The advent of new technologies has profoundly changed the waste collection service, allowing operators in the sector to adapt and fully enter the world of waste management 4.0. Today we talk about how to optimize these processes with the help of machine learning.
Before continuing, it is necessary to fully understand what Machine learning is , why it is so important and how much it can improve waste management in a 4.0 perspective.
What is Machine Learning?
Machine learning in practice is what we can define as "automatic learning" , the ability of a software to learn specific tasks for which it has not been programmed through the recognition of certain patterns within the processed data.
This definition is due to the American scientist Arthur Lee Samuel, the pioneer of what we know today as Artificial Intelligence.
Currently the fields of application of Machine learning have become increasingly greater, especially thanks to the technological evolution known as Industry 4.0 which has definitively revolutionized the industrial world as we are used to knowing it.
Going into more detail, Machine learning algorithms use mathematical models to learn directly from the information they have available, without the need for predetermined equations and / or algorithms.
In the field of waste management, the interaction between man and machine allows software to manage and process a large amount of data with extreme precision, managing to foresee and coordinate the various activities.
Machine learning and waste
management: the benefit of performance
The exchange of information we talked about earlier is one of the key points on which the current technology of a 4.0 waste management software is based. In this sense, Machine learning is able to further amplify the concept of performance in the waste collection and disposal service.
This happens because by integrating the two components we are able to process a lot of information thanks to which it is possible to suggest better interactions in an automated way. By improving performance.
How can we explain it better? With an example: imagine having to manage the waste collection service of hundreds of bins located within the same city. These are the factors to consider:
· Different distances between the different collection centers
· Timing and displacements not homogeneous
· Staff organization
· Management of collection vehicles
Starting from these elements, we must also consider that each single container will have a different performance depending on the number of users who use it. As a result, some bins will fill earlier and others less.
How to proceed? Thanks to the technology present in each of them that allows you to keep filling levels under control by communicating independently to the waste management software where and when to intervene, the emptying carried out and the necessary interventions.
In this case it is also possible to trace the best route for the vehicles by actually intervening in the optimization of the costs of the vehicles and personnel.
All this is possible thanks to an automatic learning system which, based on the management of each single information, establishes the best solution to apply to improve performance.
The automation of these digitized processes allows the waste management software to offer high performance by optimizing services and simultaneously guaranteeing precision and punctuality.
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