Carbonized adlai (Coix lacryma-jobi) shells for fixed-bed adsorption of textile dyes a bayesian regularization-backpropagation neural network modelling. - Batayen, Christian Jade Ocampo
Carbonized Adlai derived from waste Coix lacryma-jobi shells was checked for its potential to act as an adsorbate for the various textile dyes Eriochrome Black T (EBT), Reactive Blue 19 (RB19), Methyl Orange (MO), and Methylene Blue (MB), which act as harmful pollutants once they mix in watersheds. Acting as the adsorbent in a fixed-bed column, Adlai was tested for its absorption capabilities against the aforementioned dyes. The experimental data was used to train an Artificial Neural Network (ANN) under a Bayesian Regularization training algorithm in MATLAB R2021a. The resulting network had three input neurons consisting of the contact time, initial concentration, and volumetric flowrate, while the output neuron returned a C/C0 value. The number of neurons in the hidden layer was optimized using RMSE values, and the relative sensitivity was analyzed using Garson's equation for relative sensitivity. The regression data was also checked for robustness. Two-sample t-test was used in the determining if there is a significant difference between the simulation data and the experimental data. The resulting simulated data was used in linearized mathematical modelling of fixed bed adsorption using Adams-Bohart, Thomas, and Yoon-Nelson. Results show that the models were most sensitive to the time parameter, while initial concentration and flowrate shared the remainder. It was discovered that the breakthrough time, exhaustion point, and the adsorption capacity of the column decreased with increasing flowrate and increasing initial concentration. Regression training values show the networks for EBT, RB19, MO, and MB have R2 values of 0.99968, 0.99986, 0.99891, and 0.99965 making the values robust. Error bar histograms also show the errors clustered around zero, making for good generalization of the data set. It was also found that for all four dye types used, MB was the dye with most affinity for carbonized Adlai. Overall, the artificial neural networks were deemed capable of data prediction and generalization, and were considered as good models for breakthrough curve generation.