AI in Waste Management

Smart waste management systems are slowly replacing the traditionally manual and tedious job of handling, collecting and disposing of waste. These smart systems are not only solving the problem that has long plagued waste management, but also introducing greater efficiency, sustainability and data-driven decision making.

For the longest time, manual scavenging has been one of the most inhumane and hazardous forms of labour in India. Despite the Employment of Manual Scavengers and Construction of Dry Latrines (Prohibition) Act, 1993, and the Prohibition of Employment as Manual Scavengers and their Rehabilitation Act, 2013, the practice has not yet ceased to exist. However, robotics and AI are now emerging as powerful tools to finally put an end to this demeaning practice. Only last year, India’s 1st septic tank cleaning robot was launched, offering an end-to-end solution to eliminate manual scavenging and strengthening the Swachh Bharat campaign. The robot uses AI to inspect, clean and manage septic tanks and manholes, eliminating the need for sanitary workers to get inside the toxic environment.

AI and Solid Waste Management 

Use of AI technology in management of Solid Waste Management (SWM) can help reduce transportation distance, reduce costs and save time. Successful application of AI technology in SWM would help cities accelerate Sustainable Development Goals 9 (Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation) and 11 (Make cities and human settlements inclusive, safe, resilient and sustainable). 

The use cases for AI in SWM include:

• Automated sorting systems: AI robotic arms fitted with cameras and sensors apply Artificial Neural Network (ANN) and Convolutional Neural Networks (CNN) models to identify, sort, and separate different types of waste materials. AI technology can be used to recycle a wide variety of inorganic wastes, including glass, plastics, cardboard, metals and cups. This saves significant time, increases efficiency and reduces the risk of contamination.
• Smart collection: Sensors and AI algorithms help optimise the source collection of waste by generating patterns, traffic conditions and route optimisation. This helps save time and fuel, while also reducing the carbon footprint of waste collection vehicles.
• Data-driven decision making: With AI’s predictive analytics, municipalities and waste management companies can plan resources more strategically and efficiently. This foresight is crucial for managing seasonal variations and planning for special events that might generate more waste.

Future opportunity

While the use of AI in waste management holds a lot of promise, it also provides a paradox. The growing reliance on AI-powered devices and systems in our daily lives will add to the problem of electronic waste (e-waste), posing a serious environmental threat if not disposed of or recycled properly. There is no doubt that the use if AI will make waste management easier, but it also requires some level of manual sorting of organic and inorganic waste at the source if generation before reaching a big sorting plan. Without policy level changes that enforce the habit of better waste disposal by citizens, AI waste sorting cannot be fully utilised. Local government-level awareness campaigns and encouraging them to partner with enterprises can also improve waste sorting at grassroots levels. The government must also offer financial assistance to small and medium enterprises to cover the high cost of AI waste sorting systems. Assistance of data centres, 5G, and transportation from Government should be the priority for waste categorisation and treatment. With more innovation and better technology for recycling and reuse of waste materials into productive means, we can minimise environmental degradation, reduce dependency on landfills and promote a circular economy.