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

Asmae El Brahmi, Souad Abderafi, Rachid Ellaia

Abstract


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.

Keywords


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

Full Text:

PDF

References


Aguilar, L., Zha, S., Cheng, Z., Winnick, J., and Liu, M. (2004). A solid oxide fuel cell operating on hydrogen sulfide (H2S) and sulfur-containing fuels. Journal of Power Sources, 135(1-2), 17-24.

Boumehraz, M. A., Mellas, M., and Kriker, A. (2018). Study on durability of the concrete of sanitation network in ouargla algeria under the existence of sulphates attack. Indonesian Journal of Science and Technology, 3(1), 11-17.

Delgado, S., Alvarez, M., Rodriguez-Gomez, L. E., and Aguiar, E. (1999). H2S generation in a reclaimed urban wastewater pipe. Case study: Tenerife (Spain). Water Research, 33(2), 539-547.

EL Brahmi, A., and Abderafi, S. (2020). Hydrogen sulfide production assessment based on sewage physicochemical properties using artificial neural network. Materials Today: Proceedings, 27, 3028-3032.

Farobie, O., and Hasanah, N. (2016). Artificial neural network approach to predict biodiesel production in supercritical tert-butyl methyl ether. Indonesian Journal of Science and Technology, 1(1), 23-36.

Firer, D., Friedler, E., and Lahav, O. (2008). Control of sulfide in sewer systems by dosage of iron salts: Comparison between theoretical and experimental results, and practical implications. Science of The Total Environment, 392(1), 145-156.

Godini, H. R., Ghadrdan, M., Omidkhah, M. R., and Madaeni, S. S. (2011). Part ii: Prediction of the dialysis process performance using artificial neural network (ANN). Desalination, 265(1-3), 11-21.

Hamed, M. M., Khalafallah, M. G., and Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling and Software, 19(10), 919-928.

Hughes, M. N., Centelles, M. N., and Moore, K. P. (2009). Making and working with hydrogen sulfide: The chemistry and generation of hydrogen sulfide in vitro and its measurement in vivo: A review. Free Radical Biology and Medicine, 47(10), 1346-1353.

Karaci, A., Caglar, A., Aydinli, B., and Pekol, S. (2016). The pyrolysis process verification of hydrogen rich gas (H–rG) production by artificial neural network (ANN). International Journal of Hydrogen Energy, 41(8), 4570-4578.

Khayet, M., and Cojocaru, C. (2013). Artificial neural network model for desalination by sweeping gas membrane distillation. Desalination, 308, 102-110.

Kouki, S., M’hiri, F., Saidi, N., Belaïd, S., and Hassen, A. (2009). Performances of a constructed wetland treating domestic wastewaters during a macrophytes life cycle. Desalination, 246(1-3), 452-467.

Lambert, T. W., Goodwin, V. M., Stefani, D., and Strosher, L. (2006). Hydrogen sulfide (H2S) and sour gas effects on the eye. A historical perspective. Science of The Total Environment, 367(1), 1-22.

Nielsen, P. H., Raunkjær, K., and Hvitved-Jacobsen, T. (1998). Sulfide production and wastewater quality in pressure mains. Water Science and Technology, 37(1), 97-104.

Nourani, V., Mousavi, S., Sadikoglu, F., and Singh, V. P. (2017). Experimental and AI-based numerical modeling of contaminant transport in porous media. Journal of Contaminant Hydrology, 205, 78-95.

Nourani, V., Elkiran, G., and Abba, S. I. (2018). Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach. Water Science and Technology, 78(10), 2064-2076.

Olden, J. D., and Jackson, D. A. (2002). Illuminating the “black box”: A randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1-2), 135-150.

Parande, A. K., Ramsamy, P. L., Ethirajan, S., Rao, C. R. K., and Palanisamy, N. (2006). Deterioration of reinforced concrete in sewer environments. In Proceedings of the Institution of Civil Engineers-Municipal Engineer, 159(1), 11-20.

Pomeroy, R. (1959). Generation and control of sulfide in filled pipes. Sewage and Industrial Wastes, 31(9), 1082-1095.

Schmitz, J. E., Zemp, R. J., and Mendes, M. J. (2006). Artificial neural networks for the solution of the phase stability problem. Fluid Phase Equilibria, 245(1), 83-87.

Taleb-Ahmed, M., Taha, S., Chaabane, T., BenFarès, N., Brahimi, A., Maachi, R., and Dorange, G. (2005). Treatment of sulfide in tannery baths by nanofiltration. Desalination, 185(1-3), 269-274.

Tumer, A. E., and Edebali, S. (2015). An artificial neural network model for wastewater treatment plant of Konya. International Journal of Intelligent Systems and Applications in Engineering, 3(4), 131-135.

Vasseghian, Y., Zahedi, G., and Ahmadi, M. (2016). Oil extraction from pistacia khinjuk-experimental and prediction by computational intelligence models. Journal of Food Biosciences and Technology, 6(1), 1-12.

Vourch, M., Balannec, B., Chaufer, B., and Dorange, G. (2008). Treatment of dairy industry wastewater by reverse osmosis for water reuse. Desalination, 219(1-3), 190-202.

Zhang, Z., and Friedrich, K. (2003). Artificial neural networks applied to polymer composites: A review. Composites Science and Technology, 63(14), 2029-2044.

Zhang, L., De Schryver, P., De Gusseme, B., De Muynck, W., Boon, N., and Verstraete, W. (2008). Chemical and biological technologies for hydrogen sulfide emission control in sewer systems: A review. Water Research, 42(1-2), 1-12.




DOI: https://doi.org/10.17509/ijost.v6i1.32322

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Indonesian Journal of Science and Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Science and Technology is published by UPI.
StatCounter - Free Web Tracker and Counter
View My Stats