Nonlinear modeling and machine learning techniques are needed for accurate prediction of contaminant sorption

Abstract

This study examined the accuracy of linearized and nonlinearized forms of kinetics and isotherm models in fitting methylene blue (MB) adsorption by waste-derived biochar. The biochars were effective at MB removal, achieving adsorption capacities of 4.15–34.39 mg/g. The best fitting model was assessed using determination coefficient (R2) and four error functions. Nonlinearized models provided a better data fit, showing higher determination coefficients (R2) of 0.86–0.999 compared to linearized models (0.229–0.988) and lower errors (9.57–36% versus 15.75–48.5%). The use of linearized forms should be avoided since modern common software readily supports nonlinear fitting. Additionally, a regression tree model was developed using machine learning to identify key factors influencing MB adsorption and offer accurate estimations of MB adsorption. Regression tree modelling exhibited excellent predictive capability (R2 = 0.99). Using feature importance analysis, the strongest predictors of adsorption capacity were initial concentration > carbon and nitrogen contents > adsorber pH > contact time. Regression tree modelling can capture process parameters and adsorbent characteristics into an easy-to-use model which can be used in process operations and optimization. The study revealed that treating of 1 m3 of dye-contaminated wastewater cost was estimated at AUD $27–230. Biochars reusability for 3 cycles was evaluated, noting a significant reduction in effectiveness (p <  < 0.001). Despite the observed decrease in adsorption capacity, waste-derived biochars continue to offer a cost-effective, environmentally sustainable solution aligning with the concept of "treating waste with waste”. The study highlights potential of using non-conventional materials to reduce the environmental impacts and cost of wastewater treatment, alongside the benefits of machine learning for process optimization.No Full Tex