Application of feed-forward and recurrent neural network in modelling the adsorption of boron by amidoxime-modified poly(Acrylonitrile-co-Acrylic Acid)

Abstract

This research reports application of artificial neural network (ANN) in investigation and optimisation of boron adsorption capacity in aqueous solution using amidoxime-modified poly(acrylonitrile-<i>co</i>-acrylic acid) (AO-modified poly(AN-<i>co</i>-AA)). Both feed-forward and recurrent ANN have been utilized to predict the adsorption potential of synthesised polymer. Three operational parameters, which are adsorbent dosage, initial pH and initial boron concentration during adsorption process were designed to study their effects on the removal capacity. The ANN was trained from experimental data and serviced to optimize, develop and create various prediction models in the process of boron adsorption by AO-modified poly(AN-<i>co</i>-AA). Among several models, radial basis function (RBF) with orthogonal least square (OLS) algorithm displayed good prediction on boron adsorption capacity with mean square error (MSE) and coefficient of determination (R<sup>2</sup>) at 0.000209 and 0.9985, respectively. With desirable the MSE and R<sup>2</sup> values, ANN worked as a promising prediction tool that was able to generate good estimate. The simulated maximum adsorption capacity of the synthesized polymer is 15.23 ± 1.05 mg boron/g adsorbent. Besides, from the results of ANN, the AO-modified poly(AN-<i>co</i>-AA) was proven to be a potential adsorbent for the removal of boron in wastewater treatment.

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

“…The choice of delay range depends on the nature of the problem and the temporal dependencies in the data. Longer delay ranges can help the network capture longer-term dependencies but also increase model complexity and computational costs 40 . Shorter delay ranges may limit the network's ability to model longer-term dependencies.…”

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

“…In [32] it was observed that nodes centroids in an RBFN can be computed using some available clustering algorithms. Such an approach assumes that the internal configuration of the constructed RBFN is directly determined by the clustering algorithm outcome.…”

“…This could be a disadvantage since centers are selected nonspecifically and the RBF performance relies critically on their location. To reduce the risk it was proposed to use the orthogonal least square algorithm, where the RBFN structure can be dynamically changed by adding a new neuron in the hidden layer [32].…”