Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Abstract

Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.

A New ANN Technique for Short-Term Wind Speed Forecasting Based on SCADA System Data in Turkey

“…The artificial neural network is a framework for data processing that is used to examine many aspects of a system that processes information and is prompted by the human nervous system, such as the brain [30]. The topology of Fig.…”

Wastewater Treatment with Technical Intervention Inclination towards Smart Cities

“…In recent years, there has been an increased emphasis placed on the development of real-time data-collecting techniques, which also include the combination of sensor technology and information science [74,75]. Models based on artificial intelligence are emerging as useful new tools for the construction of prediction models [76]. A computational artificial intelligence using artificial neural networks, also known as ANNs, can process a wide range of information because they contain billions of neurons that are connected to one another [77,78].…”

Response Surface Methodology for the Synthesis and Characterization of Bio-Oil Extracted from Biomass Waste and Upgradation Using the Rice Husk Ash Catalyst

“…Therefore, to optimize the parameters, the design of experiments (DOE) can be useful for being statistically foreseeable because of its ability to give highly reliable results with fewer experiments. , Response surface methodology (RSM) is an exceptional approach to increasing the efficiency of the reactors’ performance and studying different parameters’ behaviors simultaneously . RSM is an experiential statistical technique that can help understand the mutual relationship of several process parameters by investigating the mathematical modeling on the response variable . The quantitative data acquired from experimental design, regression model analysis, and operational conditions can result in high-end performance .…”

“… 17 RSM is an experiential statistical technique that can help understand the mutual relationship of several process parameters by investigating the mathematical modeling on the response variable. 18 The quantitative data acquired from experimental design, regression model analysis, and operational conditions can result in high-end performance. 19 A number of studies concentrate on improving the operating conditions of the bio-oil yield.…”