Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite.

As a natural adsorbent material, bentonite is widely used in the field of heavy metal adsorption. The heavy metal adsorption capacity of bentonite varies significantly in studies due to the differences in the properties of bentonite, solution, and heavy metal. To achieve accurate predictions of bentonite's heavy metal adsorption capacity, this study employed six machine learning (ML) regression algorithms to investigate the adsorption characteristics of bentonite. Finally, an eXtreme Gradient Boosting Regression (XGB) model with outstanding predictive performance was constructed. Explanation analysis of the XGB model further reveal the importance and influence manner of each input feature in predicting the heavy metal adsorption capacity of bentonite. The feature categories influencing heavy metal adsorption capacity were ranked in order of importance as adsorption conditions > bentonite properties > heavy metal properties. Furthermore, a web-based graphical user interface (GUI) software was developed, facilitating researchers and engineers to conveniently use the XGB model for predicting the heavy metal adsorption capacity of bentonite. This study provides new insights into the adsorption behaviors of bentonite for heavy metals, offering guidance and support for enhancing its application efficiency and addressing heavy metal pollution remediation.

Machine learning-based prediction of heavy metal immobilization rate in the solidification/stabilization of municipal solid waste incineration fly ash (MSWIFA) by geopolymers.

Geopolymer is an environmentally friendly solidification/stabilization (S/S) binder, exhibiting significant potential for immobilizing heavy metals in municipal solid waste incineration fly ash (MSWIFA). However, due to the diversity in geopolymer raw materials and heavy metal properties, predicting the heavy metal immobilization rate proves to be challenging. In order to enhance the application of geopolymers in immobilizing heavy metals in MSWIFA, a universal method is required to predict the heavy metal immobilization rate. Therefore, this study employs machine learning to predict the heavy metal immobilization rate in S/S of MSWIFA by geopolymers. A gradient boosting regression (GB) model with superior performance (R2 = 0.9214) was obtained, and a graphical user interface (GUI) software was developed to facilitate the convenient accessibility of researchers. The feature categories influencing heavy metal immobilization rate are ranked in order of importance as heavy metal properties > geopolymer raw material properties > curing conditions > alkali activator properties. This study facilitates the rapid prediction, improvement, and optimization of heavy metal immobilization in S/S of MSWIFA by geopolymers, and also provides a theoretical basis for the resource utilization of industrial solid waste, contributing to the environmental protection.

Synergistic utilization of industrial waste red mud and rice husk ash for eco-friendly geopolymer preparation: enhancing strength and mitigating hazardous leaching.

The prolonged stacking of substantial volumes of industrial waste red mud (RM) can have significantly hazardous effects on the environment. In order to address this critical problem, this study proposes the synergistic preparation of geopolymers utilizing RM in conjunction with another industrial waste, rice husk ash (RHA). Geopolymers with varying incorporation of RHA were prepared using sodium hydroxide and sodium silicate composite alkaline activator. The mechanical properties, microstructure, and environmental characteristics of geopolymers were investigated. The incorporation of RHA significantly enhanced the strength of RM-based geopolymers, with the highest strength of 25.40 MPa achieved at 40% incorporation. According to XRD patterns and FTIR spectra, C-(A)-S-H and N-(A)-S-H were generated during the geopolymerization, thereby enhancing the strength of geopolymers. From SEM micrographs of geopolymers, it was evident that the geopolymer matrix was constituted by the encapsulation of unreacted inert particles of RM and residual fragments of RHA with C-(A)-S-H and N-(A)-S-H. The leaching levels of trace elements and heavy metals in geopolymers are both below the regulatory thresholds, thereby effectively mitigating the presence of hazardous substances in raw materials. These findings proved that the reuse of RM and RHA for the synergistic preparation of environmentally friendly geopolymers is a promising approach to address the issue of substantial RM stacking.

Red Mud-Amended Soil as Highly Adsorptive Hybrid-Fill Materials for Controlling Heavy Metal Sewage Seepage in Industrial Zone.

With the rapid development of urbanization, the problem of environmental pollution is becoming more and more serious. As a major pollutant, heavy metals have caused serious contamination in soil and groundwater. In order to prevent the diffusion of heavy metals in the soil from industrial sewage, the concept of hybrid-fill layer construction improved by red mud was proposed in this study. This study examines the adsorption capacities of lead and zinc ions and engineering characteristics on red mud-amended soils by direct shear, permeability, adsorption, desorption batch and column tests. Two mixing methods, full particle size displacement mixing and partial particle size displacement mixing, were adopted. The results showed that red mud effectively increased the adsorption capacity of soil to heavy metal ions, and the desorption rate of ions after adsorption was less than 3%, which had good anti-desorption ability. The optimum content of red mud in hybrid-fill material can be determined as 20%. The direct shear test showed that the internal friction angle of hybrid-fill material was 38.9°, and the cohesive force was 30.3 kPa, which met the engineering strength requirements of foundation materials. Based on the test results, red mud can be used as a barrier material to prevent heavy metal contamination in industrial sewage from diffusion, which controls not only heavy metal contamination but also consumes industrial by-products.