How artificial intelligence and machine learning affect waste management

Artificial intelligence technology (AI) is rapidly becoming an invaluable tool in the heavy industry, and startups are constantly developing new AI tools to help businesses overcome existing challenges.

For example, facility waste management is becoming more important as manufacturers strive to increase efficiency, productivity and sustainability of their production processes.

However, identifying sources of waste and possible solutions can be difficult. New AI and machine learning (ML) solutions can help manufacturers improve their facility waste management.


Current challenges in facility waste management

For any facility that uses raw materials or components to create a product, waste will be a serious issue. These wastes can be solid or in the form of chemical wastes, water and smoke. Waste can be toxic, meaning it can damage the environment around a facility if not disposed of properly.

There are strategies that businesses can use to better identify and reduce facility waste. For example, lean manufacturing is a common manufacturing method that includes methods such as cost flow mapping (VSM) and source quality (QATS) – which help to reduce waste through top-down and bottom-up process interventions.

Many regulatory agencies, such as the U.S. EPA, also publish best practices in waste management at commercial facilities. Some industry organizations also publish their recommendations for implementing site sanitation plans or improving waste management practices.

However, while these methods provide waste management tools, implementation and identification of waste can often be difficult. Waste samples can be difficult to identify without sufficient information on facility processes. Waste management strategies may work in theory, but fail in practice, or they may require the site staff to work overtime to be proactive.

Waste management has become more important

At present, manufacturers are faced with rapidly changing market conditions. Constantly growing demand, lack of supply and new customer expectations have made waste management more important than ever.

More than a third of global consumers are willing to pay more for sustainable products, and some research has shown that consumers are avoiding unwanted brands.

Most consumers who want to pay more for sustainability, either Millennials or Gen Z, are younger – indicating that this trend will become even more important as the purchasing power of these generations increases.

The process of optimizing waste management not only helps businesses save money, it can also improve the business’s public image. At a time when many brands are striving to be green and demonstrate their environmental commitment to consumers, effective waste management has become important.

How businesses use AI in waste management

New tools that work with AI can help facility managers identify and manage site waste sources. These solutions work at a high level and help managers make more effective decisions and are directly on the production line where they can help floor workers identify and eliminate waste.


Vision machine for automatic recognition and waste separation

An example of a new solution that uses both robotics and AI innovation comes from an AI startup in London, Greyparrot. The company is developing a machine vision tool that is trained to identify and sort different types of waste, such as “glass, paper, cardboard, newspapers, cans and various types of plastic”.

Data from the separation algorithm can be transferred to workers, allowing them to more efficiently separate waste products into different waste streams that can be easily recycled. The company’s waste recognition API can also be used in conjunction with a robot arm or similar tool to automatically separate waste and without human control.

For businesses that already recycle, but spend a significant amount of time, labor, and money to sort waste, this tool, combined with facility robotics, can significantly speed up waste management and make the process much cheaper.

A similar startup, Winnow Vision offers a similar platform designed for use in commercial kitchens and food processing plants. Their car vision solution tracks and measures food waste and determines the value of the dollar to all the food and ingredients that the business sends to the dump, without making full use of it.

Reduce waste by improving product quality

Poor quality products can be a major source of waste. Process errors and poor quality materials can lead to defective products that businesses cannot sell, but have invested resources for them.

Some of the resources used in the product can be recycled through recycling or other applications – but it will always be more efficient to eliminate waste at the source.

Quality control systems use a set of pattern recognition and machine vision models to eliminate defective products from the manufacturing process. These control systems in conjunction with other Industry 4.0 technologies (such as IoT devices) can help to improve waste reduction production methods, such as a lean manufacturing approach.

Approaches from top to bottom AI to facility waste

A growing number of startups are offering AI products that help analyze business systems from top to bottom, rather than directly in a manufacturing process such as a car vision waste recognition system.

An example of these startups is WINT Water Intelligence, a developer of artificial water management systems. An AI solution from WINT helps to combat one of the largest sources of wastewater – leakage.

Plumbing in a facility is often complex and difficult to control, which means that small leaks may not be detected for a long time and cause significant water waste. By matching the AI ​​model, it is possible to more effectively monitor and detect water leaks when they occur. Using the technology, businesses could significantly reduce water waste without significant changes in construction processes.

Use AI to optimize facility waste management

Waste management is often difficult for industrial facilities, but new AI tools can help reduce the labor required to reduce waste.

Waste recognition and separation systems, AI for quality control and facility monitoring technology can all help to reduce waste at the facility.

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