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

A scientific approach to wastewater recovery and reuse in the textile industry

Wastewater recovery and reuse in industries requires all the basic steps of quality management. It should involve a comprehensive in plant survey of processes with wastewater generation, identification of recoverable streams, and treatment requirements for reuse. It should equally undertake evaluation of wastewater quality remaining after segregation of the recovered portion, with specific emphasis on technological implications of appropriate treatment and compliance with effluent limitations. In this study, all these factors were experimentally assessed and evaluated for a knit fabric processing textile plant.</jats:p

Application of sequencing batch biofilm reactor for treatment of sewage wastewater treatment: effect of power failure

The operational performance of the sequencing batch reactor (SBR) and sequencing batch biofilm reactor (SBBR) for treating the university campus wastewater was evaluated. The effects of power failure on performance of processes were investigated by comparing chemical oxygen demand (COD) and total suspended solids (TSS) removal, sludge settling properties and microorganism's morphological properties by using SEM photos. The experiments were carried out at four 2-L reactors made from plexiglas. Three in four reactors were operated as SBBR. SBBRs were filled with the kaldnes biomedia K1 to 40, 50 and 60% of the volume of empty reactor. SBR and SBBRs were operated at 6/24 h cycling periods on a day that consisted of wastewater fill (30 min), reaction (4 h), settling (1 h) and draw (30 min), summed up to 6 h with the hydraulic residence time of 7.5 h. The effect of filling ratio on SBBR performance was also determined. In normal operation, average COD removal rates were calculated as 86, 88.5, 90.6 and 94.2% for SBR, SBBR1, SBBR2 and SBBR3, respectively. Power failure is one of the most encountered problems in the small wastewater treatment plants. Its effect was observed at 1 cycle as short term and 4 cycles as long term. Besides the negative effects of the power failure on COD and TSS removal, it also affects sludge settling properties. While interruption time is increased, recovery took much more time than expected to reach steady state conditions for all the reactors. However, the presence of biofilm restricted the adverse effect of power failure. SEM photos and better effluent quality supported these findings