Throughout the developed and developing world, water utilities have seen their existing water distribution networks and sources of supply capacities diminish in proportionality to the exponential growth in demand. As such, water utilities practices throughout the world manage water demand via intermittent water supply (IWS). Despite the advent of robust intelligence monitoring technologies being incorporated in their networks via meters, sensors, etc., IWS challenges remain.

 

In Trinidad and Tobago, distribution network operations are configured to suit our circumstances, in that, only a few areas have 24/7 supply, with others served via intermittent water supply (IWS). This research aims to leverage local, existing, and/or emerging technologies to improve the equity of IWS in our existing distribution systems.

 

This work thus looked at using an artificial intelligence (AI) technique, fuzzy logic (FL), to design a decision support system in the first instance for better management of IWS.

The overall architecture and variables of the fuzzy system were developed from data captured via interviews of all levels of personnel at the Water and Sewerage Authority, from operators to managers. These variables included equipment availability, disruptions, network flows, and pressures as inputs, with level of service delivery as the output. Data was collected to support the ranges of the variables used. The system was designed using the fuzzy logic toolbox from the MATLAB software and tested using data from a particular network in the South region.

 

The results showed that the decision support system could mimic the decisions of trained operators in the first instance. Fuzzy logic proved to be a useful tool in designing the decision support system. For this tool to be effective, it would require some level of automation in the distribution network. It does have the potential for providing a consistent service delivery as per the supply available.

 

As there is growing momentum to address traditional and emerging challenges with the nation's water resources, solutions need to be sought. It is thus vital, as the rest of the world starts leveraging the use of technology to improve IWS operations, and that Trinidad does so as well. This work used an AI technique to assist in providing a solution to a local challenge of providing water to our communities. As we are called to find innovative solutions, this work does that for a local challenge.

Mr. Richard N. Ali graduated in 2009 with a Master of Science (M.Sc.) in Industrial Innovation, Entrepreneurship, and Management from The University of Trinidad and Tobago (UTT). Five years prior to this, he graduated with a Bachelor of Science (B.Sc.) in Electrical/ Electronic Engineering with a Major in Power Systems from University of Houston. Richard holds two Teachers Diplomas; one in Technology Education from Mount Saint Vincent University and the other in Teaching, from Corinth Teachers College, both received in 2003. He was also a teacher for twelve years, at both primary and secondary level.

 

Mr Ali is presently a Manager, Plant Production, South West, with the Water and Sewerage Authority (WASA). He has been employed with WASA since February 2007 and has been the Manager of five other departments in WASA, namely, Plant Maintenance, Operations, South East; SCADA; Special Projects; Construction Services, and New Services.

In 2010, Mr Ali was awarded the CEO's most outstanding Case Study Award. Throughout his tenure he has designed and implemented several projects within the field of SCADA, PLCs and automation, including major wastewater projects.

 

During construction of the Abysinnia Gas Processing Plant in Guayaguayare in 2005, he worked as the Field Engineer (E/I) for STORK ELECON Ltd. He also holds the rank of Inspector in the Special Reserve Police, and performs duties on a part-time basis, in the Central Division.