Estimation of the phenolic waste attenuation capacity of some fine-grained soils with the help of ANN modeling

Abstract

In the present investigation, batch experiments were undertaken in the laboratory for different initial phenol concentration ranging from 10 to 40 mg/L using various types of fine-grained soils namely types A, B, C, D, and E based on physical compositions. The batch kinetic data were statistically analyzed with a three-layered feed-forward artificial neural network (ANN) model for predicting the phenol removal efficiency from the water environment. The input parameters considered were the adsorbent dose, initial phenol concentration, contact time, and percentage of clay and silt content in soils. The response output of the ANN model was considered as the phenol removal efficiency. The predicted results of phenol removal efficiency were compared with the experimental values as obtained from batch tests and also tests for goodness of fitting in ANN model with experimental results. The estimated values of coefficient of correlation (R = 0.99) and mean squared error (MSE = 0.006) reveals a reasonable closeness of experimental and predicted values. Out of five different types of soil, type E exhibited the highest removal efficiency (31.6 %) corresponding to 20 mg/L of initial phenol concentration. A sensitivity analysis was also carried out on the ANN model to ascertain the degree of effectiveness of various input variables.

A gross error detection method based on 3MAD–GRW–MMMD

“…In recent years, the re-emergent artificial neural networks (ANN) [25][26][27][28][29] simulate the human brain network infrastructure. With many advantages such as massively parallel, distributed memory and learning ability, ANN is especially effective to deal with the information processing problem which contains imprecise and vague conditions.…”