Deep learning for pH prediction in water desalination using membrane capacitive deionization

Son, Moon; Yoon, Nakyung; Jeong, Kwanho; Abass, Ather; Logan, Bruce E.

doi:10.1016/j.desal.2021.115233

Cited by 39 publications

(6 citation statements)

References 37 publications

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“…Traditional approaches to electrode optimization often involve extensive experimental trials, which can be timeconsuming, resource-intensive, and limited in scope. Integrating Artificial Intelligence (AI) [21][22][23][24][25][26][27][28] and machine learning [27][28][29][30][31][32][33][34] techniques in many technologies, especially in this contrast provides a powerful and efficient means to explore the vast design space of CDI electrodes, predict optimal configurations, and accelerate the development of high-performance materials [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. This research uses AI-based modeling methodologies to investigate and optimize CDI electrode's key features systematically.…”

Section: Introductionmentioning

confidence: 99%

Optimization of capacitive deionization electrode features and materials using artificial intelligence-based modeling

Khalil,

AlShabi,

Abdelrazek

et al. 2024

Energy Harvesting and Storage: Materials, Devices, and Applications XIV

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Capacitive deionization (CDI) is an emerging technique for removing dissolved, charged species from aqueous solutions. It has been previously applied to brackish water and seawater desalination, wastewater remediation, and water softening. The CDI unit cell comprises two parallel electrode sheets separated by a non-conductive spacer (nylon cloth, 100 mm thick) and fixed with a rubber gasket. The electrodes are typically carbon, and the feed water flows between or through the two charged electrodes. The porous electrode pair is accused of an applied voltage difference (called the cell or charging voltage). Optimizing the CDI electrode features is essential for scaling up the technique to an industrial scale. The effect of the water flow rate and the applied voltage are key factors that affect the efficiency of the CDI units. This research used Artificial Intelligence (AI) as a smart-based modeling tool to optimize and predict the highest efficiency concerning the electrode and process parameters. The results showed that a carbon-based structure with super-electrochemical and mechanical properties could revolutionize CDI technology.

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Section: Introductionmentioning

confidence: 99%

Optimization of capacitive deionization electrode features and materials using artificial intelligence-based modeling

Khalil,

AlShabi,

Abdelrazek

et al. 2024

Energy Harvesting and Storage: Materials, Devices, and Applications XIV

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“…As a result, several researchers developed a high-performance model and performed a sensitivity analysis to unveil the impact of current on CDI performance. 42,43 Furthermore, a machine learning-based long-term system optimization strategy for MCDI has also been proposed. 44 However, since the FCDI technology employs a flowelectrode, it needs a distinct optimization approach compared to CDI and MCDI, which use fixed electrodes.…”

Section: ■ Introductionmentioning

confidence: 99%

Optimal Management Strategy for Salt Adsorption Capacity in Machine Learning-Based Flow-Electrode Capacitive Deionization Process

Yu,

Jeon,

Shin

et al. 2024

ACS EST Eng.

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Flow-electrode capacitive deionization (FCDI) has created a breakthrough toward a more stable desalination performance by adopting a flow-electrode compared to existing capacitive deionization and membrane capacitive deionization as a promising electrochemical water treatment technology. However, the FCDI technology requires investigation of various mechanisms pertaining to flow-electrode materials to achieve system optimization. Further, studies on applying machine learning to the FCDI technology have been scarcely reported. Our study aims to explore optimal algorithms via machine learning for predicting the salt adsorption capacity of FCDI processes and evaluate the feasibility of optimization applications. Concurrently, a comparative analysis was conducted through the performance model indicators of mean absolute error (MAE), mean squared error, and R 2 for support vector machine, random forest, and artificial neural network (ANN) algorithms. Herein, we demonstrated that the optimal ANNbased model exhibited the highest predictive performance, achieving R 2 and MAE values of 0.996 and 0.21 mg/g, respectively. Additionally, the Shapley additive explanations (SHAP) confirmed a trend in the contribution of influent concentration, aligning closely with the results of statistical analysis. Specifically, the change in voltage of the FCDI process serves as a key factor in determining salt adsorption efficiency. Moreover, a parallel comparison of the Pearson correlation coefficient and SHAP analyses suggests that the impact of voltage entails a nonlinear contribution within the realm of machine learning. Finally, to deploy a machine learning-driven ANN model system, we present multiple factors (e.g., weight of flow-electrodes, influent concentration, and voltages) as a reinforcement learning model for decision-making. This offers valuable insights and guidance for future operations of the FCDI process.

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“…The application of deep learning and data-intelligent models has significantly reduced the cost of monitoring and assessment of water quality. Studies conducted include the prediction of pH in water (Egbueri & Agbasi, 2022b ; Huang et al, 2019 ; Son et al, 2021 ; Stackelberg et al, 2020 ), prediction of TDS in water (Egbueri & Agbasi, 2022b ; Jamei et al, 2020 ; Mehrdadi et al, 2012 ; Salmani & Jajaei, 2016 ), prediction of TH in water (Azad et al, 2018 ; Egbueri & Agbasi, 2022b ; Roy & Majumder, 2018 ), prediction of anions in water (Egbueri, 2021 ; Mousavi & Amiri, 2012 ; Wagh et al, 2017b ; Yesilnacar et al, 2008 ; Zare et al, 2011 ), prediction of cations in water (Aghel et al, 2019 ; Bondarev, 2019 ; Katimon et al, 2018 ; Nhantumbo et al, 2018 ; Subba Rao et al, 2022b ), prediction of metals in water (Alizamir & Sobhanardakani, 2017a , 2017b ; Egbueri, 2021 ; Fard et al, 2017 ; Ozel et al, 2020 ; Rooki et al, 2011 ), and prediction of water quality indices (Chia et al, 2022 ; Egbueri, 2022a , 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Agbasi

Egbueri

2023

J. Sediment. Environ.

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Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria, works that have used data-driven algorithms in predicting PTEs in groundwater are scarce. In addition, only a few works have simulated water quality indices using machine learning modelling methods in the region. Therefore, in this study, physicochemical analyses were carried out on groundwater samples in southeastern Nigeria. The laboratory results were used to compute two water quality indices: pollution index of groundwater (PIG) and the water pollution index (WPI), to ascertain groundwater quality. In addition, the physicochemical parameters served as input variables for multiple linear regression (MLR) and artificial neural network (ANN) modelling and prediction of Cr, Fe, Ni, NO 3 − , Pb, Zn, WPI, and PIG. The results of WPI and PIG computation showed that about 30–35% of the groundwater samples were unsuitable for human consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated strong positive prediction accuracies ( R 2 = 0.856–1.000) with low modeling errors. The predictive MLR and ANN models of the PIG and WPI generally outperformed those of the PTEs. The models produced in this study predicted the PTEs better compared to previous studies. Thus, this work provides insights into effective water sustainability, management, and protection.

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Deep learning for pH prediction in water desalination using membrane capacitive deionization

Cited by 39 publications

References 37 publications

Optimization of capacitive deionization electrode features and materials using artificial intelligence-based modeling

Optimization of capacitive deionization electrode features and materials using artificial intelligence-based modeling

Optimal Management Strategy for Salt Adsorption Capacity in Machine Learning-Based Flow-Electrode Capacitive Deionization Process

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

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