Artificial Intelligence and Machine Learning are here to stay. They are already making a difference for recycling and waste management companies in the areas of automating a process to reduce cost, improving productivity, and/or increasing safety.
By Ken Tierney
Each year, the waste and recycling industry evolves, and new innovations continue to take center stage as companies realize the importance of this ongoing transformation to modern technology that will help optimize and improve the efficiencies of their business. By embracing new technology, such as artificial intelligence with a data-driven approach, companies can eliminate or reduce manual processes, speed up productivity, improve safety, cut costs, and contribute to their sustainability goals. The potential for AI to impact the waste and recycling industry through automation and intelligence will continue to transform the industry at a profound speed.
What is Artificial Intelligence (AI)?
Essentially, AI involves processes and algorithms that can replicate some human skills such as problem solving and learning. In some ways, AI is already better at some tasks than humans such as spotting patterns in vast data arrays. However, we are not at the point where AI is as talented as the human brain, and it may never be.
When used on certain tasks, AI can focus on processes that we might find laborious or we are incapable of doing. For example, you would not place a member of staff on your vehicle to check every item in the bins you collect, but cameras and AI are capable of this.
AI is also used an overall term, so within it, there are sub-sets such as machine learning, reinforcement learning and deep learning.
ML is AI that takes data processing further. It learns by being trained. You tell the AI what is wrong, and then it learns what is right using ML. In the recycling sector, it might be that you want to train the ML to recognize PET bottles. So, you show it HDPE bottles, cardboard boxes, steel cans, and PET bottles.
As you are training it, most likely it will not always recognize PET bottle images, but when it gets it right, you confirm that it is. In the background, ML is using mathematical models to code itself on the properties of PET bottles until it begins to identify them every time.
Increasingly, ML is advancing so that it no longer needs to be trained. This unsupervised learning allows it to spot patterns in data and work things out itself. This is especially useful in complex data sets, where there are no obvious trends that can be seen by humans.
Examples of this type of ML are the recommendations you might receive from Netflix or YouTube that learns your viewing habits and then suggests other things you might like.
We have also seen high adoption of CNG in the refuse industry for the same reason. According to the U.S. Department of Energy, refuse fleets using CNG save an average of 50 percent on fuel costs versus using diesel. That could mean annual savings for more than $13,000 per truck, which becomes increasingly more appealing to businesses.
There is also a type of ML called reinforcement learning, where it learns from the environment it is placed in. This is useful for robotics where the device is constantly improving. In the recycling sector, it could be that robots use reinforcement learning in future to collect bins, understanding the challenges put in front of them such as gates, plants, and trees or items placed against the bin—all things that humans can deal with easily, but robotics currently struggle with.
Deep learning is a form of ML where artificial neural networks are used to replicate and surpass human analysis. It has been used for translation purposes, medical image analysis, and climate science.
Potential future uses for deep learning could include human-like ability to assess contamination on sorting lines or identifying materials so that different paper grades could be sorted.
Why is AI Important?
We live in a society driven by data. That is not necessarily just structure data, such as numbers on a spreadsheet, but increasingly unstructured data such as images and tweets too. This means AI is being used for everything from data processing to autonomous vehicles.
As an example, AI could be used to process customer data to better understand the waste and recycles generated by customers. Patterns could be spotted that enable you to run your business more efficiently, identifying where it may make sense for you to run a food waste or paper recycling route. Route optimization software is a form of AI, as it analyzes and works out the optimal schedule based on the pattern of the customers you need to service.
When it comes to your vehicles, AI is likely to transform how they are used in the coming years. Solutions already exist that can predict maintenance schedules, especially when combined with telematics. However, self-driving capability is likely to be common on vehicles in the near-future—and AI is the bedrock of this.
By analyzing millions of images, recognizing, and deciding on data such as traffic in front and around you, pedestrians, cyclists, maybe even a bin blown into the road by strong winds, the AI using sophisticated ML algorithms and artificial neural networks decides on how to operate the vehicle safely and efficiently. We are not quite at the point where self-driving will be used soon for recycling and waste management, but it is probable that it will become normal over the next few years.
AI and ML are here to stay. They are already making a difference for recycling and waste management companies in the areas of automating a process to reduce cost, improving productivity, and/or increasing safety. While AI is a very powerful tool, it should be understood that any process using AI and ML is not 100 percent accurate and there needs to be ongoing monitoring and teaching, so that the AI engine continues to learn to ensure that it is as accurate and efficient as possible. The future for AI and ML in recycling industry is exciting and I expect that it will continue to increase across many aspects of the industry. | WA