This article was written by Anu Singh, pursuing the Training program on Using AI for Business Growth Course from Skill Arbitrage, and edited by Koushik Chittella.

Introduction 

This article aims at compiling and presenting everything that one needs to know about waste management, including the importance of waste management, different ways to manage waste, and the hindrances faced by the waste industry. This article also discusses the need for using AI in waste management and how AI is revolutionising waste management via the latest technologies. In essence, the emphasis is to look at waste management in totality and as a resource that needs to be managed in a competitive and innovative way for the benefit of the environment and humanity as a whole.

Waste management: meaning

Waste management refers to all the methods and processes of dealing with waste at every stage, from generation, collection, transportation, treatment, and disposal. Managing waste is a complicated process, as each stage is important and comes with its own problems. Waste management is divided into formal and informal waste management. 

Formal waste management

Formal waste management refers to the management of waste by both public and private service providers that handle waste from the time it is discarded to the time it is managed. These service providers are registered and regulated bodies that comply with the laws and rules set by their respective governments. 

Informal waste management

Individuals such as ragpickers and organisations that are not registered and which do not adhere to government rules and regulations come under the category of informal waste management. These individuals often face health hazards due to the lack of proper working conditions and technical know-how. There are approximately 15 million people around the world who are involved in informal waste recycling. Unlike formal waste management, this can be detrimental for our environment as most of these workers and organisations are not aware of the proper methodology and technology to segregate and manage waste.

Methods of waste management

There are many beneficial ways to handle waste, and some are better than the others.

• Recycling: Recycling refers to the conversion of waste into products that can be used again and again instead of being discarded after just one use. Most people prefer this method over disposal, as it not only leads to less waste generation but also saves the resources needed to produce more goods. Yet, only 9% of plastic is recycled worldwide. Recycling is more common in wealthier countries, as the literacy rate is high in these countries and the general public is more aware of environmental degradation being caused by plastics.
• Incineration: In this process, waste, which is dangerous, is burnt at high temperatures to free them from pollutants. It is a powerful method for waste management as it not only reduces the amount of waste generated but also leads to heat generation, which can further be utilised for energy production.
• Landfills: Landfilling is a traditional method. In this method, waste is discarded into areas called landfills. This discarded waste is then covered with soil to prevent any leakage of harmful materials into the surroundings. This involves careful planning and implementation.
• Composting: Composting is a natural process wherein natural and biotic waste is converted into a source of nourishment for the soil. This not only promotes healthy growth in plants but also paves the way towards sustainability. It is the biological decomposition of organic matter under controlled conditions.
• Waste-to-energy: In this process, non-dangerous waste is burnt to generate energy in the form of electricity or steam. rather than dumping it in a landfill. Currently, many developed countries are using the waste-to-energy method. As per a report by Grandview Research, in Europe, the waste-to-energy market size was estimated to be $13.88 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 14.4% from 2023 to 2030.

Importance of waste management

As per a report, over two billion metric tonnes of MSW are generated globally every year. This is projected to grow by about 70% by 2050. Till the time waste is being generated recklessly, its management is going to be of paramount importance. Waste management helps in creating a circular economy, in which commodities and their substances are used and reused instead of being discarded after just one use. 

Public Health

Waste management is proportionally related to public health and the general well-being of individuals. Proper waste management ensures that our air, groundwater, and soil are not contaminated, leading to a higher quality of food products and a healthier human and wildlife. Efficient waste management practices reduce the occurrence of diseases, leading to greater life expectancy and low mortality rates among workers and communities handling waste.

Conservation of Resources

Waste management leads to resource optimisation, reducing the pressure on natural resources that are finite. This also leads to greater imagination and vision among those who are dedicated towards finding new and groundbreaking solutions to the existing problem of environmental degradation. By controlling the emission of greenhouse gases and creating energy in the form of electricity or steam through waste, waste management also helps in protecting our environment.

Economic Benefits

Waste management provides employment in various sectors, such as recycling, collection of waste, and its sorting. Moreover, the retrieval of substances from waste management contributes towards greater economic development as these are in turn supplied to various industries, enhancing their productivity and cost reduction. 

Challenges faced in managing waste

• The World Bank estimates that at least 33% of today’s waste is not managed properly because of dumping and burning, leading to adverse effects on soil, groundwater, and the atmosphere. The fact that our food, in terms of crops and livestock, comes from contaminated sources is a matter of great concern.
• Since building a proper system for waste management is cost-intensive, many developing countries are not able to invest in it. This leads to reduced efficiency and increased operational costs with limited availability for service expansion. Lack of adequate infrastructure leads to illegal dumping and littering, causing health concerns and greater contamination.
• In the realm of rising population and increasing industrialisation, waste materials generated are more than that can be managed by manual labour alone. If left uncared for, this leads to serious health hazards; hence, automation is the need of the hour.
• There is a lack of general understanding and proper education about waste disposal among the citizens. Due to which unknowingly people mix polluted and non-recyclable items with the recyclable ones, reducing their quality and increasing their processing costs. The average recycling contamination rate is 25%, or 1 in 4 items.
• There is a shortage of a technically educated and experienced workforce, required for a lot of sensitive and dangerous tasks. As a result, there is also a dearth of people who can handle and manage complicated machinery. This leads to greater costs of operation, mismanaged collection, lower productivity, and reduced resource optimisation in the waste management sector.

Artificial Intelligence

Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, and solving problems. Artificial intelligence not only leads to a more changed and revolutionised world but also makes operations more cost-effective. Less human involvement also reduces the scope of mistakes and errors.

AI in waste management

Human beings are having a negative and poisoning impact on oceans and the environment as a whole. We need a solution to creating or recreating the clean oceans that we had. Ecosystems are very complex, wherein everything interacts with everything, and we don’t know the impact of putting things into the atmosphere and the resulting changes thereafter. There is a need for running large geoengineering experiments on a global scale to understand how a whole ecosystem works. AI, with the help of sensors and better machine learning, can do this in a way that experiments can be conducted, inferences can be drawn, and better decisions can be taken. Artificial intelligence is prevalent across many industries, from healthcare to advertising. When implemented properly, AI can be very beneficial in waste management. According to the World Bank, annual waste management is expected to rise by 73% to about 3.88 billion metric tonnes from 2020 to 2050. This is a staggering figure, and the clock is ticking. The world cannot afford to wait any longer for efficient waste management if we want a sustainable future for generations to come. All over the world, waste management has predominantly been labour intensive, but this is changing as a result of AI. Most countries are now waking up to the advantages of incorporating AI into the processing and management of waste. Countries like South Korea, Finland, and Australia have begun tackling 3 out of the 4 steps required in managing waste with the help of AI. AI can be employed in waste management to enhance efficiency, optimise processes, and contribute to sustainable practices.

Ways in which AI can be utilised in waste management

AI can be utilised in waste management in the following ways:

• Smart Bin Technology: This technology installs sensors in waste bins to monitor and measure the level of waste. It uses AI algorithms to analyse this data and predict fill times. Hence, this results in less distance travelled, leading to optimisation of routes, reduction in fuel consumption, and carbon emissions causing significant cost savings. It sorts garbage soon after disposal via cameras, leading to no human intervention and very little or no contamination. It is preferred in places with a greater footfall of people, like hospitals, airports, stadiums, and commercial buildings. A very good example of this technology is TrashBot.
• Material Recovery Facility: A material recovery facility (MRF) is a unique system used for sorting and recycling of waste. Here sorting is done in a phased manner so that each item that is reusable can be sold to manufacturers, leading not only to reduced garbage ending up in landfills but also to greater resource optimisation and cost reduction for industries. Firstly, the items that cannot be recycled and are dangerous and contaminated are removed and sent for disposal. Secondly, cardboards are separated from jars, and papers are separated by blowing air on the conveyor belt. Thirdly, metals are separated with the help of electromagnets, and glasses are crushed for easy transportation. Lastly, plastics are separated using infrared technology. 
• Automated Sorting: In the past, sorting recyclable materials has relied heavily on manual labour, leading to errors and wastage of time. But with AI-powered sorting systems that utilise mind-boggling computer vision and machine learning algorithms, things have changed for good. These systems accurately identify and categorise recyclables with unmatched precision and efficiency. Now high-resolution cameras capture vast amounts of visual data, enabling AI algorithms to distinguish plastics, paper, glass, metal, and other materials effortlessly. This innovative technology ensures efficient segregation at recycling facilities. AMP Robots have taken recycling to the next level with their AI-guided robotic systems. Over time, they adapt and improve their accuracy in identifying new objects. AI-powered image recognition systems are being employed to automatically sort waste into categories such as recyclable, organic, and non-recyclable. Robotic systems equipped with AI vision are used to sort items on conveyor belts at recycling facilities apart from cleaning and maintenance of waste facilities. These systems not only streamline recycling but also reduce contamination, ensuring high-quality recycled materials that are ready for a second life.  
• Automated Waste Collection Systems (AWCS): These state-of-the-art systems employ a network of underground pneumatic tubes, enabling the transportation of waste from various establishments directly to centralised collection facilities, eliminating the conventional reliance on collection vehicles.
• Landfill Technologies: Landfill technologies have undergone significant advancements. Today’s advanced landfill technologies include enhanced liner systems to prevent leachate leakage, efficient gas collection systems to capture and utilise methane emissions, and optimised landfill covers to control odour and manage gas emissions.
• E-waste Management Technologies: Modern e-waste management technologies facilitate the efficient collection, sorting, and processing of electronic waste, enabling the recovery of valuable materials and the safe disposal of hazardous components. Such technologies encompass advanced processes like hydrometallurgical and biotechnological methods.
• Hazardous Waste Management Technologies: Hazardous Waste Management Technologies manage, treat, and dispose of hazardous waste with utmost precision and care. Innovative technologies in this sector range from advanced thermal treatment processes to sophisticated chemical stabilisation techniques. These processes are aimed at neutralising the harmful effects of hazardous waste, facilitating its safe disposal, and minimising risks.
• Predictive Analytics: AI is used to analyse historical data and predict future waste generation patterns, optimise collection schedules, and allocate resources based on predictive analytics.
• Monitoring and Reporting: AI can be used in monitoring landfill sites for environmental compliance and potential issues, generating real-time reports on waste generation, recycling rates, and landfill usage.
• Optimising Recycling Processes: AI can improve recycling efficiency by identifying and extracting recyclable materials from complex waste streams. It enhances the operations at a recycling facility by automating tasks such as quality control and material separation.
• Demand Forecasting and Supply Chain Optimisation: AI algorithms can optimise the supply chain for waste management by analysing historical data, market trends, and environmental factors to forecast the demand for recycling materials. This information helps businesses to make informed decisions about production, inventory management, and resource allocation. Companies can now track the origin, composition, and processing history of recycled materials throughout the supply chain. Transparency becomes the new norm, promoting trust and ethical practices.
• Smart Waste Management Platforms: AI can help in developing platforms that integrate data from various sources, including sensors, IOT devices, and waste management facilities, to provide real-time insights and analytics for better decision-making.
• Blockchain for Waste Tracking: AI can implement blockchain technology to create a transparent system for tracking waste from its source to its final disposal, assisting in education and outreach.
• AI-powered Chatbots for Virtual Assistance: AI-powered chatbots can help in providing information and guidance on proper waste disposal and recycling in an easy-to-understand way. They can assist municipalities and organisations to take informed, data-driven decisions leading to improved efficiency, reduced operational costs, and better outcomes across various departments.
• Predictive Maintenance: Waste management machines are subject to a lot of wear and tear, which can result in breakdowns and failures. AI-powered machines can predict when a machine is likely to fail and notify the maintenance team to perform preventive maintenance.
• Waste Reduction: AI can identify areas where waste can be reduced, such as packaging, production, and consumption. By analysing data, AI can help companies identify areas where waste is being generated and develop strategies to reduce waste.
• Energy Recovery: AI-powered machines can monitor the waste-to-energy process and optimise it to produce more energy while reducing waste.

Incorporation of AI in waste management

Countries across the globe are making substantial investments in research and implementation of AI in the area of waste management. This is because they are aware of its advantages and importance. China has set a goal of becoming a $150 billion AI global leader by 2030. The United States of America has channelled $10 billion in venture capital funding towards AI. UK has channelled almost 38% of the entire venture capital into AI. Canada has committed $125 million for research in AI. Russia is investing $12.5 million annually into AI.

• IBM, with their groundbreaking recycling technology called VolCat, is a game changer. It leverages AI and machine learning algorithms to analyse the chemical properties of plastic waste and then recommends the most effective recycling methods, revolutionising the field of plastic recycling and leading to environmental conservation.
• Zen Robotics, a Finnish company, has harnessed the power of AI to revolutionise recycling in the construction and demolition industry. Their AI-powered robotics system identifies and sorts different materials like wood, concrete, and metals, improving recycling efficiency and reducing the need for manual labour.
• Tomra, a German company at the forefront of recycling technology, has introduced a cutting-edge AI-powered system known as Autosort. It identifies and eliminates contaminants from recycling streams with unparalleled precision, apart from separating various materials like plastic, metals, and paper. It detects and removes non-recyclable materials and hazardous substances.
• Pellenc ST, a French company, identifies and separates different types of plastics based on their chemical composition and colour. The result is high-quality recycled plastics ready to be transformed into a myriad of new products.

India and AI in waste management

Looking at the global trend and the extent to which a populated country like India contributes to waste generation, it has started making advancements to incorporate AI in waste management. Products like waste sorters are being developed and manufactured. A lot of startups have also come up to help people and organisations to manage waste scientifically. Here are a few examples.

• NAMO E-waste Company collects and segregates e-waste from different states and union territories in India. They also sell refurbished devices online. Waste that cannot be used is broken down, and materials like copper, iron, and aluminium are sold to factories. 
• The GEM Enviro Management company collects plastic used for wrapping and packing goods from various organisations and offices. This plastic is then used to produce consumer goods like caps, bags, and apparel.
• ExtraCarbon waste management company deals in scrap services contributing towards recycling and a sustainable lifestyle.
• Scarpshala helps in recycling trash. They use trash to make home utility and decorative products. They sell these products both online and offline.
• Paperman is a Chennai-based organisation. They not only collect items that can be recycled but also spread awareness among students in schools through programs.
• Ishitva Robotic Systems (IRS) is an Ahmedabad-based startup. They have come up with an IOT-enabled device called Sanjivani. It is capable of differentiating between recyclable and non-recyclable waste.

Challenges and hindrances

• AI requires very high initial investments, and not all countries, especially the third-world countries, can afford to set aside huge sums of money for technological innovations.
• AI-powered technologies are complicated and require technically skilled personnel to manage, interpret, implement, and maintain them. Developing countries, where providing basic education to its citizens is a challenge, find it difficult to hire such a trained and educated workforce.
• Many labour-intensive countries also find it difficult to implement AI systems in different sectors of governance and management, as there is a risk of potential resistance and backlash from the general public regarding job replacement.
• AI uses complex algorithms and substantial data for its functioning, leading to concerns pertaining to privacy and security within organisations and departments.

Suggested tips and techniques

As the current era is marked by rapid technological advancements, these latest waste management technologies provide us with hope and innovation. They promise a future where waste management will align with the global aspirations of sustainability and environmental preservation so as to bring harmony between technology and ecology. Below are a few suggestions that could lead to responsible planning and implementation of waste management methods and technologies.

• Prevention is better than cure; hence, the “Three Rs” – reduce, reuse, and recycle, should be strictly adhered to. When we restrict ourselves from using items that are meant for single use, we reduce waste generation. Usage of products multiple times leads to resource optimisation and a cleaner environment.
• Regulation and monitoring are a must in any sector of a country. This not only holds the departments accountable but also leads to greater operational transparency, prompt decision-making, and increased efficiency.
• Governments and organisations should initiate pilot projects to check and showcase the feasibility and benefits of AI-driven waste management solutions. This will help in building public awareness and in assessing its economic and environmental impacts.
• Training programs and capacity-building initiatives should be offered to waste management professionals to enhance their understanding of AI technologies leading to their effective implementation.
• Continued research and innovation should be carried out in collaboration with researchers and academicians to keep up with the rapid advancements in AI technologies. This will lead to finding need-based models and algorithms suitable for specific waste management challenges.
• Governments, waste management agencies, and organisations should invest actively in building required infrastructure like smart sensors, IoT devices, and other technologies to support AI-driven waste management solutions.
• Efforts should be made to collect, share, and maintain high-quality waste-related data for accurate predictions and informed decision-making. As AI systems rely on data, it is crucial to address privacy concerns associated with data collection, processing, and storage.
• Governments, companies, and organisations should initiate public awareness campaigns via social media, print media, and workshops to educate the general public about proper waste disposal practices in order to reduce human intervention in the contamination of waste. 

Conclusion

It is clear that AI has immense potential for revolutionising the waste management industry. The challenges faced require innovative solutions, and AI can help provide them and contribute to a cleaner and healthier future. With research, collaboration, and innovation, the mammoth task of recycling can be eased. By introducing more AI technologies, data analysis, and machine learning, recycling can be developed into a joyful task rather than just a mundane responsibility. The conscious decisions taken today will play an important role in reducing waste, preserving resources, and building a sustainable world for generations to come. Thus, finding innovative answers to the coming challenges and effective usage of the latest waste management practices is not just crucial but vital.

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