Economic Benefits of Implementing AI in Waste Management

The increase in industrial activity has led to a significant rise in the amount of waste generated. According to recent figures by the Central Pollution Control Board of India, India generates over 1,60,000 tonnes of municipal solid waste daily. This has led to prioritising effective waste management as a critical function in modern society.?

Material Recovery Facilities (MRFs) play a critical role in waste management by sorting and processing recyclable materials for reuse by businesses. However, MRFs face numerous challenges, including inefficient sorting and processing systems, increased manual labour costs, and contamination. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool to address these challenges and improve the efficiency of waste management in MRFs.

In this article, we will look through the challenges faced by MRFs, examine the role of AI in waste management and how automation and higher accuracy can lead to cost savings and increased revenue that can be achieved through automation and increased accuracy, and real-world examples of AI implementation in MRFs.

Challenges Faced by Material Recovery Facilities

Material Recovery Facilities are one of the most important players in the waste management ecosystem, collecting municipal and industrial waste, segregating them based on recyclability and then sorting them based on type and quality.?

However, these facilities face several challenges:

Inefficient Sorting Processes

In a Material Recovery Facility, the municipal solid waste collected from households and bulk customers is first segregated between recyclable and non-recyclable materials. The recyclable waste is then sorted based on type, grade, and grade colour.?

This process takes up a lot of time as different products require different sorting and processing after segregation and are done mainly by manual labour.?

Increased Costs of Manual Process

Increased manual labour costs are another challenge that MRFs face. As mentioned, manual labour is integral to the sorting and processing process but can be expensive. As labour costs continue to rise, MRFs must find ways to reduce these costs to remain profitable.

Contamination & Low Values

As waste management is currently a manual process, there is a high risk of cross-contamination between non-recyclable and recyclable materials. Thai can happen in both the sorting and segregation processes. In the segregation process, non-recyclable materials like food containers may get mixed with recyclable items, while in the sorting process, a material may be sorted into a different bin.

Role of AI in Waste Management

Artificial intelligence has revolutionised many of our industries and processes, from making the Roomba avoid obstacles to advanced uses in medical imaging. Waste management is no exception. With the introduction of AI in waste management, the challenges discussed above can be reduced and eliminated.

Moreover, Ai can also help improve the speed and efficiency of sorting & segregation processes while reducing the need for human labour. Some of the most common applications of AI in waste management include smart waste sorting systems and the identification of consumer data in segregation-at-source initiatives.

Here are some of the economic benefits of AI in waste management:

Cost Savings through Automation

AI applications In waste management, like automated waste recognition and sorting, can be highly useful for workers to understand how to sort recyclable waste faster and more efficiently.?

AI systems can collect data on the types and amounts of materials processed, providing valuable insights into the waste stream. This data can be used to optimise the sorting process and identify areas for improvement.

Increased revenue Through Higher-Quality Recyclables

Implementing AI in waste management at MRFs can improve the quality of the recyclables by reducing contamination. Contamination occurs when non-recyclable materials are mixed in with the recyclables, such as food waste, plastic bags, or other materials that cannot be recycled.?

Contamination reduces the value of the recyclables, making them less attractive to buyers, and can result in additional costs for disposal. By improving the accuracy of the sorting process, AI-based systems can reduce contamination and increase the quality of the recyclables, resulting in higher revenue for MRFs.

Conclusion

In conclusion, the use of AI in waste management at Material Recovery Facilities (MRFs) has the potential to revolutionise the entire waste management industry. Adopting AI technology can help MRFs optimise their operations and increase their waste sorting and processing efficiency, ultimately reducing waste disposal and environmental impact.

While there are still some challenges regarding cost and implementation, the potential benefits are clear. As we continue to seek ways to address the global waste crisis, AI-powered waste management solutions offer a promising path forward.

Seeing the challenges faced by the Material Recovery Facilities, Recykal has developed Smart Skan, an AI-powered technology that segregates recyclable and non-recyclable materials on a conveyor belt into separate categories & records the waste materials entering the facility. Want to know more about how Smart Skan can benefit your Material Recovery Facility? Click here to know more.