Modeling nitrate concentrations in a moving bed sequencing batch biofilm reactor using an artificial neural network technique

In this study, the performance data of a moving-bed sequencing batch biofilm reactor (MBSBBR) treating synthetic wastewater were simulated using multi-layer perceptron neural-network technique. Multi-linear regression (MLR) technique is also used for a comparison. The performance of MBSBBR was evaluated using these models for a set of experimental results obtained from a model reactor operated with different cycle times and temperatures. The experimental data were retrieved from a previous reported work. Operational time, temperature, ammonium nitrogen, and pH were used as inputs for modeling, whereas nitrate concentration was the output variable. The results of the models were compared using statistical criteria, such as mean square error, mean absolute error, mean absolute relative error, and determination coefficient (R2). The results showed that the multi-layer perceptron neural-network produced more accurate results than those of MLR, although the latter gave reasonable results. © 2014, © 2014 Balaban Desalination Publications. All rights reserved

Modeling nitrate concentrations in a moving bed sequencing batch biofilm reactor using an artificial neural network technique

In this study, the performance data of a moving-bed sequencing batch biofilm reactor (MBSBBR) treating synthetic wastewater were simulated using multi-layer perceptron neural-network technique. Multi-linear regression (MLR) technique is also used for a comparison. The performance of MBSBBR was evaluated using these models for a set of experimental results obtained from a model reactor operated with different cycle times and temperatures. The experimental data were retrieved from a previous reported work. Operational time, temperature, ammonium nitrogen, and pH were used as inputs for modeling, whereas nitrate concentration was the output variable. The results of the models were compared using statistical criteria, such as mean square error, mean absolute error, mean absolute relative error, and determination coefficient (R2). The results showed that the multi-layer perceptron neural-network produced more accurate results than those of MLR, although the latter gave reasonable results

Evaluation of macular, retinal nerve fiber layer and choroidal thickness by optical coherence tomography in children and adolescents with vitamin B₁₂ deficiency.

Purpose To investigate macular, Retinal Nerve Fiber Layer (RNFL) and choroidal thickness in children and adolescents with vitamin B-12 deficiency and no neurological examination finding. Methods The study group includes of thirty-three children aged 8-17 years who were brought to the Pediatric outpatient clinic with the symptoms of fatigue and forgetfulness and whose Vitamin B-12 levels were detected < 200 pg/ml. The control group was the 30 children and adolescents applied to the same policlinic with various symptoms and whose Vitamin B-12 levels were found normal. Children and adolescents with chronic systemic/ocular disease history and myopia or hyperopia more than 4 diopters were not included in both groups. Spectral Domain-Optical Coherence Tomography (SD-OCT) was used for measurements. Results Mean Macular thickness value was 261.2 +/- 17.6 in the Vitamin B-12 deficiency group and 267.7 +/- 17.4 in the control group. Mean value of Retinal Nerve Fiber Layer (RNFL) thickness was 103.5 +/- 7.5 in the Vitamin B-12 deficiency group and 104.3 +/- 8.9 in the control group. The mean values of Choroidal thickness were 360.1 +/- 59.8 and 316.9 +/- 95.4 in Vitamin B-12 deficiency and control groups, respectively. There was a statistically significant increase in choroidal thickness in Vitamin B-12 deficiency group compared to controls. Conclusion Statistically significant increase in the Choroidal thicknesses of children and adolescents with Vitamin B-12 deficiency is important in terms of shedding light on studies that will contribute to a better understanding of the relationship between vitamin B-12 and inflammation

Pollutant footprint analysis for wastewater management in textile dye houses processing different fabrics

BACKGROUND: This study investigated the water and pollution footprints of a dye house, which processed cotton knits, polyester (PES) knits and PES-viscose woven fabrics. Experimental evaluation was carried out for each processing sequence. Variations in wastewater flow and quality were established as a function of the production program in the plant. A model evaluation of wastewater dynamics was performed and defined specifications of an appropriate treatment scheme. RESULTS: The plant was operated with a capacity of 4300 t year−1 of fabric, which generated a wastewater flow of 403 500m3 year−1 and a COD load of 675 t year−1. The overall wastewater footprint of the plant was computed as 91m3 t−1 and the COD footprint as 160 kg t−1 of fabric. Depending on the fabric type, results indicated expected changes in wastewater flow between 600 and 1750m3 day−1 in COD load between 1470 and 2260 kg day−1 and in COD concentration between 1290 and 3400mgL−1. CONCLUSION: A model simulation structured upon COD fractionation and related process kinetics revealed partial removal of slowly biodegradable COD, coupled with high residual COD, which would by-pass treatment. Resulting biodegradation characteristics necessitated an extended aeration system, which could also enable partial breakdown of residual COD. Effluent COD could be reduced to 220–320mgL−1 with this wastewater management strategy. © 2018 Society of Chemical Industr