Talbot News


Digital water optimisation, machine learning and artificial intelligence (AI) are bringing about a shift in the way forward-thinking organisations are building their resilience against water scarcity, routine supply disruptions and climbing costs.

An essential move considering that the World Economic Forum, in its latest Global Risk Report, lists water as one of the top risks for doing business in South Africa.

Troubling trends, but not all doom and gloom  “It wasn’t too long ago that water in South Africa was a cheap and seemingly plentiful resource so there was no real motivator for industry to manage it efficiently. Today, most companies find themselves paying up to R80 for every kilolitre (kl) they use and discharge to sewer,” says Talbot water strategist Helen Hulett.

Annual price hikes are hovering anywhere between 12% and 15% and some municipalities are already implementing increases of up to 50% during drought conditions.

Digitisation, the most effective and affordable solution

Talbot is a company with vast experience in helping organisations understand and effectively manage their water risks and has worked with players in the mining, food and beverage, sugar, public utility and industrial sectors to accelerate their transition into the digital water space.

“Industry is rapidly realising that digitisation offers genuine opportunities to boost water efficiency, reduce wastage and contamination, and increase profits,” says Hulett.

And the numbers are significant.

“We’ve found that for a smaller water user that utilises around 20 000kl a month, an easily achievable 20% optimisation equates to a monthly R200 000 in savings.

“That’s R2.4 million a year, and – at a current escalation rate of 13% – equates to more than R15 million in cumulative savings over the next five years,” says Hulett.

For larger, more water-intensive industries, she says, opportunities for savings on water and service charges can run into the billions.

All about the data

Smart water engineer and data scientist Sashnee Naicker explains that Talbot’s digital offering is not just an off-the-shelf software package but rather a service that enables clients to perform continuous optimsation, rectify process inefficiencies that lead to increased water costs, investigate opportunities to harness wastewater recovery and reuse; and address quality concerns.

“Using a combination of internet of things (IOT) sensors, data science, and water expertise, we structure a system to identify the challenges and opportunities that exist – not just in the back and front-end of a cycle but, more importantly, in the process itself,” she says.

Data is gathered, cleaned and stored in a secure, web-based platform known as TalbotAnalytics before data scientists implement machine learning and AI methodologies to harness optimisation and problem-solving value that results in better plant management, operations and decision-making.

“It is all about having consistent, reliable data at your fingertips and the tools and expertise to interpret it and extract value that cannot always be seen at the surface,” says Naicker.

She believes that – based on current water scarcity projections, solutions like these are no longer going to be a ‘nice to have’ but rather a necessity.  

Cost-effective and fully customisable

“There’s a common misunderstanding that digitisation is always hugely expensive and can only be integrated into new-design plants. The fact is that it can be affordable, scalable and completely customisable, and integrated into most existing facilities.”

“We have created a service that blends affordable sensor technology (if it is not already in place) and advice built on decades of intellectual property and experience.”

Talbot runs regular showcases to educate organisations on digital water optimisation and the valuable, and affordable, solutions that are available to help them address their water challenges.

Engineering News – https://www.engineeringnews.co.za/article/machine-learning-a-lifeline-for-big-water-users-2021-03-18

Scroll to Top