Journal Article

SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment

Arshad, Ushtar, Bilad, Muhammad Roil, Waqas, Sharjeel, Malik, Asher Ahmed, Nordin, Nik Abdul Hadi Md, Saeed, Anwar Ameen Hezam, Sambudi, Nonni Soraya, Harun, Noorfidza Yub

2022

Overview

Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants. The effect of operational parameters (disk rotational speed, hydraulic retention time (HRT), and sludge retention time (SRT)) was studied on the membrane permeability. ANN and SVM are machine learning algorithms that aim to predict the model based on the trained data sets. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. Feed-forward ANN with the back-propagation algorithm and SVN regression models for various kernel functions were trained to augment the membrane permeability. An overall comparison of predictive models for the test data sets reveals the model’s significance. ANN modelling with 13 hidden layers gives the highest R2 value of >0.99, and the SVM model with the Bayesian optimizer approach results in R2 values higher than 0.99. The MRBC is a promising substitute for traditional suspended growth processes, which aligns with the stipulations of ecological evolution and environmentally friendly treatment.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Artificial neural networks / Back propagation / Back propagation networks / Bacteria / Bayesian analysis / Biofilms / Biological contactors / biological wastewater treatment / Chemical oxygen demand / Datasets / Effluent quality / Effluents / Hydraulic retention time / Kernel functions / Learning algorithms / Machine learning / machine learning algorithm / Mathematical models / membrane fouling / Membrane permeability / Membrane processes / Membranes / Modelling / Neural networks / Optimization / Permeability / Prediction models / Purification / Regression analysis / Regression models / Retention / Retention time / Rotating disks / Rotation / Sewage / Shear rate / Sludge / Statistical analysis / Support vector machines / Wastewater treatment / Water treatment