The use of artificial neural network (ANN) for the prediction of reactive blue 19 adsorption by coffee husk-derived activated carbon. - Gabay, Benjie Ryan Tubongbanua (2024.)
This study focuses on the development and optimization of an Artificial Neural Network (ANN) model for the adsorption of reactive blue 19 dye onto coffee husk-derived activated carbon. The optimal ANN architecture, determined through a brute force algorithm, consists of a single hidden layer with eighteen neurons, employing the Levenberg-Marquardt (LM) algorithm for training. The architecture is expressed as 4-18-1, representing four input neurons, eighteen hidden neurons, and one output neuron. Sensitivity analysis using Garson's algorithm and stepwise methods identify adsorbent dosage as the most influential parameter, followed by contact time, initial dye concentration, and activation temperature, with the hierarchy denoted as Adsorbent dosage > Contact time > Initial dye concentration > Activation temperature. Relative importance calculations using Connection Weights Algorithm reveal the nature of the relationships, indicating that an increase in activation temperature, initial dye concentration, or contact time leads to a decrease in % dye removal, while an increase in adsorbent dosage and contact time corresponds to an increase in % dye removal. The ANN model outperforms the Response Surface Methodology (RSM) model in predicting the percentage of dye removal. Evaluation metrics (R2 and RMSE) demonstrate superior performance for the ANN model (R2 = 0.9946, RMSE = 1.0874) compared to the RSM model (R2 = 0.9701, RMSE = 2.5430). Nonlinear adsorption isotherm modeling reveals that Langmuir and Temkin isotherms best fit the equilibrium adsorption data. The kinetic model fitting indicates that the Pseudo-second-order kinetic model best describes the adsorption process, suggesting chemisorption as the dominant mechanism. Diffusion models confirm that the adsorption onto active sites of the adsorbent is the major rate-limiting step. The ANN-generated data accurately represent the adsorption process, aligning well with both isotherm and kinetic models.
Thesis (Undergraduate, Bachelor of Science in Chemical Engineering) School of Technology, University of the Philippines Visayas, Miagao, Iloilo. 2024.
Keywords: Coffee husk; Artificial neural network