Design and implementation of a performance assessment methodology cognisant of data accuracy for Irish wastewater treatment plants

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Abstract

Without intervention, wastewater treatment plants will become more resource intensive as they strive to meet environmental regulations. This challenge is set against the backdrop, in many countries, of an emphasis on cost reduction and increasing concern regarding the sustainability of the sector. It is imperative that tools and methodologies are developed that allow the wastewater sector to measure resource efficiency and benchmark performance in a standardised and efficient manner. Existing benchmarking systems can offer detailed analysis of many aspects of wastewater treatment. However, these systems do not offer a means of assessing the accuracy of the data used for performance assessment. This paper presents and pilots a unique performance assessment methodology for wastewater treatment plants which addresses these challenges; enabling stakeholders to (i) benchmark a wide variety of wastewater treatment plants in an efficient and standardised manner (ii) identify data accuracy issues and (iii) isolate where and how resources are consumed during wastewater treatment. The methodology is implemented within an adaptive toolkit that can enable benchmarking of wastewater treatment plants with varying capacity, technology, sampling frequency, data accuracy and management practices. The methodology was piloted at a number of wastewater treatment facilities and enabled identification of data availability and accuracy issues which can significantly reduce the effectiveness of benchmarking. The toolkit links resource efficiency to operational performance (e.g. contaminant removal rates) at the wastewater treatment facility to provide a more holistic representation of operational efficiency.

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APA

Doherty, E., McNamara, G., Fitzsimons, L., & Clifford, E. (2017). Design and implementation of a performance assessment methodology cognisant of data accuracy for Irish wastewater treatment plants. Journal of Cleaner Production, 165, 1529–1541. https://doi.org/10.1016/j.jclepro.2017.07.083

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