Modified biochar for the adsorption of roxarsone and arsenic Ions in water

Abstract The research findings show that the maximum adsorption capacity of arsenic ions and roxarsone significantly increases under optimized reaction conditions (temperature 30°C, pH value 6, adsorbent dosage 0.6 g/L). The degree and direction of influence of different reaction conditions on the removal rate also vary[4]. The combination model of multiple linear regression and random forest regression shows good fitting between the predicted results and actual data, indicating that the model can accurately reflect the influence of adsorption conditions on the removal rate and provide support for the reliability of the experimental results. Based on the experimental data obtained under the optimal reaction conditions, these findings not only verify the high efficiency of modified biological carbon in removing arsenic under specific conditions[5], but also provide theoretical basis and data support for designing more efficient removal schemes and optimizing process parameters in the future.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2025-02-01
DOI
https://doi.org/10.1088/1742-6596/2964/1/012078
Primary Topic
Electrochemical sensors and biosensors
Type
article
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article

Modified biochar for the adsorption of roxarsone and arsenic Ions in water

Yuming Dong, Linmin Zhang, Shixiong Jiang, Siyu Zhang
Journal of Physics Conference Series
Electrochemical sensors and biosensors
article

Modified biochar for the adsorption of roxarsone and arsenic Ions in water

Yuming Dong, Linmin Zhang, Shixiong Jiang, Siyu Zhang
article en

Abstract

Abstract The research findings show that the maximum adsorption capacity of arsenic ions and roxarsone significantly increases under optimized reaction conditions (temperature 30°C, pH value 6, adsorbent dosage 0.6 g/L). The degree and direction of influence of different reaction conditions on the removal rate also vary[4]. The combination model of multiple linear regression and random forest regression shows good fitting between the predicted results and actual data, indicating that the model can accurately reflect the influence of adsorption conditions on the removal rate and provide support for the reliability of the experimental results. Based on the experimental data obtained under the optimal reaction conditions, these findings not only verify the high efficiency of modified biological carbon in removing arsenic under specific conditions[5], but also provide theoretical basis and data support for designing more efficient removal schemes and optimizing process parameters in the future.

Journal of Physics Conference SeriesVol. 2964(1)
Dalian Polytechnic University (CN)
Openalex Percentile: Top 97%
Electrochemical sensors and biosensors
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