Environ. Protec. 133, 169–182.
https://doi.org/10.1016/j.psep.2019.11.014