Artificial Neural Network Analysis of Sulfide Production in A Moroccan Sewerage Network

Asmae El Brahmi, Souad Abderafi, Rachid Ellaia


Sulfide in urban wastewater leads to the formation ofhydrogen sulfide and its release in the air. This molecule is anodorous compound, representing an annoyance and healththreat for workers and the nearby population. In order toprevent hydrogen sulfide emission, it is necessary toevaluate sulfide concentration in sewage water and identifyenvironmental key parameters that enhance sulfideproduction. In this study, Artificial Neural Network (ANN)method was used to analyze the presence of this substancein a Moroccan sewerage network. Experimental data ofwastewater composition of Tangier sites (north of Morocco)were used for the training, testing, and validating the ANNmodel. The results showed satisfactory capability of ANN topredict sulfide concentration in aqueous phase, reachingvalue of 89%. Dissolved oxygen and temperature have themost significant impact on sulfide production. The obtainedmodel can be the first step towards monitoring sulfide forbuilding up in sewers and consequently applying it into anappropriate treatment.


Artificial Neural Network, Prediction, Hydrogen sulfide, Wastewater characterization.

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