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.
Highlights
This study demonstrated an membrane rotating biological contactors (MRBC) system to control membrane fouling through the generation of a certain shear rate near the membrane surface
An Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) were applied for optimizing membrane permeability through its operating parameters
ANOVA analysis indicated that all three operating parameters significantly impact the permeability
Summary
Conventional approaches focus on improving membrane properties, optimizing operational parameters, and tweaking the hydrodynamics near the membrane surface [3–6]. All these techniques result in high initial cost and high energy demand, limiting their widespread application [7,8]. ANN modeling has been used in a full-scale wastewater treatment plant to optimize the dynamics of the biological effluent characteristics (COD, biological oxygen demand, total nitrogen (TN), suspended solids (SS)) [36]. This study investigates the relationship between operational parameters (disk rotational speed, membrane-to-disk gap, and organic loading rate) and the response parameter of permeability to find the optimal condition of the process. The optimization of the membrane-incorporated wastewater treatment process improved membrane permeability and reduced the operational cost of the process
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