Abstract:In this study, experiments have been conducted to evaluate the organics and nutrients removal from synthetic wastewater by a laboratory scale moving bed biofilm process. For nutrients removal, moving bed biofilm process has been applied in series with anaerobic, anoxic and aerobic units in four separate reactors. Moving bed biofilm reactors were operated continuously at different loading rates of nitrogen and Phosphorus. During optimum conditions, close to complete nitrification with average ammonium removal efficiency of 99.72% occurred in the aerobic reactor. In the aerobic reactor, the average specific nitrification rate was 1.8 g NO x -N kg VSS. The results of the average effluent soluble COD concentration from each reactor showed that denitrification process in the second anoxic reactor consumed most of the biodegradable organic matter. As seen from the results, denitrification rate has increased with increasing NO x -N loading in the second anoxic reactor. The aerobic phosphate removal rate showed a good correlation to the anaerobic phosphate release rate. Moreover, phosphate removal rate showed a strong correlation to the phosphate loading rate in the aerobic reactor. In optimum conditions, the average SCOD, total nitrogen and phosphorus removal efficiencies were 96.9, 84.6 and 95.8%, respectively. This study showed that the moving bed biofilm process could be used as an ideal and efficient option for the total nutrient removal from municipal wastewater.
a b s t r a c tDue to the efficiency and flexibility of integrated biological systems, they have been widely developed for the treatment of various waste-waters in recent decades. Present study aims at revealing the efficiency of bio-filter and activated sludge (BF/AS) integrated biological system in phenol removal from wastewater. The cylindrical bioreactors (continuous flow, 12 L) are made from Plexiglas. Interlaced plastic discs were fixed inside the reactor to hold the biofilm. The effects of pH (6.5-8), phenol concentration (100-500 mg/L), nitrogen (30-80 mg/L), phosphorous (6-16 mg/L), glucose concentration (50-500 mg/L), MLSS concentration (1450-3500 mg/L) and hydraulic retention time (2-4.4 h) were evaluated. Results of the study showed that this system could remove 500 mg/L of phenol concentration and 4-4.5 kg COD/m 3 d organic load in 4 h under favorable conditions. Pearson correlation coefficient between removal efficiency and phenol concentration was -0.446 (P < 0.001). By increasing phenol concentration, removal efficiency decreased. Appropriate COD/N/P for maximum efficiency was equal to 100/10/2. Dominant identified bacteria in the system include: Pseudomonas aeruginosa, Pseudomonas alcaligenes, acetinobacter, morexella and Brevundiomonas vesicalaris. Phenol decomposition was done according to the second-order kinetic reaction. Concentration difference (ΔA) to retention time difference (ΔT) ratio were 53.9, 69.5 and 100 for retention times of 2.2, 6 and 7 h, respectively. The BF/AS integrated biological system is highly efficient in removing phenol from aqueous and as an environment-friendly procedure could treat waste-waters containing average phenol concentrations in a relatively short period.
Given the capacity of Optical Coherence Tomography (OCT) imaging to display structural changes in a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called “Livelayer” which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra’s Shortest Path First (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of Inner Limiting Membrane, Inner Plexiform Layer–Inner Nuclear Layer, Outer Plexiform Layer–Outer Nuclear Layer was much less than that for the manual segmentation, 5 s for the ILM (minimum) and 15.57 s for the OPL–ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL–INL, OPL–ONL, respectively. The Bland–Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL–ONL and < 1 for the other two. The unsigned errors between the Livelayer and the manual algorithm was 1.33 for ILM and 1.53 for Nerve Fiber Layer–Ganglion Cell Layer in peripapillary B-Scans. The Dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.
Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called “Livelayer” which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra’s Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15.57s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and < 1 for the other two. The dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.
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