Abstract
Treatment of petroleum refinery wastewater using anaerobic treatment has many advantages over other biological method particularly when used to treat complex wastewater. In this study, accumulated data of Up-flow Anaerobic Sludge Blanket (UASB) reactor treating petroleum refinery wastewater under six different volumetric organic loads (0.58, 1.21, 0.89, 2.34, 1.47 and 4.14 kg COD/m3•d, respectively) were used for developing mathematical model that could simulate the process pattern. The data consist of 160 entries and were gathered over approximately 180 days from two UASB reactors that were continuously operating in parallel. Artificial neural network software was used to model the reactor behavior during different loads applied. Two transfer functions were compared and different number of neurons was tested to find the optimum model that predicts the reactor pattern. The tangent sigmoid transfer function (tansig) at hidden layer and a linear transfer function (purelin) at output layer with 12 neurons were selected as the optimum best model.
| Original language | English |
|---|---|
| Pages (from-to) | 2077-2082 |
| Number of pages | 6 |
| Journal | Research Journal of Applied Sciences, Engineering and Technology |
| Volume | 6 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 01 Jan 2013 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Engineering(all)
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Optimization of anaerobic treatment of petroleum refinery wastewater using artificial neural networks. / Ahmed Salih, Gasim Hayder; Kutty, S. R.M.; Isa, M. Hasnain; Alemu, L. T.
In: Research Journal of Applied Sciences, Engineering and Technology, Vol. 6, No. 11, 01.01.2013, p. 2077-2082.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimization of anaerobic treatment of petroleum refinery wastewater using artificial neural networks
AU - Ahmed Salih, Gasim Hayder
AU - Kutty, S. R.M.
AU - Isa, M. Hasnain
AU - Alemu, L. T.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Treatment of petroleum refinery wastewater using anaerobic treatment has many advantages over other biological method particularly when used to treat complex wastewater. In this study, accumulated data of Up-flow Anaerobic Sludge Blanket (UASB) reactor treating petroleum refinery wastewater under six different volumetric organic loads (0.58, 1.21, 0.89, 2.34, 1.47 and 4.14 kg COD/m3•d, respectively) were used for developing mathematical model that could simulate the process pattern. The data consist of 160 entries and were gathered over approximately 180 days from two UASB reactors that were continuously operating in parallel. Artificial neural network software was used to model the reactor behavior during different loads applied. Two transfer functions were compared and different number of neurons was tested to find the optimum model that predicts the reactor pattern. The tangent sigmoid transfer function (tansig) at hidden layer and a linear transfer function (purelin) at output layer with 12 neurons were selected as the optimum best model.
AB - Treatment of petroleum refinery wastewater using anaerobic treatment has many advantages over other biological method particularly when used to treat complex wastewater. In this study, accumulated data of Up-flow Anaerobic Sludge Blanket (UASB) reactor treating petroleum refinery wastewater under six different volumetric organic loads (0.58, 1.21, 0.89, 2.34, 1.47 and 4.14 kg COD/m3•d, respectively) were used for developing mathematical model that could simulate the process pattern. The data consist of 160 entries and were gathered over approximately 180 days from two UASB reactors that were continuously operating in parallel. Artificial neural network software was used to model the reactor behavior during different loads applied. Two transfer functions were compared and different number of neurons was tested to find the optimum model that predicts the reactor pattern. The tangent sigmoid transfer function (tansig) at hidden layer and a linear transfer function (purelin) at output layer with 12 neurons were selected as the optimum best model.
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U2 - 10.19026/rjaset.6.3827
DO - 10.19026/rjaset.6.3827
M3 - Article
VL - 6
SP - 2077
EP - 2082
JO - Research Journal of Applied Sciences, Engineering and Technology
JF - Research Journal of Applied Sciences, Engineering and Technology
SN - 2040-7459
IS - 11
ER -