Digitalization of waste recycling services: a great story… that concerns you! By Olivier Pagès

We can easily compare industrial sectors to gastronomic recipes: some of them are traditional, others are more contemporary. In terms of waste collection and recycling, we used to be more traditional. But this was way before the leader of the recycling sector, SUEZ Group, decided to force the sector to enter in the new era of digitalization.

 

 

When industry meets technology…

The story is edifying, and characteristic of a virtuous meeting between an industrial need and a technology. It is also a meeting with a start-up dedicated to B2B innovation through the creation of critical data for a worldwide leader in its sector. It all started with the presentation by ffly4u to Suez teams in Occitanie, of the connected drums solution developed by ffly4u. It is based on “Edge AI Low Power®” technology, allowing embedded processing (in our ffly4u device) of the data produced by the movement sensor through Artificial Intelligence, which means Machine Learning and Deep Learning. The demo consisted in showing how a connected drum is able to differentiate its rolling on the ground from its rolling on an axial drum. This identification of drum life phases makes it possible to accurately compute the daily length of unwound cable on each drum with a success rate close to 95%. At the end of 2020, this data had become a “must have” in the cable and telephone fiber sector – an indicator of the working site progress – which prevails today in most of the calls for tenders in Europe and Middle East. 

 

The skip filling alert, a key indicator of the work in progress 

The Suez teams were then convinced of the relevance of our technology to create new specific business data, more valuable than the geolocation of objects. To digitize their business, they came to the conclusion that their central and differentiating data was the skip filling alert. To do so, it was necessary to identify the number of compactions, consisting in compressing, by a roller at the end of an articulated arm, the contents of the skip. The embedded “Edge AI Low Power®” technology is essential in this use case. Thanks to this innovative technology, the skip is able to identify, through the recognition of the vibratory signature of the arm on the skip, the action of the compressor arm.  

 

5% reduction in greenhouse gas emissions (GHG)

After a validation test of the end-to-end service, integrating at dock/off dock geolocations, loading / unloading alerts, emptying and other presence on industrial sites, 2 types of dashboards have been designed, a daily and a weekly one. The deployment on 13 sites and more than 200 skips was then implemented very quickly at the end of 2020 allowing a reduction of 5% in (GHG).

This is an excellent example of the value created by a leader in a traditional sector through technologies linked to the production of critical data. Each industrial sector is or will be concerned in the near future: we have already digitized the cable/fiber and waste recycling sectors, and also returnable transit packaging in the aeronautics industry.

 

New business data for a quick ROI

Each industrial sector will be revisited through the production of data, for a double challenge: a new level of industrial performance by reducing costs, and above all, by the monetization of business data that brings value to your own customers. Thus, creating a new source of income for your benefit.

Believe me, your job is already impacted … whether you are Coq au vin or molecular cuisine, your job will change in a sustainable and virtuous way through the valuation of business data. That’s for sure!

Remember the story of Suez – ffly4u. Yours is on the way!

 

Skip filling alert: myth or reality? By Nghia Phan, CTO at ffly4u

SUEZ use case: our CTO gives you an overview of the technical challenges to compute the skip filling rate, real KPI of the work in progress in the recycling industry.

 

What were the technical challenges to meet SUEZ business needs?

Even though one may not  call it as a technical challenge, the most important for us in the beginning of this project was to fully and deeply understand the complete operations of a waste collection center and understand the gain in operational performance that our customer was looking for. Without such deep understanding, it was pointless to go any further. A recycling business operates a limited number of skips, manages different type of wastes/items to be recycled, has limited number of docks, and also shares common resources such as collecting trucks and special compacting equipments. A limited number of skips means that you have to rotate them between the waste collection center (where individuals or businesses would drop off their wastes) and the actual waste recycling/processing center where the wastes are further sorted and processed.

Eventually we came to the conclusion that the Return On Investment of this project was really based on our ability to optimize the collection of only fully filled and compacted skips and hence save thousands of gallons of gasoline.

So the overall technical challenge was to be able  to determine where and foremost when a skip is ready for collection.

What available information could you use to develop a customized solution?

The short answer is that there were no readily usable inputs. As explained earlier, we spent a fair amount of time with the operators in the field to understand the business operations: what information the operators was collecting manually? How this information was then processed and used? For which purposes? What were the decisions they would take based on the manually collected information?

Once we had this understanding, we had to figure out the right technologies and the right “IOT” device to collect this same information but in an automated and systematic manner and then, how to process and report the data and associated analytics to the operators so that the correct decisions could be effectively taken.

The information to be collected was:

  • Very accurate location of each skip, especially when they are located on a specific dock in a waste collection center or when they are located in a waste processing center
  • Type of waste collected in each skip
  • Number of compacting events
  • Number of loading and unloading of skips
  • Duration of stay of a skip on a given dock
  • Number of emptying of skips. Emptying a skip allows us to reset the number of compacting events
  • Number of trips of a skip

The key differentiating and proprietary technology that ffly4u has developed and has applied in this specific use case is called EDGE AI Low Power®: we are processing the data locally within the device (aka “on the EDGE”) using Machine Learning / Deep Learning while minimizing the power consumption of such processing. In standard IOT devices, the processing usually takes place in the cloud with limited and poor data set because the IOT devices usually connect to the cloud using LPWAN network which has very limited bandwidth.

 

How do you compute a skip filling rate based on this combination of information?

Getting the exact filling level of a skip is the ultimate information that everyone in IOT is looking for. But the reality is that there is currently no affordable technology which is economically compatible with this type of low-value waste recycling business.

Nevertheless, at ffly4u, we managed to get a sufficiently accurate estimation of this filling level by combining the following information, obtained and enriched via EDGE AI LP®:

  • Type of waste: one can hence determine the volume density of the waste. Each dock is dedicated to a specific type of waste. Thanks to our technology, we are able to also assign / associate dynamically in real-time the type of waste to each skip.
  • Number of compacting events for a given skip: the lower the volume density of the waste is, the more you need to perform compacting to optimize the filling of the skip. This data is continuously adjusted by machine learning and our EDGE AI LP® technology.
  • Duration of stay of a skip on a given dock for a given type of waste. This data is continuously adjusted by machine learning and our EDGE AI LP® technology.
  • Number of loading, unloading and emptying operations which helps determining when to reset the number of compacting events between each rotation of skips between the waste collection center and the waste processing center.

The skip filling rate is a key indicator for Suez and more generally for the recycling industry as it has a direct impact on the skips rotation and eventually, on the quality of customer service.