Adsorption of Indigo Carmine dye onto the surface-modified adsorbent prepared from municipal waste and simulation using deep neural network

Highlights: • Matrix of municipal waste was converted into an absorbent by surface cationization. • The adsorbent had higher adsorption capacity for anionic dye Indigo Carmine. • Liu isotherm described well the adsorption process. • Classical machine learning models didn't predict accurately. • Deep Neural Network was able to model the adsorption process with 96% accuracy. A new adsorbent was prepared from municipal wastes (a mixture of Corn Stover, Paper Waste, and Yard Waste) by cationization with 3 ̶ Chloro ̶ 2 ̶ Hydroxypropyl Trimethylammonium Chloride. The FTIR spectrum confirmed the quaternary ammonium group's presence on the adsorbent surface (1450 cm⁻¹). The maximum adsorption capacity (148 mg/g) was higher than the earlier reported values. Liu isotherm described well the adsorption process, with a high R²_adj value (0.997). The pseudo-first-order equation fits well for kinetic data, and thermodynamic experiments demonstrated the endothermic nature of the adsorption. The deep neural network (DNN) is applied to simulate the adsorption process, which outperformed the classical machine learning and shallow neural network models. The DNN model predicted accurately the adsorption process with the lowest deviation from the actual values with Mean Absolute Error (MAE = 3.2), Root Mean Squared Error (RMSE = 4.89), and the highest performance accuracy of R² (0.96) as compared to various classical ML algorithms such as Linear Regressions (MAE = 12.53, RMSE = 18.01, R² = 0.42), Random Forest (MAE = 5.81, RMSE = 10.05, R² = 0.82), and Extra Trees (MAE = 4.35, RMSE = 8.22, R² = 0.88). The utilized DNN model can be used for predicting the removal efficiency of dyes for various combinations of input parameters without going through laboratory experiments.