Fouling prediction using neural network model for membrane bioreactor system

Membrane bioreactor (MBR) technology is a new method for water and wastewater treatment due to its ability to produce better and high-quality effluent that meets water quality regulations. MBR also is an advanced way to displace the conventional activated sludge (CAS) process. Even this membrane gives better performances compared to CAS, it does have few drawbacks such as high maintenance cost and fouling problem. In order to overcome this problem, an optimal MBR plant operation needs to be developed. This can be achieved through an accurate model that can predict the fouling behaviour which could optimise the membrane operation. This paper presents the application of artificial neural network technique to predict the filtration of membrane bioreactor system. The Radial Basis Function Neural Network (RBFNN) is applied to model the developed submerged MBR filtration system. RBFNN model is expected to give good prediction model of filtration system for estimating the fouling that formed during filtration process

CESE-2019

This book is a collation of articles published in the Special Issue "CESE-2019: Applications of Membranes" in the journal Sustainability. It contains a wide variety of topics such as the removal of trace organic contaminants using combined direct contact membrane distillation–UV photolysis; evaluating the feasibility of forward osmosis in diluting reverse osmosis concentrate; tailoring the effects of titanium dioxide (TiO2) and polyvinyl alcohol (PVA) in the separation and antifouling performance of thin-film composite polyvinylidene fluoride (PVDF) membrane; enhancing the antibacterial properties of PVDF membrane by surface modification using TiO2 and silver nanoparticles; and reviews on membrane fouling in membrane bioreactor (MBR) systems and recent advances in the prediction of fouling in MBRs. The book is suitable for postgraduate students and researchers working in the field of membrane applications for treating aqueous solutions

Wastewater Treatment and Reuse Technologies

This edited volume is a collection of 12 publications from esteemed research groups around the globe. The articles belong to the following broad categories: biological treatment process parameters, sludge management and disinfection, removal of trace organic contaminants, removal of heavy metals, and synthesis and fouling control of membranes for wastewater treatment

Evaluation of Greywater and A/C Condensate for Water Reuse: An Approach using Artificial Neural Network Modeling

Alternative resources play a vital role for water-sensitive infrastructures where consistent water supply is a challenge, and freshwater resources are limited. Greywater and A/C condensate are potentially new alternatives for increasing urban water supply. An advanced physical filtration system for greywater treatment was developed named as GAC-MI-ME. It is comprised coarse filtration (CR-F) followed by microfiltration (MF), granular activated carbon (GAC), ultrafiltration (UF), ultraviolet (UV), and reverse-osmosis (RO). GAC-MI-ME effluent-quality was analyzed for greywater from laundry, shower, and wash basin. High-grade effluent equivalent to unrestricted water reuse was observed at UF and RO units. A subsequent tool (GREY-ANN) was proposed for GAC-MI-ME effluent quality predictions. Artificial Neural Network (ANN) was applied to develop 5 unit models for selected parameters including Biochemical Oxidation Demand, pH, Total Dissolved Solids, Turbidity, and Oxidation-Reduction Potential to predict effluent quality at each stage of GAC-ME-MI treatment using water quality databases (developed from a series of experiments testing greywater of varying strength). The 15 days storage potential of GAC-MI-ME treated effluents were also analyzed and showed no significant quality depletion in UF and RO effluent quality. A hybrid modeling approach was applied to A/C condensate estimation, which included a psychrometric based “Air-Conditioner-Condensate” (ACON) model, and data-driven “Internal Load Analysis using Neural Network” (ILAN) model. The ACON model uses mass and energy balance approach for HVAC systems operating under steady state conditions. It accounts for psychometric states of different air parcels during the cooling and dehumidification process. The ILAN model was developed using ANN for the city of Doha to predict Internal Load at a daily time step for variable climatic conditions (temperature, relative humidity). The ACON- ILAN models were validated for a test building and applied for yearly condensate estimation for Doha. The virtual simulations of the hybrid model showed an annual condensate volume of 1370 and 3700 l/100 m^3 of cooling space for 20% and 100% outdoor-ventilation. The condensate quality (for limited water quality parameters) showed values within primary and secondary drinking water standards, except for copper, which had marginally higher concentrations. Overall, the GREY-ANN and ACON-ILAN may improve greywater and A/C condensate reuse potentials

Assessment and characterization of dissolved organic nitrogen removal in simulated soil aquifer treatment systems

Treated wastewater (reclaimed water) is becoming more critical as an alternative water source due to population growth and extreme precipitation patterns. The more we rely on water reuse schemes, the more we have to expand our knowledge about the efficiency of treatment processes. As part of the water reuse programs, soil aquifer treatment systems are widely applied to enhance groundwater resources and the quality of treated wastewater by providing environmental barriers, including soil and water. There have been decades of applied research on different types of soil/aquifer-based treatment systems focusing on their operation and maintenance as well as their capability to remove contaminants. However, considering the fact that the list of contaminants of concern are continually developing and these systems are highly site-specific, there is always a need to understand better the fate of compounds that can act as a contaminant themselves in specific doses or become pre-cursors of other contaminants.Therefore, this research focused on dissolved organic nitrogen (DON) that has yet to be adequately investigated. Effluent DON could be problematic in the receiving waters as a nitrogen source causing eutrophication. It can also act as a precursor for nitrogenous disinfection by-products. In this study, we used unsaturated soil columns to simulate the vadose zone treatment systems in the lab. We simulated field characteristics by using the soil from the vadose of effluent rapid infiltration basins and reclaimed water from a full scale water reclamation rather than artificial wastewater. We observed steady DON removal in the columns for more than a year and a half, as well as the improvement of water quality through the removal of other organic compounds. Since N-Nitroso-dimethylamine (NDMA), one of the nitrogenous disinfection by-products, concentrations were pretty low in our feed reclaimed water, and we did not reach conclusive results about the columns’ capability of further removal of NDMA. However, our results showed that the columns could remove NDMA precursors by removing the reclaimed water organic matter. In addition, we characterized the organic matter in the reclaimed water applied to the columns using fluorescence spectroscopy EEM spectra. EEM spectra analysis has been widely used for organic matter characterization in drinking or surface water. Here, we focused on its capability to monitor the performance of soil aquifer treatment systems. We were able to detect humic-like, fulvic-like, and tyrosine-like compounds in the influent and effluent of the columns using the fluorescence regional technique. We followed their change through the columns and found that most of the DON removal was attributed to humic-like compounds, even though the removal was observed in different defined regions. We fitted a PARAFAC model with four components to our data that was able to be compared with other models across different aquatic systems. The main component of the model (C1) was attributed to the aromatic molecules of terrestrial origin and associated with humic-like compounds. Another detected component (C3) corresponded with microbial humic-like and terrestrially delivered and was reported at drinking water treatment plants. The results confirmed the capability of EEM spectra as a monitoring tool for the organic material and that its potential should be further investigated in the lab and field-scale soil aquifer treatment systems under different hydrogeological conditions, operation and maintenance techniques, and source water quality

FAULT DETECTION AND ISOLATION FOR WIND TURBINE DYNAMIC SYSTEMS

This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier

A sustainable ultrafiltration of sub-20 nm nanoparticles in water and isopropanol: experiments, theory and machine learning

This research focused on ultrafiltration (UF) for particles down to 2 nm against membranes with larger pore size in water and IPA, which has the potential to save up to 90% of energy. This study developed electrospray (ES) - scanning mobility particle sizer (SMPS) method to fast and effective measure retention efficiencies for small particles (ZnS, Au and PSL) on polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and polycarbonate (PCTE) in different liquids. Theoretical models that could quantitatively explain the experimental results for small particles in medium-polarity organic solvents were also developed. Results showed that the highest efficiency was up to ~80% with 10 nm Au nanoparticle challenged on 100 nm rated PTFE, which demonstrated the feasibility of the proposed sustainable UF. The theoretical models were validated by experimental results and indicated that a higher efficiency was possible by enhancing material properties of membranes, particles, or liquids. Therefore, optimization on filtration condition was performed. A hybrid artificial neural network (ANN) and particle swarm optimization algorithm (PSO) models was firstly applied in this case. The dataset includes all the experimental results and some additional calculated retention efficiencies. Optimization parameters include membrane zeta potential, pore size, particle size, particle zeta potential, and Hamaker constant. The ANN model provided highly correlated predicted values with target values. The PSO model showed that a filtration efficiency of 99.9% could be achieved by using a 52.2 nm filter with a -20.3 mV zeta potential, 5.5 nm nanoparticles with a 41.4 mV zeta potential, and a combined Hamaker constan

Technology, Science and Culture

From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them

Mechanistic and kinetics of carbon dioxide fixation in aerobic biogranules developed from palm oil mill effluent

Palm oil mill effluent (POME) generated from oil palm industries is a major source of water and air pollution. Ineffective wastewater treatment results in excessive production of carbon dioxide (CO2) that contributes to global warming. In this study, photosynthetic aerobic biogranules (PAG) were develop in a lab-scale phototrophic sequencing batch reactor (PSBR) using POME as substrate. The capability of PAG for CO2 fixation was determined and the biokinetic parameters were estimated from the chemical oxygen demand (COD) fractionation of POME. The PAG were developed in a four litre column reactor that was operated with four hour per-cycle time for 40 days. After six weeks of development process, it was observed that, compact-structured PAG were formed in sizes between 1.0-3.0 mm and a maximum settling velocity of 97 m/h. The mass liqour suspanded solid (MLSS) and the mass liqour volatile suspanded solid (MLVSS) were found to increased from 2.0-8.0 g/L and 2.5-7.0 g/L, respectively. The field emission scanning electron microscope (FESEM) analysis showed the presence of spherical-shaped bacteria (cocci) with sizes ranging from 1.86-2.57 μm. The removal performance achieved was 55 % for COD, 85 % for total nitrogen (TN) and 72 % for total phosphorus (TP). In additon, the CO2 fixation was analysed in terms of carbon content of the PAG. The CO2 fixation rate was 0.0967 g/L/d and for one year application the result was estimated to be 4.178 g/L/d. The stoichiometric and kinetic parameters were determined to describe the bioprocess of PAG development using the PSBR system. The COD fractionation of POME indicated the biodegradable substance was 72.9 % with the largest fraction of 46.8 % for slow biodegradable substances (XS). The biokinetic parameters for the maximum specific growth rate of heterotrophic biomass (μmaxH) was 3.36 d-1 while the half-saturation coefficient for the readily biodegradable substrate (Ks) was 40.1 gm-3. The biokinetic parameters obtained were verified for the development process of PAG in the PSBR system