Automated machine learning-based reverse osmosis membrane flux prediction and chemical dosage dynamic adjustment

Abstract

The complexity of influent water quality and the diversity of predictive models pose significant challenges to the efficient identification of optimal membrane flux prediction models. This study proposes an integrated decision tree approach combined with AutoML to classify influent types simultaneously (with an accuracy > 96.08%) and rapidly screen the optimal model from 2400 candidates. By classifying the influent types under typical conditions such as groundwater, reservoir water, reclaimed water, and recycled water, the model optimization efficiencies increased by 43.59%, 52.35%, 51.52%, and 48.05%, respectively, compared with that of the unclassified treatment. The optimal model screened by AutoML demonstrated excellent predictive performance across varying influent parameter fluctuations (R² > 94.5%, RMSE < 0.061), with a reduction in optimization iterations of up to 60.48%. Parameter importance analysis revealed that accurate matching between influent parameter variations and model characteristics is key to achieving high prediction accuracy. Furthermore, the selected optimal models enabled accurate prediction of the optimal scale of inhibitor dosage under different influent conditions, providing theoretical and technical support for the scientific application of membrane treatment chemicals.

Graphical abstract

Keywords

Automated machine learning / Optimal model / Influent classification / Model fast screening / Scale inhibitor

Highlight

	● An AutoML-based method for fast screening of flux prediction models is proposed.
	● Accurate matching of predictive models and influent types is achieved.
	● Model screening achieved a 60.4% reduction in the number of iterations.
	● An AutoML-driven dosing strategy for membrane agent management is established.

References

[1] Ammi Y , Khaouane L , Hanini S . (2015). Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks. Korean Journal of Chemical Engineering, 32(11): 2300–2310

[2] Anis S F , Hashaikeh R , Hilal N . (2019). Reverse osmosis pretreatment technologies and future trends: a comprehensive review. Desalination, 452: 159–195

[3] Bagheri M , Akbari A , Mirbagheri S A . (2019). Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: a critical review. Process Safety and Environmental Protection, 123: 229–252

[4] Cheng Z Y , Meng X , Wang Y T , Kong F G , Jia H , Wang J . (2024). Insights of membrane fouling under scale inhibitor synergistic condition monitored by ultrasonic phased array: fouling spatial and density characteristics. Journal of Membrane Science, 699: 122670

[5] Cui S X , Gao Y C , Huang Y Z , Shen L L , Zhao Q M , Pan Y R , Zhuang S L . (2023). Advances and applications of machine learning and deep learning in environmental ecology and health. Environmental Pollution, 335: 122358

[6] Gil J D , Ruiz-Aguirre A , Roca L , Zaragoza G , Berenguel M . (2018). Prediction models to analyse the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants. Desalination, 445: 15–28

[7] Hosseinzadeh A , Altaee A , Ibrar I , Zhou J L . (2025). Modeling and optimization of reverse salt diffusion and water flux in forward osmosis by response surface methodology and artificial neural network. Chemical Engineering and Processing-Process Inten-sification, 208: 110140

[8] Huang Y P , Wang C , Wang Y H , Lyu G F , Lin S J , Liu W J , Niu H B , Hu Q . (2024). Application of machine learning models in groundwater quality assessment and prediction: progress and challenges. Frontiers of Environmental Science & Engineering, 18(3): 29

[9] Ignacz G , Bader L , Beke A K , Ghunaim Y , Shastry T , Vovusha H , Carbone M R , Ghanem B , Szekely G . (2025). Machine learning for the advancement of membrane science and technology: a critical review. Journal of Membrane Science, 713: 123256

[10] Jordan M I , Mitchell T M . (2015). Machine learning: trends, perspectives, and prospects. Science, 349(6245): 255–260

[11] Karmaker S K , Hassan M M , Smith M J , Xu L , Zhai C X , Veeramachaneni K . (2021). AutoML to date and beyond: challenges and opportunities. ACM Computing Surveys, 54(8): 175

[12] Lai Y Z , Xiao K , He Y F , Liu X , Tan J H , Xue W C , Zhang A Q , Huang X . (2025). Machine learning for membrane bioreactor research: principles, methods, applications, and a tutorial. Frontiers of Environmental Science & Engineering, 19(3): 34

[13] Lee H J , Halali M A , Baker T , Sarathy S , de Lannoy C F . (2020). A comparative study of RO membrane scale inhibitors in wastewater reclamation: antiscalants versus pH adjustment. Separation and Purification Technology, 240: 116549

[14] Maher Y A , Ali M E A , Salama H E , Sabaa M W . (2020). Preparation, characterization and evaluation of chitosan biguanidine hydrochloride as a novel antiscalant during membrane desalination process. Arabian Journal of Chemistry, 13(1): 2964–2981

[15] Miao S , Zhou C L , AlQahtani S A , Alrashoud M , Ghoneim A , Lv Z H . (2021). Applying machine learning in intelligent sewage treatment: a case study of chemical plant in sustainable cities. Sustainable Cities and Society, 72: 103009

[16] Mousavi S Z , Momeni S A , Dehdashti B , Dehdashti D , Fatehizadeh A , Amin M M . (2022). Advanced treatment of industrial estate effluent using ozone-membrane processes based on optimized random forest model. Journal of Water Process Engineering, 48: 102897

[17] Nagarajah TPoravi G (2019) A review on automated machine learning (AutoML) systems. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). Bombay: IEEE, 1–6

[18] Niu C X , Li X S , Dai R B , Wang Z W . (2022). Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: a critical review. Water Research, 216: 118299

[19] Osman A I , Nasr M , Farghali M , Bakr S S , Eltaweil A S , Rashwan A K , Abd El-Monaem E M . (2024). Machine learning for membrane design in energy production, gas separation, and water treatment: a review. Environmental Chemistry Letters, 22(2): 505–560

[20] Philibert M , Villacorte L O , Ekowati Y , Abushaban A , Salinas-Rodriguez S G . (2024). Fouling and scaling in reverse osmosis desalination plants: a critical review of membrane autopsies, feedwater quality guidelines and assessment methods. Desalination, 592: 118188

[21] Samaraweera S A P T , Najim M M M , Alhafi Alotaibi B , Traore A . (2024). Impacts of a partially connected wastewater treatment plant on the water quality of stormwater drains used as an irrigation source. Frontiers in Environmental Science, 12: 1412717

[22] Schroeder D W , Guo W X , Missimer T M . (2021). Groundwater quality change impacts on a brackish-water reverse osmosis water treatment plant design: the City of Clearwater, Florida. Desalination and Water Treatment, 211: 31–44

[23] Song M J , Choi S , Bae W B , Lee J , Han H , Kim D D , Kwon M , Myung J , Kim Y M , Yoon S . (2020). Identification of primary effecters of N₂O emissions from full-scale biological nitrogen removal systems using random forest approach. Water Research, 184: 116144

[24] Sun Y P , Clarke B , Clarke J , Li X . (2021). Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning. Water Research, 202: 117384

[25] Sweity A , Oren Y , Ronen Z , Herzberg M . (2013). The influence of antiscalants on biofouling of RO membranes in seawater desalination. Water Research, 47(10): 3389–3398

[26] Sweity A , Ronen Z , Herzberg M . (2014). Induced organic fouling with antiscalants in seawater desalination. Desalination, 352: 158–165

[27] Tang Y C , Wu Y H , Tong X , Bai Y , Wang W L , Chen Z , Xu A , Ikuno N , Koji N , Hu H Y . (2024). Fouling characteristics and flux prediction model of reverse osmosis membrane based on hydrophobic fractions in reclaimed water. Separation and Purification Technology, 335: 126187

[28] Tang Y L , Zhang S R . (2021). Fault detection of FWTPs in coal-fired power plants using K-WD-KPCA in consideration of multiple operation conditions. Asia-Pacific Journal of Chemical Engineering, 16(2): e2599

[29] Tayyebi A , Alshami A S , Tayyebi E , Buelke C , Talukder M J , Ismail N , Al-Goraee A , Rabiei X . (2024). Machine learning–Driven surface grafting of thin-film composite reverse osmosis (TFC-RO) membrane. Desalination, 579: 117502

[30] Tong T Z , Wallace A F , Zhao S , Wang Z . (2019). Mineral scaling in membrane desalination: mechanisms, mitigation strategies, and feasibility of scaling-resistant membranes. Journal of Membrane Science, 579: 52–69

[31] Yeo C S H , Xie Q , Wang X N , Zhang S . (2020). Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning. Journal of Membrane Science, 606: 118135

[32] Yu W , Song D , Chen W , Yang H . (2020). Antiscalants in RO membrane scaling control. Water Research, 183: 115985

[33] Yu W , Song D , Li A M , Yang H . (2019). Control of gypsum-dominated scaling in reverse osmosis system using carbo-xymethyl cellulose. Journal of Membrane Science, 577: 20–30

[34] Yu Y , Jia H , Gao F , Zhu H F , Zhang L , Wang J . (2024). Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios. Water Research, 257: 121714

[35] Zhang B B , Hu X , Li B , Wu P , Cai X T , Luo Y , Deng X Z , Jiang M M . (2024). A groundwater quality assessment model for water quality index: combining principal component analysis, entropy weight method, and coefficient of variation method for dimensionality reduction and weight optimization, and its application. Water Environment Research, 96(12): e11155

[36] Zhang X Y , Lin Z L , Wu D J , Xie M , Xu J T , Bao X , Cheng X X , Wang Y , Chen F Y . (2023). Covalent codeposition modification of reverse osmosis membranes with Noria and zwitterionic copolymers for antifouling in reclaimed water production. Desalination, 567: 116973

[37] Zhou Y , Marki N , Khan B , Aguilar C , Jarma Y , Cohen Y . (2024). Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal. Desalination, 588: 117951

[38] Zhuang S T , Wang J L . (2024). Cesium removal from radioactive wastewater by adsorption and membrane technology. Frontiers of Environmental Science & Engineering, 18(3): 38

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