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<p>Lithium extraction from geothermal brines is challenging owing to the presence of competing cations—particularly sodium, potassium, and calcium—and the elevated temperatures involved. Ion-exchange resins provide a sustainable solution for the selective removal of these competing species; however, the selectivity and performance of commercial resins under geothermal conditions remain insufficiently understood. In this study, a weakly acidic cation exchange resin, Lewatit<sup>®</sup> MDS TP 208 (LIXR), was evaluated for its preferential adsorption of calcium over lithium in a synthetic geothermal brine, achieving a capacity exceeding 40&#xa0;mg&#xa0;g<sup>−1</sup>. Fixed-bed adsorption experiments were optimized using a central composite design (CCD) incorporating four operational factors: temperature, flow rate, initial lithium concentration, and bed height. Dynamic breakthrough behavior was analyzed using the Adams–Bohart, Yan, Yoon–Nelson, and Thomas models, with the Thomas model showing the best correlation (<i>R</i><sup>2</sup> ≈ 0.999). In parallel, machine learning (ML) models were developed to predict calcium adsorption capacity and lithium retention time prior to desorption. Shapley additive explanations (SHAP) analysis identified initial lithium concentration as the most influential factor, consistent with response surface methodology (RSM) outcomes. These integrated experimental and predictive insights demonstrate the strong potential of LIXR for enhanced calcium–lithium separation, offering a scalable framework for refining geothermal brines and enhancing the efficiency of downstream lithium recovery.</p> Graphical Abstract <p></p>

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Selective Separation of Calcium from Lithium in Synthetic Geothermal Brine Using Ion Exchange: Breakthrough Curve Analysis and Machine Learning Modeling Insights

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Mohamed Alghazali Musa,
  • Ariyo Suharyanto,
  • Latifa Hanum Lalasari,
  • Zela Tanlega Ichlas,
  • Mohammad Zaki Mubarok

摘要

Lithium extraction from geothermal brines is challenging owing to the presence of competing cations—particularly sodium, potassium, and calcium—and the elevated temperatures involved. Ion-exchange resins provide a sustainable solution for the selective removal of these competing species; however, the selectivity and performance of commercial resins under geothermal conditions remain insufficiently understood. In this study, a weakly acidic cation exchange resin, Lewatit® MDS TP 208 (LIXR), was evaluated for its preferential adsorption of calcium over lithium in a synthetic geothermal brine, achieving a capacity exceeding 40 mg g−1. Fixed-bed adsorption experiments were optimized using a central composite design (CCD) incorporating four operational factors: temperature, flow rate, initial lithium concentration, and bed height. Dynamic breakthrough behavior was analyzed using the Adams–Bohart, Yan, Yoon–Nelson, and Thomas models, with the Thomas model showing the best correlation (R2 ≈ 0.999). In parallel, machine learning (ML) models were developed to predict calcium adsorption capacity and lithium retention time prior to desorption. Shapley additive explanations (SHAP) analysis identified initial lithium concentration as the most influential factor, consistent with response surface methodology (RSM) outcomes. These integrated experimental and predictive insights demonstrate the strong potential of LIXR for enhanced calcium–lithium separation, offering a scalable framework for refining geothermal brines and enhancing the efficiency of downstream lithium recovery.

Graphical Abstract