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Artificial intelligence models for predicting calcium and magnesium removal by polyfunctional ketone using ensemble

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  • 1Department of Environmental Engineering, Kumoh National Institute of Technology, Daehakro 61, Gumi Gyeongbuk 39177, South Korea.

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Summary

Functional polyketones (FPKs) effectively remove calcium and magnesium ions from reverse osmosis concentrate. Artificial intelligence models accurately predict ion removal efficiency, aiding zero-liquid discharge system optimization.

Keywords:
Decision treeExtreme gradient boostPolyfunctional ketonesRandom forestReverse osmosis concentrateScaling ions

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Area of Science:

  • Environmental Science
  • Materials Science
  • Chemical Engineering

Background:

  • Calcium (Ca2+) and magnesium (Mg2+) ions are primary scaling contributors in reverse osmosis (RO) concentrate, hindering zero-liquid discharge (ZLD) system performance.
  • Effective removal of these divalent cations is crucial for maintaining ZLD system efficiency and preventing scaling.

Purpose of the Study:

  • To predict the removal efficiency of Ca2+ and Mg2+ from simulated RO concentrate using functional polyketones (FPKs).
  • To optimize the ion removal process by investigating factors like adsorbent dosage, feed concentration, and pH.
  • To evaluate the predictive capabilities of artificial intelligence (AI) models, including decision tree (DT), extreme gradient boost (XGB), and random forest (RF), for ion removal.

Main Methods:

  • Synthesis of FPKs using four different amines: 1,2-diaminopropane (DAP), 1-(2-aminoethyl) piperazine (AEP), 1-(3-aminopropyl) imidazole (API), and butyl amine (BA).
  • Experimental determination of Ca2+ and Mg2+ removal by FPKs from simulated RO concentrate.
  • Development and application of DT, XGB, and RF AI models to predict ion removal based on experimental data.

Main Results:

  • AI models, particularly XGB, demonstrated high accuracy in predicting Ca2+ and Mg2+ removal by FPKs.
  • Higher coefficients of determination (R2 values ranging from 0.841-0.935) were observed for Mg2+ removal using AEP and DAP-based FPKs compared to API and BA.
  • The XGB model showed robust performance for both Ca2+ and Mg2+ removal, with minor variations noted for AEP and BA predictions by DT and RF models.

Conclusions:

  • AI models offer a viable and efficient alternative for predicting Ca2+ and Mg2+ removal by FPKs.
  • FPKs synthesized with specific amines show promising potential for mitigating scaling in RO concentrate within ZLD systems.
  • The study highlights the utility of AI in optimizing water treatment processes and understanding complex interactions in ion removal.