Modeling Different Types of Constructed Wetlands for Removing Phenol from Olive Mill Wastewater using an Artificial Neural Network

Arda Yalcuk

Ekoloji, 2013, Issue 88, Pages: 28-35



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An Artificial Neural Network (ANN) was developed to model phenol removal in vertical and horizontal constructed wetlands. For this, a pilot scale horizontal-flow (planted and unplanted) and three vertical-flow (planted and unplanted) reactors made of PVC were designed. In these reactor systems two wetland plants, Typha latifolia and Cyperus alternatifolus, and different bedding media were used. The reactors were operated for 89 days.
A feed-forward network was used to model the wetlands. The network was fed with two subsets of operational data. The training procedure for effluent phenol concentrations from different wetlands was quite successful; a perfect match was obtained between measured and calculated concentrations. The ANN model was competent in providing a reasonable match between the measured and calculated concentration of phenol for the following scenarios: Horizontal flow with Cyperus (R2=1 for HF planted and R2=0.79 for HF unplanted), vertical flow with Cyperus (R2=0.99 for VF1 planted and R2=0.99 for VF1 unplanted), vertical flow with Typha (R2=0.99 for VF2 planted and R2=0.99 for VF2 unplanted), and vertical flow with Cyperus and Typha (R2=0.99 for VF3 planted and R2=0.99 for VF3 unplanted) in the effluents of the constructed wetlands. ANN can easily be applied to improve the performance of horizontal and vertical type constructed wetlands.


Constructed wetlands, olive mill waste, phenol, artificial neural network, feed-forward network


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