with ๐๏ธ Brian Moloney โ Founder & Managing Director @ StormHarvester
๐ง StormHarvester is the leading company in smart drainage systems (and more).
This episode is part of my series on Water Digitization โ check it out! ๐
What we covered:
๐ How a simple, sturdy yet brilliant concept could have saved Queensland 3 billion pound worth of infrastructure damage
๐ How hyperlocal weather forecasts and existing network sensors make for a perfect combination if you leverage them cleverly
๐ How and Why machine learning outperforms modeling
๐ How it can save utilities tremendous money by preventing sewer blockages long before they happen
๐ How better rainwater management is an opportunity for greywater reuse and landscaping
๐ How actuation of networks might be the next frontier
๐ On-Premise Vs. Cloud Solutions
๐ Brianโs Vision, Stormharvesterโs Mission & the role that Venture Capital plays in the Water Industry
๐ฅ โฆ and of course, we concluded with the ๐ง๐๐ฅ๐๐ ๐๐๐ง๐ ๐ฆ๐ช๐๐จ๐ฉ๐๐ค๐ฃ๐จ ๐ฅย
Teaser:
Resources:
โก๏ธ Send your warm regards to Brian on LinkedIn
โก๏ธ Visit StormHarvesterโs Website
is on Linkedin โก๏ธ
Full Transcript:
These are computer generated, so expect some typos ๐
Antoine Walter:
So, hi, Brian, welcome to the show. Hi, how are you doing well? Iโm very good. Thank you. Let me start with a postcard. Actually. Itโs the first time I have the pleasure to welcome someone from Ireland. So youโre in Belfast right now. What can you tell me? That would be crispy about Belfast.
Brian Moloney:
Itโs actually quite a nice day here today. Itโs 12 to 15 degrees, which is unseasonable for us. So the window is open here a bit and I can actually see the sun coming in for the first time this year. So itโs a beautiful environment to be doing the podcast from.
Antoine Walter:
So I hope you have no hard feelings, you know, Iโm French youโre in Ireland. I think for the first time, since a decades, friends won against Ireland in rugby. So I hope you donโt mind if I brag a bit. No.
Brian Moloney:
So weโll give you this one. Weโll be back again next year.
Antoine Walter:
Itโs letโs say very probable. So letโs go to the matter. Actually. I was looking at your path and I was noticing that you started up in letโs say hardcore engineering, civil engineering, hydraulic environmental engineering, and now youโre more on the clouds, artificial intelligence. So thatโs kind of a transition from hard to soft from far, but can you guide us through your steps?
Brian Moloney:
Yeah, so I think it all goes back, I suppose, at the start to my university days. So I went to Trinity college in Dublin and actually I did a course that allowed us to study different disciplines of engineering. So I studied both civil engineering and also some software engineering for the first number of years, and then specialized into the area of civil engineering and drainage and hydraulic modeling and everything associated with that. But it gave me a good, broad understanding for the initial years of the software and the building of software and how that might be applied to stormwater and wastewater drainage networks. So I think it all started maybe back then, and then it developed them to my time in Australia was really where it came to the forefront. Then
Antoine Walter:
Actually your times in Australia, according to what I read on your website, itโs the, the time and place where you had your aha moment, which led you to creating actually your companyโs Stromharvester. Can you tell us something about this aha moment first that we understand that one? And then can you explain me how you have these aha moments down under and you create your company in the UK?
Brian Moloney:
Yeah, absolutely. I suppose I was graduated in Ireland as a civil engineer and started to work initially in drainage, but very quickly we fell into 2006 and 2007, 2008 and the global recession. So in that instance, there was quite a lot of work happening in Australia around engineering and civil engineering. So I decided to go for my career and to move to Australia. I actually started living outside of Brisbane in Queensland, and it was just 2009 when I started moved over here and, and started to get established only a couple of months when Iโd been living there, there was a, a huge catastrophic event called the Queensland floods, where there was 3 billion pounds worth of damage done to the infrastructure of Queensland. And I was living in Queensland at this time outside of Brisbane. So one of the things that struck me when I was there, the flood wasnโt just a one day event.
It happened over the course of about a month. It was continued high rainfall, which would come intermittently every couple of days. But the thing that really confused me is that we knew the rainfall was coming a day or two in advance because we could see from the accurate local forecast information that they had in Australia at the time, but we couldnโt do anything about it as engineers. At the time I was working as a drainage engineer for a local council in Australia. And what we used to do is essentially we would go around to all the housing developments and we would ask all the individuals in the housing developments, could they please empty any of their storm tanks or their rainwater harvesting tanks like a day before the big rainfall would come. And I just taught as, as an engineer, this was crazy, such a reactive way to try and solve a problem of trying to mitigate the first impacts of the heavy rainfall.
So it was actually around the same time in Australia when I got my first iPhone. And I couldnโt understand how there could be so much technology in this little device and then our infrastructure, which is the thing that protects us against these real environmental catastrophes is so reactive, literally everything in the stormwater and the drainage networks in Australia and every country in the world was the same at this time was reactive. So something would happen and then the drainage network would react to me. It just seems so silly that you would have infrastructure there when you have technology available in the world that you could have this really clever, predictive drainage network. And it would be able to see the rainfall that was coming in the coming days. It could understand what that would mean for a pumping station in a sewer network or for a flood tank or a reservoir, and we could drop levels.
And it just seemed that there was such a lack of use of technology in civil engineering compared to how other industries had developed like oil and gas and other elder industries had raised ahead embracing the internet of things and different technologies, subsequently machine learning. So that was really where it came from. It was just so the story was when we were working as part of the council, we were going out and we were actually manually knocking on a door and asking somebody, can you empty your flood tank tonight? Are your rain water harvesting tank tonight? Because thereโs a big rainfall event coming tomorrow. So it started to work in my garage and actually built a little device. That was a little really basic internet of things, connected device that would open and close a small valve based on first of all, an automatic command that I could give it a remote command, link that up to a web portal where I could then open and close the valve from the web portal.
And I suggested to the council, instead of us actually going around and manually opening the valves, why donโt we issue these valves to everybody? And then we can do that remotely from the control center. And then once we got that idea embedded, I started to explore the idea of, okay, why donโt we automatically link these valves to a forecast platform, which would allow us to actually drain down the tank remotely. If we knew that was one of these big storm events coming and they happen regularly, you know, that one in 2011 was a particularly serious rainfall event, but in Australia itโs such a seasonal climate in parts of Queensland. They get this heavy rainfall every year or something similar. So, so this was a repeated problem that they had, but they had no technology solution for us. So really the first, the first, it wasnโt even a company at that stage. It was just developing a solution to solve a problem that I saw in the market. And it was just to help out the council that I was working with. It wasnโt necessarily to try and go and really push a commercialization or anything like that. But that was really the first instance. It was seeing all this other technology that could affect and that could help us and we werenโt using it. So that was the Genesis of the idea.
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Antoine Walter:
So you were scratching your own itches, which is probably the best way to come out with something, but that was still in Queensland. And you brought that back with you to Dublin or to dub, to, to Belfast. Where did you come back and why did you come back
Brian Moloney:
To Belfast! Yeah, so I was working there and we were getting a lot of success with that project, but ultimately once the recession had sort of started to ease and construction had started back in Ireland, there was a need for engineers back in Ireland, again. So from Australia, I contacted the university, Queens university in Belfast, and we started with doing a remote project initially. And then I moved over to do a closer project with the university as they were very interested in this type of technology and how it could be applied. And, and we started to get some students doing finally a projects. And then we ultimately developed out into, as a student doing a PhD, ultimately on this type of solution. So really there was a strong interest from Queens university to help me develop the concept. And, and they saw the potential. There was for these types of solutions and then, you know, just a desire to, to personal desire to come closer to home.
Antoine Walter:
So that means that your original product or the original service you were bringing is your Stormwater solution that you have today still in portfolio. So that was the origin of the company. Is it still the heart of what you do today because you also have the other part, which is linked to the networks. So what is, do you have a preferred child? Thatโs a difficult question.
Brian Moloney:
No, I donโt. I donโt think we have a preferred child. So the product is itโs been quite a successful product with us now, and weโve got lots of installations of that product around the UK. Weโve got a really strong partnership with the company Wavin, and weโre using Wavin flood to install these smart valves around different projects around Europe. And some of those projects are fascinating, fascinating applications of this new smart technology, really, really optimizing what we can do with stormwater networks. What we started to look at then was that, you know, weโre doing this at localized flood tanks for helping municipalities to, to manage their flood tanks or their storm tanks. And we started to look at the utilities and how technologies like this assist water utilities. So we started to look at drainage networks that might have some infiltration, or might have some storm water coming into them.
We started to look at wastewater networks and the influence of stormwater on wastewater networks. And what we found is that certain situations, the wastewater network, the flow could increase 10 to 20 times during periods of high rainfall on certain areas of combined source. So we said this predictive system that we had built to link infrastructure to the weather forecast, it had to be useful also to utilities because they have a lot of the same problems with their infrastructure being very reactive. So we started to look at Don and we found when we went to talk to utilities that they already had sensors installed all over their pipe networks, that pumping stations and CSO points, combined sewer overflow locations. And they already had a network of sensors, but everything was still reacting. So we started to bring that predictive and smart technology into the utilities and applying that in various different ways, anywhere where we see an opportunity to improve the performance of the drainage network using our predictive technology is something weโre going to be interested in helping a utility to do
Antoine Walter:
Letโs deconstruct that thereโs two elements here, which I find very interesting the first and weโll conclude with the first two, itโs a do it reverse. But the first is you mentioned that there are already a lot of sensors in place on the networks. And I was wondering if thatโs the case on all the networks or if thatโs really the advanced utility that has all these sensors. And the second is this element of the forecast, because as an outsider, letโs say for me, really, for the stupid, I have no clue about how you might program all of that. And I might think that to start with, I wouldnโt start with hyper-local weather forecast. I would do a sturdy, simple thing that might eventually work at 5% off of its potential, but just to do a proof of concept, but apparently you had it embedded from the very beginning. So it was part of your thatโs used the big names of your minimum viable products. To have these hyper-local weather forecast, how easy is it? Does it sound much more complicated than what it really is? Because to me that sounds really like, like complex.
Brian Moloney:
It is, and it can be very complex, but you can simplify it as well in the description and how you explain it. So the hyperlocal forecast information is absolutely critical to drainage networks. So it has to be a key component of what we do. So weโve, up-skilled ourselves and weโve upskilled all of our professionals that are working with us in understanding the different forecasts that are out there, what the abilities of different forecast providers are, how we can get this forecast information into the correct format, how we can use us and then how we critically, how we can link it to different points around the drainage network. So if you take, for example, a sewer network, so take the city that youโre in. If you look at the sewer network of the city that youโre in, there is no point in us predicting that there is going to be rainfall across the entire city for a couple of hours.
So say weโre going to get 10 mils of rainfall tonight, and then it will be gone tomorrow across the whole city to have benefit. We have to know exactly whatโs coming at exactly what part of the city at what time. So then we can start to do something really smart and move the water around or the stormwater or slow it down. I really good analogy. I like to put out there for is could you imagine driving into your city tonight? If there was no traffic lights anywhere, it would be chaos. Everybody wouldnโt know at what junction to move and how to travel around. Thatโs what the drainage networks are below our city. At the moment, there is no smarts. Thereโs no predictive in there. So itโs critical to put this predictive and the smart technology and you can get so much benefit from doing that. Itโs basically like running a city with traffic loads or without traffic lights.
Antoine Walter:
So it is mandatory or itโs, it brings a lot of value that I get it pretty easily. But whereโd you get your data from you have a partnership with a local weather company, or do you do that yourself? Is it data that is open source freely available to everyone? How can I imagine that your weather data, whereโd you get that from?
Brian Moloney:
So weโre not weather forecasters, or weโre not a meteorological agency and ourselves. So what we do is weโll partner with various different meteorological areas in different countries, and sometimes multiple providers in countries. Some providers are good at giving really short term, maybe up to six hours, rainfall for customer go to the meat, more longer-term stuff. Some are good at hyper-local. So itโs by combining various different weather for cost providers all into the mix, all into one system, and then being able to pull out the correct information for the correct asset at the correct time in real time, and be able to do that quickly and make calculations on that quickly. So weโre not experts in predicting the weather, but we are experts in putting various different prediction sources together, putting our expertise at top of them and taking that information and making it relevant to storm water and drainage networks.
Antoine Walter:
Okay. So thatโs very clear, which brings me to my first part of the question, which is the sensors on the network, because you have this data of the weather and the other part of the data is what you get from the network. How common is it to have sensors on the networks? Itโs really everywhere in the UK. You would see those sensors everywhere in Europe, everywhere in the world, or is it really the highest end utility thatโs have their networks fully equipped with sensors?
Brian Moloney
I suppose it all depends. It comes down to how many sensors do you need to make a system like this work? And the bottom line is not as much as what people think they need. Thereโs a real drive over the last number of years to put more and more and more sensoring into storm water and wastewater networks. Itโs not always the right thing to do. You need to, first of all, understand the data that your existing sensors are giving you. So what existing sensors do you have in a network? So a wastewater network is made up of several different components, so that thereโll be pipes, thereโll be pumping stations, and thereโll be usually outfall pints or combined sewer overflows or sewer overflow points where you discharge from. Usually the pumping stations will be sensored. So thereโll be some sensoring and the pumping stations. Cause, cause do you tell him he needs to know how the pumps are operating?
If theyโre deteriorating, if theyโre running well, not running well. So this is the first point you will have monitored. So this is, this is a basic, and this is in pretty much all utilities. The next step then is to CSO as the combined out the outflow points across the network. And usually utilities will have some sensoring at their overflow points. In most instances, if not, they will have a plan over the next number of years to put sensors in there. Because if theyโre going to have some discharge to the environment, they need to know the regulator or the environmental agency of the country will usually want to understand how much theyโre going to discharge, where theyโre going to discharge if theyโre discharging so that they understand, if there will be an, a consequence to the environment of the storm discharge. And then the really sophisticated utilities might supplement this with some sewer level sensing around different areas, particularly maybe areas where theyโve had some blockages or areas where thereโs had some issues in the network. Our system can work once weโve got the pumping station. So we can work at basically level one, censoring level, two censoring, itโs going to optimize. Youโre going to be able to do more and level three years to all singing all dancing system, where you could really, really develop out your benefits.
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Antoine Walter:
You mentioned your system, and thatโs now the part where reading your case, studies, reading your documentation. I wasnโt able to understand exactly how that works because on that same microphone I had an interview with with Kando I donโt know if you know them can do is putting some sensors in the network and then they do modeling. And from what I understand from your technology, youโre not modeling you do something, which according to what I read is even better. So how do you do to process this data if youโre not modeling?
Brian Moloney:
Yeah, thatโs really interesting. So I have a confession to make, so my background is actually hydraulic modeling. So thatโs what I did for a lot of the time I was working as a consultant. So Iโve come from the industry of hydraulic modeling. Nobodyโs perfect. So the big difficulty, the hydraulic modeling has, is in terms of trying to apply that to real time. Itโs very, very difficult to run the complex hydraulic models that are required in real time to actually give us accurate information in real time so that we can make operational decisions. Cause an operational decision will need to be made. You know, if youโre making it in 15 minutes time, that might be too late because the, you know, in a pumping station, typically itโll turn on maybe after 10 minutes and turn off after five minutes. So you could miss two cycles of the pump.
If youโre a 15 minutes or a half an hour doing your calculation or your collaboration, as well as that, I think most people in the industry understand that hydraulic models are theyโre inherently inaccurate and itโs difficult to calibrate them continually in real time to make sure theyโre always calibrated. So what we do is we actually use machine learning as an alternative. If youโre looking to make operational decisions as to how you should run your network in real time, we believe that machine learning is a better solution than a detailed hydraulic model to do this. So the reason for this is you take the actual data from the actual site, from the sensors in the site. And we use this, we build a machine learning model around the actual data, also using the hyperlocal forecast. So a time series of the information that youโre getting from the sensor time series of the forecast information, also critically, any other influencing data that you think might be influencing the level of the sewer or the pattern of the sewer, like local borehole level for perhaps cause you might have some groundwater infiltration into a sewer network, like maybe a load the river river network.
And I can tell you some fascinating examples of where weโve added different information into the feature set up the machine learning. And we were able to really get our prediction window really, really, really accurate on the back of dash. The theory here is that if you have a sensor in the network, you should use this sensor not to collaborate a hydraulic model, but actually to predict whatโs going to happen at that sensor point, based on the data you get from that sensor and some, some form of sophisticated calculation, like an AI model or machine learning model. So really thatโs the Genesis. Iโm not saying we donโt need hydraulic models as an industry. If you want to build another hundred houses on the outskirts and you want to understand how your whole network is going to be affected, hydraulic models are really good for that. And that thatโs really what theyโre required for. If you want to change the behavior of your infrastructure operationally in real time, we feel strongly and weโve seen the results that machine learning models with sensors are a better way to do that. Thatโs our fundamental principle of the offering weโre bringing into the utility sector. So
Antoine Walter:
The data is gathered is reconciliate it with this weather forecast, data feeds into machine learning and artificial intelligence. But where does all of that happen? Is it a cloud platform? Is it an on premise?
Brian Moloney:
Yeah. So most often we can do on premise solutions, but most often weโll do it in the category recommend thereโs huge advantages to your data processing, to being able to scale up different data processing applications in real time. And then scale back down again, we would recommend strongly that you would do a cloud solution, but itโs not mandatory. We can also do on-premise solutions if required.
Antoine Walter:
So data gets collected to the cloud and the cloud sentence, then a recommendation. How much of actuation do you find on the networks today? Because you have a recommendation there, there would be two outcomes. Whether you have a network which is actuated, then you can directly move and you can benefits at the maximum from this reactivity that you have through your system. Or if you have a recommendation to an operator that whether pushes a button or maybe has to go somewhere with his car and open or close the valve. So how is it today? How, how much activation do you find on the networks as much as sensors or even less, even more?
Brian Moloney:
Yeah, Iโm certainly much less than sensors. I think the, the application of the actuator would have been my guess, but yeah, yeah, absolutely. So the application of the actuation across the network is not, there is certainly some standout examples of some utilities who have that and have it working really well. But in general, thatโs not something thatโs practiced by utilities generally around the world. I think itโs something thatโs calming. Utilities are becoming open. You know, all the factors that everybody understands, climate change, greater populations, constraints, budgets, theyโre pushing utilities to have to try new types of solutions, to get more out of the existing infrastructure that they have. Something like predictive control valves and actuators are critical to that, but you donโt have to go to that step to get the benefit. Weโve done projects like blockage prediction, real-time blockage prediction using this technology, using the forecast and the machine learning.
So where you would be developing a prediction for each asset, you be giving a trench, hold an operating threshold for each asset. And then if you go outside your operating threshold. So if thereโs a anomalous level or anomalous flow in the network, so something thatโs not expected in the network, then thatโs highlighting to you that there might be a problem there. And weโve been able to get really accurate results with a case study we did at Essex water. Weโre also rolling that out with a couple of other utilities, but West six water or a big utility in the Southwest of England, we were able to identify running for three months, across 3000 kilometers of the source. We identified 60 blockages in real time that they were able to clear a pipes out and ensure we didnโt have a pollution incident. So we were detecting the early formations of blockages by identifying anomalous flows in the network.
Antoine Walter:
Actually thatโs an interesting this book story because I have to confess, I had no clue that the problem was thus important. I read in your documentation again, thatโs solely in the UK, itโs 100 million pounds every year to remove those blockages. And from your case study, I did quite a stupid statistics. But I found out that it was roughly one blockage for three kilometer fives every year. So it sounds to me like a huge problem and in what I read as well as that you were able to detect those blockages 14 days before they really happen. So how much of the blockages can you eliminate in percentage?
Brian Moloney:
Yeah. So this is all correct. And I think that a hundred million figure is actually from a couple of years ago. So not that that has increased because the problem is continually increasing sources. This is a huge problem for utilities. The blockage problem. If you go on most utilities, actually, why, why, why is it noise increasing? So both. Yeah, so really itโs, thereโs two factors that contribute to about 90% of the sewer blockages. The first one is wet wipes where these non-degradable wipes are being used more regularly. I read a figure today that last year, I think there was 8 billion wipes sold individual wipes sold in the UK. So all of those are being used. Some of them then are being flushed down the tilers and they donโt degrade like toilet paper might degrade and they stay in the sewer when that den is combined with the Isles and the fatโs coming off of cooking and the facts that are okay to go down the drain.
But once they actually hit the sewers, they cooled down and they form a solid. So this all melds together and it forms like a rock like substance in the sewers. And thatโs what you call a fatberg and thatโs whatโs causing a lot of the issues is these thought these, these, they look grotesque when you see them, but theyโre essentially like the big, big rock formation in the sewer pipes, which obviously is a huge problem because itโs a, itโs essentially a big blockage. So what do utilities are trying to do is to get to these blockage formations Airlie before they turn into these big fatberg formations and actually get in and get them moved on and jet them out so that they get through the network, because if they start to form in the pipe, then youโve got a big problem. So understanding the correct flow, doing really accurate flow windows are level thresholds in the sewers, monitoring them, using machine learning to understand when youโre going outside of an expected threshold or are outside of an expected level. Itโs really, really valuable to allow that utilities are starting to understand the potential and the benefits around.
Antoine Walter:
So itโs good because you defined the fatberg. So I was really wondering what the fatberg was. Now have a clear definition. Itโs crazy. When you think of it, 8 billion wet wipes, only in the UK. If youโre listening to this and youโre still flushing wet wipes in your toilets. I mean, I donโt know how we as professionals shall repeat it, but donโt do that. Just that just donโt. So you find out about this blockage is 14 days in advance. You can clear them and how much of them can you detect all of them? 90% of them, 50% of them. Whatโs your accuracy there?
Brian Moloney:
Yeah. So, because weโve developed out this technology with the machine learning capabilities and because weโre using the hyper-local rainfall forecast and weโve been at this since 2011, believe it or not, that was when the Queensland floods happened. Thatโs when I started to develop out this technology in the projects that weโve run with utilities, we actually havenโt missed a single blockage. Weโve got every single blockage rice weโve got 95% accuracy on our alerts are maybe nine 90, four 95, something like this. And then youโre talking about 5% false positives. So what weโre finding is that you need to have very high levels of accuracy or else the operations crews into utilities lose faith in the solution. So every time you send a message to the operation crew, that you need to go to a certain site at a certain location, they need to find something when they get there, or else they start to say, Oh, this machine learning doesnโt work or this technology isnโt good.
So itโs really, you have to have that level of accuracy or else you wonโt get buy-in from the operational staff. And what we found is we were running proof of concepts with different utilities, and we would actually get a phone call at the end of the proof of concept from the maintenance are the operational crew. That would say, please donโt turn this off. We like this, this, this is working for us. Finally, weโve got a solution. Thatโs able to get us through the blockages. So we donโt have to deal with the big fat burgs down the line. Please keep this running and the utility, but ask us, please keep this going. And to me that proves the value of a proof of concept. When the operational crews are the ones that are asking you, you know, this is working, we shouldnโt take this out.
Antoine Walter:
Thatโs for the networks, if I go back to the attenuation tanks, if I got it right, what you do there is that. And I mean, you are on the donโt waste water podcasts. So trust me, I like the Idea. Itโs about not wasting water. You can reuse a part of that water because you know that the storm event is gone. So thereโs no new rain for the next two days. So why not use that water or to use it as grey water to flush the toilet or non-potable reuse? How does that work?
Brian Moloney:
Yeah, so thatโs a, so weโre jumping all over the place. This is a first solution that we described was putting a smart file versus smart pump, linked to a forecast platform into a storm tank or a flood tank. So let me give you an example of a project. So weโve got some really fascinating projects being installed currently and more coming as we go across this year in the Netherlands. So the net are nuns, certain cities. Weโve got a big project in Austin and a big project in us and dull in these cities. This is what our partner company woven, who do the flood tanks with this solution. Weโre often are putting and retrofitting in these new flood tanks into these cities. And the municipality has understood that thereโs an increased flood risk. So theyโre trying to minimize and make the city safer by installing these flood attenuation structures and tanks in different parts of the city.
So weโve come up with this solution that we control the level in this tank based on our smart files. So we can predict what waterโs coming when itโs coming. And what weโre doing is weโre recycling water out of this tank because now we can hold water in the tank, but if thereโs a big flood event coming, we can release it and drain it down. And then we can, the next time we get some small rainfall, we can hold the water in the tank. Again, weโre able to use this water to actually water the vegetation in the town center, all around the cities. So instead of during the summer when thereโs a potential hose pipe ban on that, all the vegetation dies and all the nice flowers that exist around the center of the town. Now weโre able to water them from the tank that has been installed.
So the tank is not only increasing, there are mitigating the flood risk. Itโs also providing the ability to recycle and keep all your greenery in the area. Green. Sometimes itโs been linked to the local public toilets in the area. So thereโs all different sorts of things. If, if we put it in on an individual development for a particular building, we can actually reuse the water for drinking or for non drinking water. So for a toilet flushing or for cleaning or for irrigating, the, the plants as Iโve described. So itโs about being smart with the water weโre using. Thereโs lots of benefits here because this stormwater otherwise would have ended up in a combined sewer, which mightโve contributed to pollution. It would have probably have ended up if not being discharged in, in a pollution event, it would have ended up in the waste water treatment works, where it would need to be treated, but instead of needed to be treated, itโs actually providing value by allowing it to recycle some water back in the network. It just makes so much sense on so many levels to actually implement this tire of technology. Now
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Antoine Walter:
It exists. So I know that it sounds a bit like Iโm dragging you left and right, and Iโm sorry for that, but Iโm really curious. Itโs a fascinating field, but of course you donโt want to treat all of that water in your wastewater treatment plants. But I was discussing that with with Marie Launay, hi Marie, if listened to that. And she made some, some studies and some research on micropollutants, which would be given back to the environment through this storm attenuation tanks. And what she found out is that there are waves there. The first backwash of a parking lot is heavily polluted. I mean, heavily is full of those micro pollutants. Whereas the next 20 minutes of a rain event is much less sensible to this pollution. So if you were able to say, okay, letโs take this parts, letโs store it there.
We can put it directly back to the network because thereโs a storm of itโs an ongoing right now, but when the storm event is over, letโs push back this part of the water, which is polluted and letโs reuse the rest in grey water. So you could really do a separation. And when I saw in your explanation video that your, your attenuation tanks are basically split in two. I thought if itโs split in two, why not pitch it in three or in four and have these different waves covered that way? Does it sound utopic or is it like marginal gains and not worth it? Whatโs your thought process here?
Brian Moloney:
Yeah, I think itโs a very interesting suggestion. Very interesting solution. I think the beauty of this technology is that it allows us to explore and really push the boundaries here and things like this, because what we now have is we now have a dynamic control system thatโs installed in the flood tank. So really we can control the flood tank in whatever way we want. And we can retrofit that. We can change that by altering a bit of software so that we run it in a slightly different sequence. For example, we post the valves open every now and again to make sure we get some movement of water in the time to provide it to if I had water sitting there in one position for too long and, and avoid those issues. So if there was a requirement to drain off some of the first flush and then hold the water back and then later release it back in.
So it could eventually get to the treatment works. Itโs all absolutely possible. And itโs the beauty of putting a system in there that can be dynamic and remotely controlled using software is that, you know, if you put a big concrete tank in the ground and you walk away, itโs there forever and it can only perform in that way. It can change its behavior. So if climate change changes, it changes the flood pattern or the discharge pattern, the software can change with us, and then we can keep the tank constant and updated. And itโs just a technology that exists to do that now, which weโre putting into the, into these tanks.
Antoine Walter:
You mentioned this reference in the Netherlands, and you mentioned your partnership with, with Wavin. You are currently distributed in 20 countries, right? Letโs move to the business side of things. First, whatโs your business model? What are you selling right now as itโs a service is itโs a fee based on, on the installation of a software and then a service fee. How does that work?
Brian Moloney:
Merrily? Weโre a SAS company. So it will be a software as a service charge. So thatโs primarily how we work. If we need to put in a valve, a smart valve, there will be a charge for putting in some hardware. Only if itโs required. If the hardware is not required, weโre not pushing the hardware. Weโre all about the software, but sometimes if the utility or the development doesnโt have that hardware themselves, we might have to help them arm provided to them. There can be a hardware charge if they need the hardware. But primarily what weโre trying to focus on is the software side of things. We see ourselves as a software solution that can do prediction better than anybody else in the industry. And that allows us to do all these other technologies, like the automated control of the valve to improve the flow of water and whole buck water, to predict blockages and wastewater networks to do real-time control for utilities. So itโs, itโs the software that enables all this and the real smarts are in the software. The hardware is just a facilitator so that we can, we can put the control mechanism in there, but how you control that is where the brains are. And thatโs where all of our art our IP is.
Antoine Walter:
And what about this distribution in this 20 countries? Is itโs you directly? Is it through partnerships? How do you expand internationally?
Brian Moloney:
Well, we deal with some countries ourselves, and then we have a deal with woven to go around. A lot of countries around Europe woven are obviously a big presence there, a big and their parent company RBR really environmentally focused. And thatโs where the alignment really came really well because our mission is to improve drainage networks so that we can improve the environment. And that really struck a chord with the parent company of Marvin who are RBF and theyโre a Mexican company. So that distribution happens with woven sales network that exists around various different countries. Now, some countries weโll go to ourselves, some countries, weโve got people working on our say, working in ourselves. Weโre very, hands-on in the UK. We do have rainwater harvesting partners in the UK, but with the utilities, weโre very hands-on ourselves and the UK. Weโre obviously very hands-on ourselves in Ireland and then various different countries. Weโve got different models, but a lot of it is true. This wealth and partnership model, which has been a, for more point of view, a fantastic success.
Antoine Walter:
I was surprised I have to say positively surprised because of the radical transparency aspect of that, which I really love in companies, but to see from your, the pitch of your company on that, you mentioned that you are backed by venture capital. And Iโm always wondering when I see companies backed by venture capital inside the water industry and had several of them are on that microphone. Iโm always wondering if hypergrowth is something which is achievable within the water industry. So I would be interesting here to have your take. Yeah.
Brian Moloney:
We are backed by venture capitalists financing. So, I mean, itโs really exciting because it gives us the ability to scale quicker than what we would otherwise be able to do. So weโve installed systems, weโve tested systems, weโve proven the concept in a number of countries and a number of different concepts that weโve all discussed across this podcast. Now we have the ability to take those concepts that have worked across a number of countries and quickly scale them into, into a lot of different countries at the same time. So itโs a, itโs a really, really exciting time and where it aligned really well with our investors, because, you know, within the, the investment funds, the people that we deal with are, believe it or not civil engineers. Now, they may not have worked as civil engineers for a long time, but thatโs where the background was.
Thatโs where theyโve come from true education. Thatโs where they started their own careers. And a lot of those, these people are entrepreneurs themselves that have had successful companies, you know, in this fear. So they really get what weโre doing from day one, theyโve bought into our vision for what we want to do. And, you know, seeing that there is a huge environmental problem is a huge, huge area of growth to improve sewer networks and drainage network, stormwater networks, you know, and thatโs the primary thing that drives us. Itโs the primary thing that drives them. Theyโve also got an environmental slant, which is also a very positive thing from our point of view, that they do care about the environment they are investing in companies that can improve the environment. And I think were bought on, on, on a really aligned path. And weโre both going really going after the market in terms of trying to bring the solutions out to as many countries as we can, and really push ourselves.
Antoine Walter:
You mentioned that they are aligned with your vision, but what is your vision? Where do you see yourself in, letโs say two years, five years on the long run.
Brian Moloney:
So I think really what weโre trying to do is weโre trying to improve drainage networks or wastewater and storm water in our networks. We need to, our mission is to improve the performance of them. And we do this using technologies like hyper-local weather, forecasting, machine learning. So really getting out and being able to improve as many drainage networks and wastewater networks as we possibly can. So we can save as much water as we possibly can take as much water out of the combined store network, prevent as many overflows and pollution incidents as we possibly can in as many countries as we can nearly make an environmental difference so that we can, we can really say, and look back in five years time that we did something, we made a change, we took some technology, we applied it into drainage and we were able to improve the performance of drainage networks and improve the,
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Antoine Walter:
So itโs not only a vision. Itโs also a mission somehow. How many people are you right now within the company
Brian Moloney:
At the moment we are between 15 and 20. So weโre growing very quickly. So maybe last year we were, we were 10, seven the year before that we were four or five. So weโre growing really, really, really quickly. And with the clients that weโre signing on every week, weโre growing quicker and quicker, the challenge that weโre having is getting, getting people as quick as we possibly can. Thatโs our challenge. The clients seem to want our services. Theyโre interested in what we do, but we need to be able to deliver for the big utility clients that weโre taking on. So weโre constantly trying to push ourselves forward.
Antoine Walter:
You mentioned that once you have something which works, do you deploy it in other countries? So basically once you have your, your product market fit around your solutions, then you can deploy it to other countries. Is it all going to happen through waving or do you also plan to have in a close to mid-term future? I donโt know where a us office or an Australian office would make about sense given your, your path.
Brian Moloney:
Yeah, thatโs a, something very much on my horizon as the Australian opportunity and, and tackling that because of my history and the company coming from Australia, initially, it wonโt all be done through love and love and have great coverage across Europe for the storm tanks in the flood tanks. But the strategy will be to look for other partners that can help us, certainly our partners across America that can help us. And weโre talking to a number of those at the moment. And similarly in Australia, weโre really keen to get in and get active in the Australian market. We think weโve a solution. Theyโve got all the same problems across Australia and New Zealand. So we think that thereโs a really good fit there for what we do. So part of it will be true additional partnerships to supplement the wildland partnership. And part of it will be true direct outreach. We will do a certain amount directly in these countries as well. I think stay close to a market. Itโs really important to have the representation from your own company in those, those markets youโre interested in targeting.
Antoine Walter:
And now, you know, the, the golden last question, I had very honest answer to that question. Sometimes some other times people just told me they donโt know. And because sometimes they didnโt know when sometimes they didnโt want to tell. And some other times they told me, no, thatโs a stupid question. So I, up to you really, all the answers are possible. Do you want to build the next big thing in this water industry? Or do you like building, and at some point you want to exit and start building something else?
Brian Moloney:
I think I like building, I want to take this company forward, but we need to make sure that what we do gives the company the best chance to, to use this technology. Itโs not, itโs not the company, itโs the ability to use this technology and to take this technology to the market. So whether we have to do that through a merger or two acquisitions or how we do that, but itโs what Iโm really passionate about is taking this technology. I can see the benefits of it. I can see what it can do now. And itโs bringing that into this industry. This industry is, you know, true education. Itโs been, my whole career is working in drainage and storm water and wastewater networks. And I really see that thereโs a big difference can be made from using this technology. So however that happens quickest and at the biggest scale, thatโs what Iโm interested in. And if thatโs also taking more funding and really pushing forward further, if itโs also merging with other companies or itโs also acquiring companies, or if itโs also going in under the umbrella of another company that can really promise us that growth and get, and make the biggest difference, weโre open to all of those suggestions, but Iโm just so excited about moving this technology and getting it out there.
Antoine Walter:
So youโre following the mission while you believe in what youโre doing. So I think thatโs a good combo. Makes about sense. Brian, would I propose you is to switch to a rapid fire questions? Thatโs okay for you. Yep. Perfect.
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Rapid fire questions.
Antoine Walter:
So in that section, I try to keep the question short and Iโd be happy if you keep the answers short, but Iโm not cutting the microphone. So donโt worry. My first question would be what is the most exciting project youโve been working on and why? Yeah, so
Brian Moloney:
The, I think one of the really, really exciting projects weโve worked on is the project weโve completed with West six water. The one weโve completed with waste six water to me would be a really exciting one where weโve been able to predict blockages. We havenโt missed a single blockage in the network that weโve been working on and itโs, itโs transformational for their maintenance crews. Theyโre moving to a condition-based maintenance approach, which is fundamentally changing and improving how theyโre, how theyโre able to respond to these blockage issues. I think that thatโs been a really practical, exciting project.
Antoine Walter:
Okay. Let me cheat here right away. I just have one number, which I didnโt understand the, in this case study, when I read it, itโs minus 96 persons control room alert. What does that mean?
Brian Moloney:
So how the utility is working is at the moment theyโve got older sensors out in the network. So theyโve got their pumping station sensors. Theyโve got some CSO outflow sensors and during periods of heavy rainfall, basically what happens is their control room lights up like a Christmas tree because all of the alerts go during rainfall and the entire control room. You know, it becomes unmanageable to a point where a lot of utilities have to silence for a day and just put all their alerts, turn all their alerts off for a day because they canโt understand where the maintenance crews are meant to go and where theyโre not meant to go because they canโt understand what a genuine alert or whatโs just normal high level in the pipes because of rainfall. And what weโre doing is weโre using our machine learning to determine when is a high level, okay?
Because itโs just rainfall and itโs within the operating parameters are when is a high level not okay because itโs showing something out of sequence. So whatโs, the pipe is usually during this density and intensity of rainfall, duration of rainfall fills to 50%, but our machine learning has arranged then of 55 to 45% that we should be within for that sewer. If the sewer goes to 70% or 80%, then thereโs a problem because something is happening in the sewer that shouldnโt be, and thatโs where the ops crew should go. Not to the other 50 sewers that are behaving in the normal practical way that they just fill up an empty. So itโs, we were able to turn off or silence 95% of their alarms and leave on the 5% that are critical, where they need to get out, or thereโs going to be pollution that shouldnโt go out. That could be avoided.
Antoine Walter:
Okay. All clear. So sorry, Iโm the one that sidetracked to you, but that was intriguing to me. And Iโm glad you made it much more clearer. Whatโs your favorite part of your current job?
Brian Moloney:
I think itโs seen the benefit, seen the rewards that we get when the team comes together. So we had a project today where we got really, really good feedback from the utility client, that they were really happy with something that we were doing a piece of work that we had provided to them. And there was four people involved in that project, all doing a small bit different in terms of, of their own piece, of what they were doing. All the four people came together to provide this one solution that had our clients basically saying, this is absolutely what we need to do. Itโs helped us. And it saved us money. Itโs the reward that we get when the team is working well together to achieve the mission.
Antoine Walter:
What is the trend to watch out for in the water industry?
Brian Moloney:
Oh, well, I, I think I canโt I canโt preach for the last 40 minutes about smart network, smart drainage forecast, prediction control. We say more than real-time control. We say forecast predictive control because real-time control is still, itโs still behind time. If you can do predictive control, thatโs what weโre preaching. And I think thatโs the area where thereโs huge benefits coming down the line.
Antoine Walter:
What is the thing you care the most when youโre working on a new project and what is the one you care the least?
Brian Moloney:
Oh, ah, thatโs, thatโs a really difficult question. What we care the most about a new project is delivering a positive outcome for the client so that we so that we can help them to reach their goal, be that financial or be that environmental. So if weโre trying to, to provide a system in for a client that can actually reduce pollution and, or save them money at the same time. So that would be the, the most important thing, the least important thing. I donโt know, thatโs too difficult for me to answer.
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Antoine Walter:
Surprisingly, thatโs the most common answer I get to this question. Itโs hard to say what you care, the least I get it. Do you have sources to recommend, to keep up with the water and wastewater market trends?
Brian Moloney:
I think nodding in particular, I think the conferences, the industry conferences are fantastic. So I went to WEFTEC in Chicago last year. Well, not last year, the year before, when it was possible to travel. And that was just such a fantastic experience to see all the different companies, just under one roof, with all their different solutions, all challenging, different problems coming with different mentalities. I highly highly recommend WEFTEC also Aquatech and Amsterdam, a fantastic show, a fantastic presentation, and really, really, really good and seek out people like Swan forum and people like imagine H2O, who are the, who are the groups that bring these innovative technologies together and showcase them to the industry. Theyโd be really the places that Iโd be looking out it. I know thereโs not, you can always, always check out storm harvesters, LinkedIn page, cause weโre very active and weโre really trying to show what weโre doing to everybody.
Antoine Walter:
Well-Placed would you have someone to recommend that we should definitely invite as soon as possible on that same microphone?
Brian Moloney:
Oh very interesting. So the European product manager for, for so motor with woven is Amman called Herschel van from the Netherlands who was a real advocate and Lavin have done some really interesting stuff yes. With what their partnership with storm harvester, but also in enabling more vegetation and more trees using their crates and their planter boxes. And theyโre able to leave the vegetation to breed and get the moisture around the vegetation without causing root damage. Theyโve got a number of solutions like that that are really innovative and real positive. I think heโs a really interesting person too, that has a really good aspect and real good vision on this. I know not, you know, some of the utility managers and leaders that we work with are really, really innovative. And theyโve actually had the chord face of using this and lots of other technology.
Thereโs a utility called West six water. Iโve mentioned before where weโve done a lot of really good work and Jody Knight manages their marketplace or heโs involved with some of their marketplace work. And he is fantastic advocate for solutions that will improve the performance. Even if they are very innovative, that will provide, will improve. The utilities are performance. So Jody is a, a real strong candidate I would feel. And maybe her chill as well. Thanks for the Dublin recommendation. If people want to follow up with you just after this interview, you mentioned your, your company page on LinkedIn, Iโll put the link to your LinkedIn, to your, your webpage. I was also impressed with see, on your webpage number of case studies you had. I mean, itโs, itโs interesting to, to get a better grasp on, on what youโre doing. And I saw that all of you on your, about page, all of the people that are listed are directly listed with the LinkedIn.
So I guess that must be a, a medium of choice. Yeah, very much. Any other place that you would recommend to, to link to? I think our LinkedIn is our strongest the strongest one at this moment in time, we also have a Facebook page, which weโre very active on. Some people like using that platform other than not. I mean, just, I think just LinkedIn, keep an eye on our blog, on our website as well. Weโre quite active in that. And then weโre always trying to add case studies onto our website. So keeping an eye on our case studies and our website also are very interested. And then my personal LinkedIn, Iโm always very active on there. Thatโs I, I find that a really good tool to engage with other industry professionals and share tots on, on different things. So my personal LinkedIn, Brian Maloney on LinkedIn is also a good one. Awesome hook puts all the, all the things, of course, in the, in the show notes. Like every time Brian has been a pleasure, Iโm slightly overtime in know Iโm French. I have to be not really accurate with with the times I give. But I hope itโs still fun for you. It was a pleasure. And I hope to have the chance in a couple of years to check on him.
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