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Artificial neural network (ANN) modeling of COD reduction from landfill leachate by the ultrasonic process

Arabameri M., Javid A., Roudbari A.

Environment Protection Engineering

2017

Vol. 43, nr 1

59--73

In the study, the use of an artificial neural network (ANN) has been applied for the prediction of COD removal from landfill leachate by the ultrasonic process. The configuration of the backpropagation neural network giving the lowest mean square error (MSE) was a three-layer ANN with a tangent sigmoid transfer function (tansig) at a hidden layer with 14 neurons, linear transfer function (purelin) at the output layer and the Levenberg–Marquardt backpropagation training algorithm (LMA). The ANN predicted results are very close to the experimental data with the correlation coefficient (R2) of 0.992 and the MSE of 0.000331. The sensitivity analysis showed that all studied variables (contact time, pH, ultrasound frequency and power) have strong effect on COD removal. In addition, ultrasound power is the most influential parameter with relative importance of 25.8%. The results showed that modeling neural network could effectively predict COD removal from landfill leachate by ultrasonic process.

An evaluation of water quality from Mojen River, by NSFWQI index

Javid A., Yaghmaeian K, Abbasi E, Roudbari A

Journal of Ecological Engineering

2014

Vol. 15, nr 4

1--6

Water quality index (WQI) is valuable and unique rating to depict the overall water quality status in a single term that is helpful for the selection of appropriate treatment technique to meet the concerned issues. The aim of the study was to evaluating water quality from Mojen River by Water Quality Index based on National Science Foundation (NSFWQI). For this purpose, samples were collected from stations at up, middle and downstream of Mojen River in Semnan province (the biggest river in region) in a 2 years interval of 2013-2014 years. Nine parameters namely Turbidity, Biochemical Oxygen Demand, Dissolved Oxygen, Fecal Coliform, nitrate, pH, temperature, total solids and total phosphate were considered to compute the index. Our findings highlighted the deterioration of water quality in the river due to industrialization and human activities. According to NSFWQI, the best condition was recorded in the Dark haniab (Upstream) and the worst condition concerned the Pole (Midstream).