Performance management of small water treatment plant operations: a decision support system

A decision support system (DSS) is developed to optimise the performance of different operations of small water treatment systems to improve day-to-day decisions. The support system includes a data management system, knowledge-based system, performance assessment of different unit processes, fault tree analyses, preventive and corrective actions and event tree analysis (ETA). Performance assessment identifies the critical events (failures) and fault tree analysis identifies the interrelationships between the critical events and the root causes. Fault trees are developed based on the information obtained from events of waterborne outbreaks, responses to questionnaires by the participating smaller utilities, state-of-the-art literature review and personal communication with the operators. ETA is used to identify the potential health outcomes which are further integrated with the quantitative microbial risk assessment. The developed DSS is advanced to an automated user friendly program that can be used by treatment plant operators to assess system performance.

[1]  Solomon Tesfamariam,et al.  Intra-utility performance management model (In-UPM) for the sustainability of small to medium sized water utilities: Conceptualization to development , 2016 .

[2]  Angus R. Simpson,et al.  Expert system for water treatment plant operation , 1996 .

[3]  Ansel Bather,et al.  An expert-hypertext system for water chlorination and chloramination operations , 1998 .

[4]  Solomon Tesfamariam,et al.  Risk-Based Framework for Improving Customer Satisfaction through System Reliability in Small-Sized to Medium-Sized Water Utilities , 2016 .

[5]  Solomon Tesfamariam,et al.  Inter-Utility Performance Benchmarking Model for Small-to-Medium-Sized Water Utilities: Aggregated Performance Indices , 2016 .

[6]  R. W. Gillham,et al.  Walkerton: Lessons learned in comparison with waterborne outbreaks in the developed world , 2002 .

[7]  Paul A. Bosela,et al.  Cryptosporidium Outbreak (Water Treatment Failure): North Battleford, Saskatchewan, Spring 2001 , 2008 .

[8]  J. K. Edzwald,et al.  Enhanced coagulation: US requirements and a broader view , 1999 .

[9]  J. Aramini,et al.  Infectious disease outbreaks related to drinking water in Canada, 1974-2001. , 2005, Canadian journal of public health = Revue canadienne de sante publique.

[10]  James K. Edzwald,et al.  Aluminum Coagulation of Natural Organic Matter , 1990 .

[11]  Gertjan Medema,et al.  Fault tree analysis of the causes of waterborne outbreaks. , 2007, Journal of water and health.

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Bekir Sahin,et al.  Fault Tree Analysis of chemical cargo contamination by using fuzzy approach , 2015, Expert Syst. Appl..

[14]  W E Vesely,et al.  Fault Tree Handbook , 1987 .

[15]  Marc Edwards,et al.  Increasing alkalinity to reduce turbidity , 2000 .

[16]  Tarek Zayed,et al.  Condition Assessment of Water Treatment Plant Components , 2009 .

[17]  M A Hamouda,et al.  Decision support systems in water and wastewater treatment process selection and design: a review. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[18]  S. Hrudey,et al.  Published Case Studies of Waterborne Disease Outbreaks—Evidence of a Recurrent Threat , 2007, Water environment research : a research publication of the Water Environment Federation.