Machine learning assisted adsorption performance evaluation of biochar on heavy metal

doi:10.1007/s11783-024-1815-4

Front. Environ. Sci. Eng.

2024, Vol. 18

Issue (5) : 55 https://doi.org/10.1007/s11783-024-1815-4

Machine learning assisted adsorption performance evaluation of biochar on heavy metal

Qiannan Duan¹, Pengwei Yan¹, Yichen Feng¹, Qianru Wan², Xiaoli Zhu¹(

)

¹. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
². Department of Environment Science, Shaanxi Normal University, Xi’an 710062, China

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Abstract

● A machine learning path for predicting biochar adsorption efficiency was constructed.

● Stacking model has exhibited better prediction accuracy and generalization ability.

● The proposed method could be used to optimize the preparation conditions of biochars.

Heavy metals (HMs) represent pervasive and highly toxic environmental pollutants, known for their long latency periods and high toxicity levels, which pose significant challenges for their removal and degradation. Therefore, the removal of heavy metals from the environment is crucial to ensure the water safety. Biochar materials, known for their intricate pore structures and abundant oxygen-containing functional groups, are frequently harnessed for their effectiveness in mitigating heavy metal contamination. However, conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious. To address this challenge, this paper proposes a data-driven machine learning (ML) approach to identify the optimal biochar preparation and adsorption reaction conditions, with the ultimate goal of maximizing their adsorption capacity. By utilizing a data set comprising 476 instances of heavy metal absorption by biochar, seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar. These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions. The results demonstrate that the stacking model, which integrates multiple algorithms, allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.

Keywords Machine learning Biochar Heavy metal Adsorption efficiency

Corresponding Author(s): Xiaoli Zhu

Issue Date: 18 January 2024

Cite this article:

Qiannan Duan,Pengwei Yan,Yichen Feng, et al. Machine learning assisted adsorption performance evaluation of biochar on heavy metal[J]. Front. Environ. Sci. Eng., 2024, 18(5): 55.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1815-4
https://academic.hep.com.cn/fese/EN/Y2024/V18/I5/55

Fig.1 Schematic representation of working flow that machine learning models are used to predict the adsorption efficiency of biocarbon materials on heavy metal. (a) A test data mining process for data collection. (b) Seven ML regression algorithms (LR, KNN, SVR, RF, DT, GBDT and XGBoost) and their stacking algorithm for model building, and a k-fold cross-validation strategy was adopted to optimize the hyperparameters. (c) Model evaluation using a series of biochar adsorption experiments.

Tab.1 Physicochemical properties of the biochar samples

Parameter	Unit
$pH H 2 O$ ^a)	–
C	%
(O + N)/C	–
O/C	–
H/C	–
Ash	%
SA	m²/g
CEC	cmol/kg
T ^b)	°C
pH_sol ^c)	–
C₀	mmol/L
HM_x ^d)	–
AE	–

Tab.2 Physicochemical and adsorption reaction parameters of the biochar for model building

Fig.2 Machine learning process of the stacking model.

Fig.3 SEM images (a) and corresponding EDS spectra (b) of biochars derived from the YM, the BS and their mixtures (YM:BS = 1:1) at 500 °C.

Fig.4 FT-IR spectra of the biochar at different calcination temperatures. (a) Raw biochars at 400 °C; (b) mixed biochars at 400 °C; (c) raw biochars at 500 °C; (d) mixed biochars at 500 °C.

Fig.5 The influence of three single-variable control experiments on the Cd²⁺ removal percentage and the adsorbed amount of Cd²⁺ per weight unit of biochar. The removal percentages and the adsorbed amount of the biochar at different (a) Initial solution pH, (b) Solid to liquid ratio, and (c) Initial Cd²⁺ concentrations.

Fig.6 Statistics and analysis of the built data set. (a) Boxplots indicating the distribution of the physicochemical features for 476 biochar samples. (b) A SCC matrix illustrating the correlations for the 12-dimensional variables and adsorption efficiency.

Fig.7 (a) Scatter plots between predicted and actual values on the test set. The data set was divided into training set and test data by an 80/20 split, and each model’s generalizability was measured by a 5-fold cross-validation. Evaluation parameters: R², coefficient of determination; RMSE, root mean square error; dashed line, y = x line. (b) Accuracy scores of all models.

Fig.8 Prediction results of stacking model on experimental data.

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