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Elucidating the black-box nature of data-driven models in the adsorption of reactive red M-2BE on activated carbon and multi-walled carbon nanotubes through SHapley Additive exPlanations

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Abstract

The removal of reactive red M-2BE dye textile from aqueous solution was performed using multi-walled carbon nanotubes (MWCN) and powdered activated carbon (PAC). Kinetic adsorption modeling has been performed using machine learning (ML) algorithms of artificial neural networks, adaptive-neuro fuzzy inference system (ANFIS), random forest, gradient boosting, and support vector machine. Although ML models are more accurate, they often fail to interpret the reasoning behind predictions. Therefore, the SHapley Additive exPlanations (SHAP) were used to understand the effect of each feature on the adsorption capacity. The ANFIS has presented the best statistical metrics with \(R=0.9993\), \(RMSE=0.0214\), and \(SAE=7.1172\). A higher adsorption capacity was observed for MWCN compared to PAC; while the first peaked at 300 mg L−1, the second approached 230 mg L−1. Temperature was found to have the smallest contribution in describing adsorption capacity. This novel application of ML with SHAP can provide important insights for adsorption researchers.

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Funding

The authors acknowledge the financial assistance from CAPES, CNPq, and FAPERGS. The authors also acknowledge the financial support from ANP-PRH 52.1.

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  1. Chemical Engineering Department, Universidade Federal de Santa Maria, Av. Roraima, 1000, 97105-900, Santa Maria, RS, Brazil

    Henrique Gasparetto, Guilherme Luiz Dotto & Nina Paula Gonçalves Salau

  2. Institute of Chemistry, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, 91501-970, Porto Alegre, RS, Brazil

    Éder Claudio Lima

  3. Technology Development Center, Universidade Federal de Pelotas, Rua Gomes Carneiro, 1, 96010-610, Pelotas, RS, Brazil

    Fernando Machado Machado

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  1. Henrique Gasparetto
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HG: conceptualization, methodology, software, validation, formal analysis, writing—original draft. NPGS: conceptualization, resources, writing—review and editing, supervision, funding acquisition. FMM: investigation, writing—review and editing. ÉCL: resources, supervision, writing—review and editing. GLD: resources, supervision, writing—review and editing.

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Gasparetto, H., Lima, É.C., Machado, F.M. et al. Elucidating the black-box nature of data-driven models in the adsorption of reactive red M-2BE on activated carbon and multi-walled carbon nanotubes through SHapley Additive exPlanations. Adsorption (2023). https://doi.org/10.1007/s10450-023-00420-z

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