Clean Technology Meets Data Science: News and Articles

Imagine H2O , one of the largest water incubators working across several countries, recently held their startup showcase day. Imagine H2O has been around for several years now and they have funded and helped promising startups in over 20 countries to date. They have several startups in their portfolio that have focused on using combined sensors or robots, machine learning and physical systems to solve problems around the world. In fact, this seems to be the focus of much of the innovation happening in the water tech sector recently! Let’s take a look at some water tech startups from around the world. AInWater : The startup focuses on using machine learning and traditional optimization algorithms to monitor waste water treatment plants and help improve the efficiency of their operations. They’ve been featured in Forbes and have been working with a number of water utilities in South and Central America. CivilGrid: A fascinating company that builds a digital twin of utility lines, enviro...

Digital twins for water, electricity and smart cities have become more than just a buzzword in the past couple of years. As we’ve discussed before, a “digital twin” of any system is when data from the system, whether through sensors, satellite imagery, drones, robots or SCADA systems, are integrated to form a digital representation of what the system looks like over time. While there have always been researchers in academia and the scientific community working on aspects of these systems, we have seen an explosion of interest across all sectors in the past couple of years. Some of that interest is due to Covid, of course. When the shutdowns happened across the world, a key question that was asked of all physical facilities, especially critical infrastructure like water treatment plants, wastewater plants and power plants, was to develop methods that would allow for remote monitoring of these systems. The answer to that question was a combination of sensor data, machine learning both f...

If you are working in the high-tech sector or are at a startup, the first half of 2023 was definitely a roller coaster. We saw venture funding dry up for many sectors; saw the collapse of the Silicon Valley Bank - the bank where many startups and venture capital funds kept their money; and observed layoffs happen in the technology giants - Google, Facebook, Apple and others. In fact, one of the VC firms, Cervin, did an analysis showing that venture funding has been at an all-time low this year, with only 19 mega deals being signed in Q1 2023 compared to 98 the previous year; funding fell by 53% for angel and seed funding and by 30% for Series A rounds. In fact, except for companies focusing on generative AI and AI adjacent technology in the high-tech sector, it has been an exceptionally difficult year financially. How did the clean-tech sector fare? Unsurprisingly, while there were some impacts on startups and their ability to raise additional funds from venture capital firms, the se...

Here's to our beautiful blue dot - with all the mountains, oceans, forests, grasslands, animal and plant life that inhabit it. If there's one thing that we as a species can do, it's doing our best to protect our world - for after all, there is no Planet B.  

How many different materials go into making your smartphone? According to the American Chemical Society, at least 64 different types of chemicals constitute your smartphone . Many of these materials, particularly the heavy earth metals, are extracted from the Earth through mining operations. Mining has been a part of human society for several thousand years now - from the shallow mines of earlier centuries to the more extractive ones created in the last hundred years. The challenge of mining and other extractive industries is that they are essential to many of our needs today - coal for energy, silicon for solar panels and heavy earths for electronics being among the well known ones. At the same time, they cause significant damage to local environments through the disturbance to the land caused by establishing the mine itself, to environmental impacts on water, land and ecosystems from mining operations.   So far, it’s been difficult to map the spatial extent and environmental impa...

What are some of the interesting startups in the water sector this year? Water tech typically does not get funded at the same rate as other clean technology sectors - primarily because the time to achieve an acceptable ROI for venture funds is usually longer than the lifecycle of the fund. However, there are still VCs who are interested in funding the water sector and there are always startups that find government funding, partnerships and other ways to build and grow. Let’s take a look at some startups that are combining data science and water to help solve interesting problems! 1. Ainwater , headquartered out of Chile, uses AI-based algorithms to optimize water and wastewater plant operations, ensuring compliance while increasing energy efficiency by 30%. 2. CivilGrid , in the San Francisco Bay Area, helps collect all the geospatial and regulatory information required for water companies and other infrastructure companies to plan their operations. 3. IFlux , out of Belgium, uses a co...

In April, the IPCC published the next installment of reports on the state of the planet . Several alarms on the state of the planet were sounded - and yet, most researchers were concerned that the report did not go far enough. Why was that? One of the challenges with synthesizing science across multiple areas and bringing it to the point where policy makers can make decisions that follow the science is that the latest research often doesn’t make it into these reports. That’s because the research is cutting-edge, which by definition means that new discoveries are happening and the science is not yet at the point where clear recommendations can be made. Second, with cutting-edge science, researchers are still discussing the nitty-gritty details of what the processes are, what’s driving them and how the system is functioning. That makes it much more difficult to reach a scientific consensus on the topic.   So, what is one such area of cutting edge research in climate science? Climate ...

It's the end of 2022 and we have another few weeks left before our cohort finishes the course on "Introduction to Machine Learning and Data Science in Water and Wastewater". We've had so many amazing people take this course and discover new insights into what's happening with the data at their water utility or organization. As we've been building our courses for individuals, we've learnt so much about what is useful to utilities and people working in the space.  So for 2023, we are going to be taking a short 4-month hiatus where we are focusing on building some new and amazing courses that will help professionals in the space learn more of these skills. We're always looking for feedback, so please do email us while we are building our new courses!

 We had such a terrific response to our first ever  " Introduction to Machine Learning and Data Science in Water "  course, that we've decided to open it up for registration for the second time. If you're in the water sector, are curious about machine learning and data science and not sure how to get started using it in your work - join our course and we'll give you the tools! This is the second time we're doing a 10-week, cohort-based course and we plan on doing more of them in the future. So, what does our asynchronous, cohort-based course do for you? One of the challenges with completing courses online is that it's so easy to sign up for one, get busy with life and work, fall behind and then give up on finishing the course that you signed up for. Our cohort-based and asynchronous approach helps you overcome that. The course material is online - so you get to complete it and review it when you get the time. However, we also have weekly office hours that ...

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of c...

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !

 We're delighted to welcome our first ever cohort to our " Introduction to Machine Learning and Data Science in Water " course. If you're in the water sector, are curious about machine learning and data science and not sure how to get started using it in your work - join our course and we'll give you the tools! This is the first time we're doing a 10-week, cohort-based course and we plan on doing more of them in the future. So, what does our asynchronous, cohort-based course do for you? One of the challenges with completing courses online is that it's so easy to sign up for one, get busy with life and work, fall behind and then give up on finishing the course that you signed up for. Our cohort-based and asynchronous approach helps you overcome that. The course material is online - so you get to complete it and review it when you get the time. However, we also have weekly office hours that are live - so you get to ask your questions to our experts, have th...

Last time we looked at how machine learning can help water utilities manage their maintenance and operations efforts - especially when dealing with hard-to-reach parts of the water system like buried water pipes. Today, let’s talk about how machine learning is being used in developing new technologies and building prototypes for decentralized, small-scale systems. Desalination has been studied and deployed at scale for several years now. As different parts of the planet face increasing water stress, desalination is being evaluated as one of several potential solutions - together with water conservation and recycled water. In the Middle East of course, large-scale desalination plants have been in operation for several decades, with Israel being one of the countries at the forefront of developing and implementing the technology. Large-scale desalination plants have the advantage of scale - you can build a single system and then connect it to your existing water network. Sufficient data a...

When are machine learning and data science useful in the water sector? Are they useful if you have a large system with lots of data? Or are they useful if you’re looking at small-scale, decentralized systems?   The answer, as you might have guessed, is both. The difference is in the type of tools and algorithms being deployed and the results that are being sought - but, in both cases, machine learning and data science provide invaluable help in getting people access to clean, safe drinking water. Let’s take a look at two very different applications of water tech. One is for a water utility that is attempting to improve its systems to ensure that clean, safe water continues to flow to the citizens of the city and the other is for a small, rural community that needs cheap, reliable access to clean water. Interestingly, while the goals and requirements of both these applications are very different, the thread that connects both of them is machine learning. Today, we’ll talk about the ...