In 2018, Cybera’s Data Science for Albertans team partnered with local startup Synauta on a two-week pilot. At the time, founder Dr. Mike Dixon shared an office space with Cybera, and we learned about his new company over conversations in the shared kitchen. We jointly realized there was an opportunity to trial the use of data science methodologies, which have now become central to Synauta’s platform.
Today, Synauta uses machine learning techniques to determine ways to reduce the energy used in “reverse osmosis desalination” systems. These systems use pressure to remove the salt and other mineral components from ocean or groundwater, which is used in areas without access to fresh water, but can be very energy intensive.
In this post, we spoke with Dr. Mike Dixon, CEO, to find out how Synauta was able to use data science to speed up the delivery of its final product.
What does Synauta do?
Synauta is a “cleantech” startup that is actioning machine learning technology alongside desalination plant operators (pictured below). Our goal is to treat more water with less energy and less chemicals, and save customers up to 20% on their operational costs.
Why is this technology so important?
The global demand for water and energy is rapidly increasing, and desalination is the only climate-independent source of water. However, this process uses *a lot* of energy and chemicals. Plant operators do not have time to optimize a desalination plant as they need to respond to daily alarms and prioritize the supply of water. These challenges, combined with machine learning gaining traction, mean utilities and industrial desalinators are seeking out new technology solutions to run plants under more optimal conditions.
What value is Synauta bringing for Alberta?
Synauta is bringing a new export market to Alberta. While our solution has applications for remote reverse osmosis users in First Nations communities and municipalities, the majority of our users are water-short countries or island nations.
How did Cybera help Synauta?
In our very early stages of development, Synauta worked with Cybera on a two-week sprint cycle to develop our first round of machine learning to achieve a proof of concept. Cybera helped our team assemble data, and then split the work into two potential solutions, which we both explored.
During the sprint we communicated daily and, at the end of the process, Cybera presented two scenarios, each showing clear benefits for desalinators.
Cybera helped Synauta learn more about the broader potential for machine learning and about various algorithms that we could use. We were also able to present the findings of the process, along with other information, to IRAP and Alberta Innovates to apply for further funding to develop our minimum viable product.
We were surprised by how quickly the Cybera team was able to learn about the desalination domain and deliver meaningful results. We were particularly pleased with the level of physics understanding on the team, which accelerated the sprint work in a meaningful way.
Is Synauta and Cybera working together on any other projects?
We continue to work together through the training programs Cybera offers to people wanting to improve their data science abilities. Synauta is proud to support these programs through attending demo days and advising data scientists who are seeking employment.
How is Synauta progressing?
We are now in the process of testing our software across a handful of users in several countries. We are seeing real world energy savings and attracting great attention in the desalination market!
If your organization is interested in running a four-week sprint with Cybera’s Data Science For Albertans team, visit our website to learn how to get involved.