Romanova, Anna, Faramarzi, Asaad, Mahmoodian, Mojtaba and Alani, M. Amir (2014) An Evolutionary Polynomial Regression (EPR) model for prediction of H2S induced corrosion in concrete sewer pipes. In: 11th International Conference on Hydroinformatics (HIC 2014), 17-21 Aug 2014, New York City, USA.

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Abstract

The sulphuric acid is a known growing threat to concrete sewer pipes. Acid production is dictated by rapid urbanisation, increased use of hot water and discharge of toxic metals and sulphate containing detergents into the wastewater. Concrete sewer pipe corrosion due to sulphuric attack is known to be the main contributory factor of pipe degradation. Very little tools are available to accurately predict the corrosion rate and most importantly the remaining safe life of the asset. This paper proposes a new robust model to predict the sewer pipe corrosion rate due to sulphuric acid. The model makes use of a powerful Evolutionary Polynomial Regression method that provides a new methodology of hybrid data-mining. The results obtained by the model which was validated in the field indicates that the proposed hybrid methodology can accurately predict the corrosion rate in concrete sewer pipe’s given that the pipe installation conditions as well as in-pipe sewage conditions are known.

Item Type:	Conference or Conference Paper (Paper)
Additional Information:	[1] This paper was first presented at the 11th International Conference on Hydroinformatics (HIC 2014) held from 17–21 August 2014 in New York, USA. It was given within Session R56: Water Distribution Networks: Infrastructure Health on 18 August 2014.
Uncontrolled Keywords:	EPR, corrosion, sewer pipes, sulphuric acid
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TD Environmental technology. Sanitary engineering
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Engineering (ENG) Faculty of Engineering & Science
Related URLs:	Organisation Organisation
Last Modified:	30 Jan 2017 15:11
URI:	http://gala.gre.ac.uk/id/eprint/11438

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