Effective use of data is crucial to improving the water usage for industrial operations

Jane Ren, CEO at Atomiton, explains that there are technologies available to drastically improve water usage

Water touches every aspect of development and livelihoods, and it links with nearly every Sustainable Development Goal (SDG). It drives economic growth, supports healthy ecosystems, and is essential and fundamental for life itself. So proclaims the preamble to the water security day at COP27. The UN briefing goes on to add that water availability is critical to human activities, ecosystems preservation, poverty reduction, peace, and security. However, water security is far from being achieved, and the pressure on water security is likely to increase in the coming decades.

According to the IPCC Report, without effective adaptation measures, water scarcity will generate severe economic consequences. Companies around the globe are increasingly aware that they must take action to improve their use of water both in volume and quality. When developing a strategy to improve water usage there are two factors to consider, the footprint organizations leave on the environment, and the risk environment it places on a company’s operations or business continuity.

It must also be remembered that there is one crucial difference between carbon and water. Carbon emissions have a global impact, so they can be approached with global decision-making processes. When it comes to water, this is very much a local issue with local problems and consequences. That means that from a corporate perspective, making one decision that applies to all global facilities is difficult because every facility has its unique situation. Some facilities may have too much water and suffer flooding, some may have too little water and suffer from drought, and in other regions, water quality could be poor because of pollution.

When developing a strategy for improving water use performance, there are several stages of the water lifecycle to consider – intake, usage, and discharge. The volume of water taken from the local water resources can be a problem, particularly in regions or times of water shortages. After the water is used in a facility, it is discharged either back into the water stream if it has been cleaned sufficiently or into a waste stream if it does not meet regulatory requirements.

But as with any other process improvement to improve how you use water you must first measure and understand its use in detail and then use that information to develop insights and actions to improve performance. You can calculate your water use in many different manners. For example, you could count the total volume flown through all the processes. Or you can measure the net usage, which is the difference between your intake and discharge.

One of the most significant gaps today is people do not measure and track water; they do not turn data related to water into a metric. When you ask the company how much water they draw from all sources or how much they discharge, they often cannot answer this. They do not gather the data and understand their water balance. So how much do they draw, and how much do they discharge? This information is becoming more critical as it is increasingly required for environmental, social and governance (ESG) reporting. Without access to this information, it is difficult to explain your environmental practices. Organisations cannot create metrics that measure how efficiently they use water and mitigate water-related risks without this information.

In a recent project with AB InBev, Atomiton demonstrated the value of data by using AI-based predictive analytics to improve water use efficiency. At the AB InBev brewery in Guarulhos, Brazil, Atomiton is helping reduce water consumption in bottle washing. The potential for savings is enormous as the company, in 2021, produced 581 million hectolitres of beer. From the scale perspective, all the international breweries use 32 billion glass bottles annually. Atomiton’s KPIs estimate it can reduce about 2.5 million cubic meters of water.

The AB InBev example is only the beginning. The project looked at how much water was used for every bottle cleaned. Unfortunately, this kind of water intensity metric is scarce in manufacturing. There is some raw data available, but they do not measure it. However, the success enjoyed by AB InBev is a compelling example of the power of using data effectively. It is impossible to improve and increase awareness when you do not metricize and measure. However, data can enhance water usage, benefiting the environment, improving performance and profitability, and allowing manufacturers to meet tight regulations around water use.

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