scholarly journals Support Vector Regression Modelling of an Aerobic Granular Sludge in Sequential Batch Reactor

Membranes ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 554
Author(s):  
Nur Sakinah Ahmad Yasmin ◽  
Norhaliza Abdul Wahab ◽  
Fatimah Sham Ismail ◽  
Mu’azu Jibrin Musa ◽  
Mohd Hakim Ab Halim ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Support Vector ◽  
Batch Reactors ◽  
Grid Search Method ◽  
Internal Interaction ◽  
Kernel Parameters

Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The prediction becomes harder when dealing with a limited dataset due to the limitation of the experimental works. A radial basis function algorithm with selected kernel parameters of cost and gamma was used to developed SVR models. The kernel parameters were selected by using a grid search method and were further optimized by using particle swarm optimization and genetic algorithm. The SVR models were then compared with an artificial neural network. The prediction results R2 were within >90% for all predicted concentration of COD. The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment.

Dynamic Modelling of Aerobic Granular Sludge Artificial Neural Networks

2017 ◽  
Vol 7 (3) ◽  
pp. 1568
Author(s):  
Nurazizah Mahmod ◽  
Norhaliza Abdul Wahab
Keyword(s):  
Neural Network ◽  
Batch Reactor ◽  
Dynamic Modelling ◽  
Oxygen Demand ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Batch Reactors ◽  
Neural Network Approach ◽  
Dynamic Neural Network ◽  
Promising Development

Aerobic Granular Sludge (AGS) technology is a promising development in the field of aerobic wastewater treatment system. Aerobic granulation usually happened in sequencing batch reactors (SBRs) system. Most available models for the system are structurally complex with the nonlinearity and uncertainty of the system makes it hard to predict. A reliable model of AGS is essential in order to provide a tool for predicting its performance. This paper proposes a dynamic neural network approach to predict the dynamic behavior of aerobic granular sludge SBRs. The developed model will be applied to predict the performance of AGS in terms of the removal of Chemical Oxygen Demand (COD). The simulation uses the experimental data obtained from the sequencing batch reactor under three different conditions of temperature (30˚C, 40˚C and 50˚C). The overall results indicated that the dynamic of aerobic granular sludge SBR can be successfully estimated using dynamic neural network model, particularly at high temperature.

scholarly journals Improved support vector machine using optimization techniques for an aerobic granular sludge

2020 ◽  
Vol 9 (5) ◽  
pp. 1835-1843
Author(s):  
Nur Sakinah Ahmad Yasmin ◽  
Norhaliza Abdul Wahab ◽  
Aznah Nor Anuar
Keyword(s):  
Support Vector Machine ◽  
Batch Reactor ◽  
Granular Sludge ◽  
Optimization Techniques ◽  
Aerobic Granular Sludge ◽  
Support Vector ◽  
Good Prediction ◽  
Grid Search ◽  
Limited Data ◽  
Data Set

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.

scholarly journals A Bioreactor Designed for Restricting Oversize of Aerobic Granular Sludge

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 374
Author(s):  
Hongbo Feng ◽  
Honggang Yang ◽  
Jianlong Sheng ◽  
Zengrui Pan ◽  
Jun Li
Keyword(s):  
Particle Size ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Granular Sludge ◽  
Pilot Scale ◽  
Ammonia Nitrogen ◽  
Pollutant Removal ◽  
Volume Index ◽  
Aerobic Granular Sludge ◽  
Air Bubbles

Aerobic granular sludge (AGS) with oversized diameter commonly affects its stability and pollutant removal. In order to effectively restrict the particle size of AGS, a sequencing batch reactor (SBR) with a spiny aeration device was put forward. A conventional SBR (R1) and an SBR (R2) with the spiny aeration device treating tannery wastewater were compared in the laboratory. The result indicates that the size of the granular sludge from R2 was smaller than that from R1 with sludge granulation. The spines and air bubbles could effectively restrict the particle size of AGS by collision and abrasion. Nevertheless, there was no significant change in mixed liquor suspended solids (MLSS) and the sludge volume index (SVI) in either bioreactors. The removal (%) of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N) in these two bioreactors did not differ from each other greatly. The analysis of biological composition displays that the proportion of Proteobacteria decreased slightly in R2. The X-ray fluorescence (XRF) analysis revealed less accumulation of Fe and Ca in smaller granules. Furthermore, a pilot-scale SBR with a spiny aeration device was successfully utilized to restrict the diameter of granules at about 300 μm.

Continuous flow aerobic granular sludge reactor for dairy wastewater treatment

2015 ◽  
Vol 71 (3) ◽  
pp. 440-445 ◽  
Author(s):  
C. Bumbac ◽  
I. A. Ionescu ◽  
O. Tiron ◽  
V. R. Badescu
Keyword(s):  
Continuous Flow ◽  
Flow Reactor ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Granular Sludge ◽  
Experimental Period ◽  
Phosphate Removal ◽  
Dairy Wastewater ◽  
Aerobic Granular Sludge ◽  
Removal Efficiencies

The focus of this study was to assess the treatment performance and granule progression over time within a continuous flow reactor. A continuous flow airlift reactor was seeded with aerobic granules from a laboratory scale sequencing batch reactor (SBR) and fed with dairy wastewater. Stereomicroscopic investigations showed that the granules maintained their integrity during the experimental period. Laser diffraction investigation showed proof of new granules formation with 100–500 μm diameter after only 2 weeks of operation. The treatment performances were satisfactory and more or less similar to the ones obtained from the SBR. Thus, removal efficiencies of 81–93% and 85–94% were observed for chemical oxygen demand and biological oxygen demand, respectively. The N-NH+4 was nitrified with removal efficiencies of 83–99% while the nitrate produced was simultaneously denitrified – highest nitrate concentration determined in the effluent was 4.2 mg/L. The removal efficiency of total nitrogen was between 52 and 80% depending on influent nitrogen load (39.3–76.2 mg/L). Phosphate removal efficiencies ranged between 65 and above 99% depending on the influent phosphate concentration, which varied between 11.2 and 28.3 mg/L.

Cultivation of Aerobic Granular Sludge under Selective Pressure in a Sequencing Batch Reactor

2011 ◽  
Vol 255-260 ◽  
pp. 3037-3041 ◽  
Author(s):  
Kui Zu Su ◽  
Chang Wang ◽  
Hui Fang
Keyword(s):  
Suspended Solids ◽  
Sequencing Batch Reactor ◽  
Settling Velocity ◽  
Selective Pressure ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Granular Sludge ◽  
Volume Index ◽  
Aerobic Granular Sludge ◽  
Granulation Process

Aerobic granules were cultivated in the sequencing batch reactor at 15-25°C, pH 7.0 ± 0.1. Settling time decreased from 5 minutes to 1 minute gradually. As increasing the chemical oxygen demand (COD) and NH3-N in influent, COD removal efficiency and mixed liquid suspended solids of the reactor increased. Sludge volume index decreased continuously for a few days and then stabilized at 22 ml g-1. Selective pressure induced by settling velocity was proved to play a crucial role in activated sludge granulation. Based on the continuously measured data, the granulation process was divided into three phases, granules namely initiating, developing and maturating.

Simulation of Aerobic Granular Sludge Process Efficiency

2017 ◽  
Vol 68 (8) ◽  
pp. 1723-1725
Author(s):  
Elena Elisabeta Manea ◽  
Costel Bumbac ◽  
Olga Tiron ◽  
Razvan Laurentiu Dinu ◽  
Valeriu Robert Badescu
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Batch Reactor ◽  
Granular Sludge ◽  
Process Efficiency ◽  
Aerobic Granular Sludge ◽  
Organic Load ◽  
Batch Reactors ◽  
Conventional Activated Sludge ◽  
Pollutants Removal

Using aerobic granular sludge for wastewater treatment has multiple advantages compared to conventional activated sludge systems, most important being the ability of simultaneous removal of the pollutants responsible for eutrophication: organic load, compounds of nitrogen (NH4+; NO3-) and phosphorus (PO43-). The advantages are currently exploited for developing the next generation of wastewater treatment systems while the identified limitations are approached by experimental and theoretical researches worldwide. The aim of the study consists in evaluating the possibility of predicting the system�s response to different changes in the influent wastewater loadings. The paper presents simulations results backed up by experimental data for pollutants removal efficiencies evaluation for a sequential batch reactor (SBR) with aerobic granular sludge. The mathematical model is based on the activated sludge model no. 3, which was updated by considering the simultaneous biological nitrification (NH4+NO3) and denitrification (NO3-N2) processes, thus complying with the biochemical reactions occurring in aerobic granular sludge sequential batch reactors. The model developed was validated by the experimental results obtained on a laboratory scale SBR monitored for over a month.

scholarly journals DEVELOPMENT OF AEROBIC GRANULES IN SEQUENCING BATCH REACTOR SYSTEM FOR TREATING HIGH TEMPERATURE DOMESTIC WASTEWATER

2019 ◽  
Vol 81 (3) ◽  
Author(s):  
Mohd Hakim Ab Halim ◽  
Aznah Nor Anuar ◽  
Shreeshivadasan Chelliapan ◽  
Norhaliza Abdul Wahab ◽  
Hazlami Fikri Basri ◽  
...  
Keyword(s):  
High Temperature ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Biomass Concentration ◽  
Domestic Wastewater ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Organic Loading Rate ◽  
Hot Climate

The application of aerobic granular sludge (AGS) in treating real domestic wastewater at high temperature is still lacking. In this study, the microstructure and morphology of the granules, as well as bioreactor performance, were investigated during the treatment of real domestic wastewater at high temperature (50 °C). The experiment was executed in a sequencing batch reactor (SBR) with a complete cycle time of 3 hours for the treatment of low-strength domestic wastewater at an organic loading rate (OLR) of 0.6 kg COD m−3 d−1. Stable mature granules with average diameters between 2.0 and 5.0 mm, and good biomass concentration of 5.8 g L−1 were observed in the bioreactor. AGS achieved promising results in the treatment of domestic wastewater with good removal rates of 84.4 %, 99.6 % and 81.7 % for chemical oxygen demand (COD), ammoniacal nitrogen (NH3−N), and total phosphorus (TP), respectively. The study demonstrated the formation capabilities of AGS in a single, high and slender column type-bioreactor at high temperature which is suitable to be applied in hot climate condition areas especially countries with tropical and desert-like climates.

scholarly journals Performance comparison of SVM and ANN for aerobic granular sludge

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Nur Sakinah Ahmad Yasmin ◽  
Norhaliza Abdul Wahab ◽  
Aznah Nor Anuar ◽  
Mustafa Bob
Keyword(s):  
Wastewater Treatment ◽  
Batch Reactor ◽  
Treatment Plant ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Treatment Technology ◽  
Effluent Quality

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.

scholarly journals Rapid cultivation of aerobic granular sludge by xanthan gum in SBR reactors

2018 ◽  
Vol 2017 (2) ◽  
pp. 360-369 ◽  
Author(s):  
Sha Liu ◽  
Hanhui Zhan ◽  
Yaqi Xie ◽  
Weijiang Shi ◽  
Siming Wang
Keyword(s):  
Activated Sludge ◽  
Xanthan Gum ◽  
Oxygen Demand ◽  
Granular Sludge ◽  
Surface Characteristics ◽  
Aerobic Granular Sludge ◽  
Batch Reactors ◽  
Close Monitoring ◽  
Sludge Granulation ◽  
Aerobic Sludge

Abstract This study focuses on the effect of xanthan gum on aerobic sludge granulation, through close monitoring of the physical and chemical changes of the aerobic granular sludge, and treatment performance. Two sequencing batch reactors (SBRs), R1 and R2, were seeded with activated sludge only (R1) and with a mixture of activated sludge and 40 mg/L of xanthan gum (R2). The results showed that granulation finished on the 20th day in R2, far faster than the granulation time of 30 days in R1. Meanwhile, there was a reliably higher sludge concentration, better settling properties and better particle mechanical strength in R2, and better removal performance of total nitrogen (TN) and chemical oxygen demand (COD). The results demonstrated that seeding xanthan gum enhanced the aerobic sludge granulation in the SBR. Maybe its anionic and hydrophilic surface characteristics facilitate interactions with cations and other polysaccharides, inducing stronger gelation, which promoted the formation of particles or increased the internal relationship between particles, thereby increasing the cohesion within the sludge, so that the granular sludge was not easily broken.

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