(AI) and the Internet of Things (Iot) in Sustainable Waste Management

The Role of Artificial Intelligence (AI) and the Internet of Things (IoT) in Sustainable Waste Management

Jack O’Sullivan Environmental Management Services Ireland

1. Introduction

Included among the most pressing problems facing human society on planet Earth at present are climate change, loss of biodiversity, and inefficient use of scarce resources.

Readers will be well aware of the recent IPCC special report on how close we are becoming to unstoppable climate chaos, as the world approaches a number of “tipping points” beyond which it may not be possible to prevent a seriously damaging rise in global temperatures. Widespread loss of species has also been highlighted in a recent WWF report – clearly showing that human societies and their impacts are the cause of a mass extinction.

Perhaps equally important, but less publicised, is the near-future shortage of some essential minerals, as much of what we extract from the Earth’s crust is used once and then discarded, to become waste.

In addition, waste of valuable materials, failure to re-use, repair and recycle these valuable is contributing to global warming, as the amount of energy required to extract and process the materials we use is many times greater than the amounts of energy needed to re-process discarded items and to recover the embodied materials.

Certain synthetic and composite materials are difficult to recycle; while the huge volume of discarded but potentially recyclable “wastes” presents another special challenge: how to separate each type of material, so that recyclers can be given relatively “pure” raw materials as a feedstock for re-processing.

Waste prevention and waste elimination are equally important components of an integrated waste management strategy, together with re-using, refurbishing and repairing discarded items. At each of these stages, information has to be obtained; and, based on this information and on personal or expert judgments, decisions have to be made about whether an item can be re-used, repaired, the components removed, or the entire item consigned to landfill or incineration as a last resort.

And before any of these processes are even considered, the design and manufacture of an item should have as a principal aim the goal of ensuring that the item can be easily disassembled, easily repaired, designed for long life, its components reusable to the maximum extent possible, and the materials comprising the item finally recycled or returned to harmlessly to the natural environment. This is the point where we make the transition from waste

1 management to the “Circular Economy”, an essential step in ensuring that we use materials and energy most efficiently.

The question we need to ask is – how can Artificial Intelligence (AI) and the Internet of Things (IoT) help to achieve these goals? But firstly, I would like to consider what AI and IoT have achieved, and what they can achieve, in a general sense.

2. Artificial Intelligence and the Internet of Things

Artificial Intelligence

A useful definition of what artificial intelligence can achieve is best expressed by stating that intelligent machines can perform functions similar to certain features of human intelligence, such as judgment, reasoning, recognition, learning and problem solving.

Early attempts at artificial intelligence may be said to go back as far as the 13th century, medieval Arab astrologers developed a device known as the Zairja, which used the 28 letters of the Arabic alphabet to signify 28 categories of philosophic thought. By combining number values associated with the letters and categories, new paths of insight and thought were created, i.e., new ideas could be created by mechanical means.1

The device was possibly introduced to Europe by the Catalan-Majorcan mystic, Ramon Llull (1234-1315) who became familiar with the zairja as a result of his travels and studies of Arab culture, and who used it as a prototype for his invention of the Ars Magna (The Great Art). We have to move forward in time to the mid-19th century before we find the next significant advance – the invention of the Analytic Engine by Charles Babbage and his co-worker Lady Ada Lovelace, who has been described as the first computer programmer.

A further century was to pass before the development of stored-programme computers which were able to retain a memory; and it is only in the last 60-70 years have computational devices and programming languages been developed which were powerful enough to test concepts about what intelligence actually is. Alan Turing’s 1950 seminal paper in the philosophy journal Mind is considered to be a major turning point in the history of AI, while the term artificial intelligence was first used at the Dartmouth Conference in 1956 – a meeting regarded as a historical event which opened up the field of artificial intelligence. In 1969, the first International Conference on Artificial Intelligence showed that AI had gained international recognition.

Another turning point came with the development of knowledge-based systems in the 1960s and early 1970s, followed by rapid progress in understanding common modes of reasoning that are not strictly deductive, such as case-based reasoning, analogy, induction, reasoning under uncertainty (fuzzy logic), and default reasoning.

1 https://history-computer.com/Dreamers/Llull.html

Work on “neural networks’ in the 1980s accelerated the further improvement of knowledge expert systems; and, by the beginning of the 21st century, along with the development of deep learning neural network systems, artificial intelligence successfully achieved reliable image recognition; and these developments led to the current widespread industrialisation and use of artificial intelligence in many areas of economic and social activities.

According to a recent briefing note prepared by the McKinsey Global Institute, “The time may have finally come for artificial intelligence after periods of hype followed by several “AI winters” over the past 60 years. AI now powers so many real-world applications, ranging from facial recognition to language translators and assistants like Siri and Alexa, that we barely notice it. Along with these consumer applications, companies across sectors are increasingly harnessing AI’s power in their operations. Embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation”.2

As just one example, the first voice to greet a visitor at the door of the Dubai Electricity and Water Authority (DEWA) Customer Care Centre at Ibn Battuta Mall is “Rammas” – as well as listening patiently to customers’ concerns, Rammas also knows the customer’s entire history with DEWA! Rammas is no super-human; he (or rather “it”) is a robot that runs on artificial intelligence; and DEWA is one of the 29% of large enterprises in the Arabian Gulf which said they are planning to adopt artificial intelligence in 2018.3

As a result of this relatively rapid adoption of AI, many economies in the Arabian Gulf region are expected to grow – in the UAE, this technology is expected to contribute up to 14% to the country’s GDP by 2030. The UAE will be followed by Saudi Arabia, where AI is expected to contribute 12.4% to GDP, 8.2 % in the GCC-4 – Bahrain, Kuwait, Oman and Qatar – and 7.7% in Egypt. By comparison, the contribution of AI to China’s GDP will be 26.1% and 14.5% in North America by 2030.4

2 The Promise and Challenge of the Age of Artificial Intelligence; Briefing Note prepared by the McKinsey Global Institute for the Tallinn Digital Summit, October 2018. Accessed on 30 November 2018 from https://www.mckinsey.com/featured-insights/artificial-intelligence/the- promise-and-challenge-of-the-age-of-artificial-intelligence. 3 Adding brains to machines, Middle East companies embrace AI. From https://www.thenational.ae/business/technology/adding-brains-to-machines-middle-east- companies-embrace-ai-1.778066. Accessed 04 December 2018. 4 From https://www.thenational.ae/business/technology/adding-brains-to-machines-middle- east-companies-embrace-ai-1.778066. Accessed 04 December 2018

3 The Internet of Things

Turning now to the “Internet of Things”, we find similar rapid developmental progress in recent decades, to the extent that it has been next to impossible in recent years not to come across the term ‘‘Internet of Things’’ (IoT) one way or another.5

While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses.

The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology on networked radio-frequency identification (RFID) infrastructures. Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) has defined the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving interoperable information and communication technologies.’’ 6

In the Internet of Things (IoT), a “thing” is an object of the physical world (physical things) or the information world (virtual things), which is capable of being identified and integrated into communication networks. A principal feature of the IoT is that it allows many devices to communicate with each other; a device being defined as an item of equipment with the necessary capability of communication, and the optional capabilities of identification, sensing, data capture, data storage, data processing and actuation. By exploiting these capabilities, the IoT makes full use of “things” to provide many kinds of services and many useful applications, while ensuring that security and privacy are maintained as far as technically possible.

Making sure that these services and applications work effectively and securely requires a high quality of design – of devices, networks and systems, to provide the following features: i) inter-operability between devices; ii) autonomic or self-managing networks (including self-management, self- configuring, self-healing, self-optimizing and self-protecting techniques and/or mechanisms); iii) automatic provision of services (by capturing, communicating and automatically processing data);

5 Internet of Things -- Technology and Value Added; Felix Wortmann and Kristina Flüchter, Bus Inf Syst Eng 57(3):221–224 (2015). Published online: 27 March 2015. Accessed from https://www.alexandria.unisg.ch/252999/1/s12599-015-0383-3.pdf 6 ITU-T, The Telecommunication Standardization Sector of ITU; Series Y; Recommendation Y.2060 (renumbered as Y.4000 on 2016-02-05: Global Information Infrastructure, Internet Protocol Aspects and Next-Generation Networks – Frameworks and functional architecture models: Overview of the Internet of things; June 2012. Accessed on 03 December 2018 from https://www.itu.int/rec/T-REC-Y.2060-201206-I.

4 iv) support for location-based capabilities, to sense and track the location of a device automatically; v) maintenance of confidentiality, authenticity and integrity of both data and services; and, vi) protection of privacy where necessary.

A recent report by the International Telecommunication Union (ITU) and Cisco Systems (a major international electronics and systems company) has stated that: “After more than a decade of discussion and anticipation, the Internet of Things is now firmly on its way… Connecting things, as well as people, offers prospects of new ways of monitoring situations, learning and responding in real-time” 7

This ITU and Cisco report gives many examples of where the Internet of Things has assisted water and sanitation projects, agriculture, pollution mitigation, natural resource management, improvements in energy efficiency, and is now helping to make societies more resilient to the damaging effects of climate change.

At this point we might ask how Artificial Intelligence and the Internet of Things can be applied to resolve the problems of resource utilisation, waste prevention and waste management which we identified in the introduction above.

3. A Brief Summary of Some Features of the “Waste Industry” and the Aim of Sustainable Waste Management in many countries of the world, waste management has become the preserve of the “waste industry”, members of which collect, process, separate, use landfilling or incineration to dispose of waste, or engage in recycling operations. Other activities, such as re-using, refurbishing or repairing discarded items, are carried out mainly by small organisations of citizens, or by social enterprises for the purpose of providing employment or ensuring that waste is appropriately recycled, or is not unnecessarily produced in the first place.

Every year, more than 11 billion tons of solid waste is collected worldwide and decay of the organic proportion of solid waste contributes to about 5% (1,460 mt CO2 eq) of global Greenhouse Gas (GHG) emissions. Methane from landfills represents 12% of total global methane emissions; and landfills are responsible for almost half of the methane emissions attributed to the municipal waste sector in 2010 (IPCC, 2007). The quantity of methane emitted from landfills varies from country to country, depending on waste composition, climatic conditions (ambient temperature, precipitation) and waste disposal practices.

7 Harnessing the Internet of Things for Global Development – A Contribution to the UN Broadband Commission for Sustainable Development. International Telecommunication Union, Place des Nations, CH-1211 Geneva 20, Switzerland; 2016. Accessed on-line on 03 December 2018 from https://www.itu.int/en/action/broadband/Documents/Harnessing-IoT- Global-Development.pdf.

Types of waste include: • Construction and demolition (“C&D”) waste (most of which is inert, e.g., soil, stones, rubble; but may also contain hazardous materials); • Municipal solid waste (which includes wastes from households, shops, offices and street cleaning); • Agricultural waste (of animal and vegetable origin); • Industrial and commercial wastes; • Sludges from treatment of potable water, wastewater and industrial processes; • Wastewater from all sources, including homes and industry; • Hazardous wastes (including, in some countries, hazardous wastes from the oil & gas industry); • Medical waste (hospital and clinical, including infectious materials and discarded or end-of-life equipment and instruments); and, • Waste electrical and electronic equipment (WEEE). While it is obvious that there is a considerable overlap between these categories, they are nevertheless useful.

The aim of sustainable waste management is to prevent the generation of waste, to repair or re-use discarded materials and objects where possible, to recycle the components or materials, and to make use of organic materials by anaerobic digestion or aerobic composting (which may be considered a form of recycling, as the products are returned to the soil as fertilisers and soil conditioners). These aims are generally presented in the form of the “waste hierarchy”, an example of which is given in Figure 1 below.

The only long-term sustainable solution to municipal, industrial and agricultural waste management is to eliminate the production of materials which are toxic and which cannot be naturally biodegraded, re-used, recycled or re-processed as secondary raw materials for other productive industrial or commercial uses. Waste can also be eliminated at source through product design and producer responsibility, together with waste reduction strategies further down the supply chain such as cleaner production, product dismantling, recycling, re-use and composting.

Sustainable waste management can go further, and should go further, leading to “Zero Waste”. Instead of organising systems that efficiently dispose of or recycle our wastes, we can and must learn from nature to design systems of production and consumption that have little or no waste to begin with – this will result not only in a saving of scarce resources, but will re-adjust our relationship to the earth’s material assets from a linear to a cyclical one, enhancing our ability to live comfortably while reducing environmental damage. This is the principle behind the “Circular Economy”.

The goal of environmentally sustainable development requires, as a basic principle, that human communities must behave like natural ones, living comfortably within the natural flow of energy from the sun and plants, producing

6 no wastes which cannot be recycled back into the earth’s systems, and guided by new economic values which are in harmony with personal and ecological values. In nature, the waste products of every living organism serve as raw materials to be transformed by other living creatures, or benefit the planet in other ways. Our policies and our practices need to mirror this ecological reality.

Figure 1. An example of the Waste Hierarchy (from Waste and Environment Annual Report, Abu Dhabi, 2016; accessed on-line on 04 December 2018 from https://www.ead.ae/Publications/Waste%20and%20Environment%20Annual%20Rep ort%202016/Waste%20and%20Environemnt%20Annual%20Report%202016.pdf)

4. Ways in which Artificial Intelligence and the Internet of Things can Contribute to Sustainable Waste Management

To consider ways in which AI and the IoT can contribute to sustainable waste management we need to examine how every activity which leads to the generation of discarded materials and objects, and to the eventual recovery and re-use of their components and materials, can be assisted or made more efficient by AI and IoT.

In the table below, we have indicated some ways in which this might be done – some are practical, and others speculative; but we hope that they will generate further ideas and discussions in this rapidly evolving area.

Ways in which Artificial Intelligence and the Internet of Things can Contribute to Sustainable Waste Management 1. Waste prevention In attempting to achieve the prevention and elimination of and elimination waste, there are many policy choices to be made, complex issues must be analysed, and alternative pathways towards achieving a desired or agreed objective must be compared technically, environmentally, economically and socially. While these analyses and comparisons of alternatives can be performed very well by multi-disciplinary teamwork by experienced persons, the process can be helped by system modelling, and by using AI to rapidly examine a range of alternatives. A further advantage is that AI can be used to assist countries where the necessary expertise is in short supply or unavailable, given that many waste management problems are common to most countries, with only the parameters (e.g., amounts and types of waste generated, locations, infrastructure, society and culture) being different. 2. Waste collection Commonly used navigation or route planning tools, available on all mobile phones, use a relatively simple form of AI to determine the best route, based on parameters input by the user. Similar AI systems, augmented by greater detail about amounts of waste collected at each stopping point, can be used to determine the most efficient routes for waste collection vehicles, thereby improving fuel efficiency and reducing collection time. 3. Waste separation In countries where waste materials are not carefully segregated at source (e.g., in homes), mixed recyclables are generally collected in bulk and brought to a “waste transfer facility” where the mixed materials are separated into categories (e.g., paper, cardboard, different types of plastics, textiles, etc.) by workers standing at a conveyor belt. Given that this is a tiresome and unpleasant job, with the risk that mistakes can be made, we find that mechanical devices with a range of sensors, linked to an AI system, are able to perform this task more efficiently and with better results, including better recovery of recyclables from the mixed waste stream. Robotic arms guided by cameras and artificial intelligence are helping to make municipal recycling facilities run more efficiently. Through deep learning technology, robotic sorters use a vision system to see the material, AI to “think” and identify each item, and a robotic arm to pick up specific items.

8 Ways in which Artificial Intelligence and the Internet of Things can Contribute to Sustainable Waste Management Waste separation, A data base of thousands of images of garbage in continued. different positions helps to train the AI neural-network, ultimately allowing it to learn on its own. The same computer vision cameras that power the robotic sorters can also be used to configure the layout of the maze of conveyor belts and sorting systems, thereby optimising the sorting equipment in real time to match the variable flow of materials coming into the facility.8 4. The intelligent trash We have heard about the AI equipped household can or rubbish bin. refrigerator which can tell us what foods it contains, whether any of these foods are approaching their “sell-by date”, and whether we have to go shopping for more. The intelligent (AI-equipped) rubbish bin or trash can equipped with AI programmes and IoT sensors can measure the amount of waste thrown into it and can send this data to a disposal system for processing. The system categorises the data into the type of garbage, quantity of each type, and the most appropriate we way dealing with it; and the system can refine itself or learn over time by studying historical records to improve its efficiency.9 5. Disassembly of Nearly all electronic devices for the consumer market are discarded electronic partly assembled by robotic devices which carry out the equipment (WEEE). necessary programmed tasks. On the other hand, it seems that the disassembly of these devices at the end of their useful lives is undertaken by workers in developing countries, who are exposed to toxic materials while working. Considering that the disassembly of end-of-life devices is a more complex task, especially if the requirement to maximise the recovery of re-usable components is an additional and necessary objective, we suggest that AI coupled with a range of sensing devices and actuators (to manipulate the object which is being worked on), could undertake this task more rapidly and safely. In a recent company report, Apple said it has started using teams of robots, each with 29 arms, to take apart iPhone 6s in California and the Netherlands. This process, according to Apple, is more efficient at recovering materials than traditional methods like shredding.10 These machines work 24x7, and are more efficient than the humans they replace.

8 Dumpster diving robots: Using AI for smart recycling, GE Reports, July 10, 2017. This recycling robot uses artificial intelligence to sort your recyclables; from FORBES Magazine, April 4, 2017. 9 BBN Times, 3 November 2018. 10 Apple’s Annual Green Bond Impact Report, 2017 update. Apple Inc, Cupertino California. Quoted in the Guardian, 21 March 2017.

9 Ways in which Artificial Intelligence and the Internet of Things can Contribute to Sustainable Waste Management 6. Detection of Dumping of waste illegally, often in relatively remote irregularities in areas, and the exportation and importation of waste under waste management, falsified documents, are among the problems associated e.g., illegal dumping, with waste management – problems continually faced by hidden dumps of regulatory agencies or authorities, even in developed waste, etc. countries. Detection of these transgressions is not easy, and the problems may come to light only after the perpetrator has disappeared or has gone beyond the reach of the law. Early detection (and prevention) requires the identification of anomalous or irregular behaviour (e.g., unusual movements of waste transport vehicles, unexplainable changes in waste-related statistics, etc.); and this is particularly difficult in cases of single or isolated instances where there is no clear pattern of illegal activity. Such instances carry a high risk if, for example, a single truck- load of very hazardous or toxic waste were dumped where it could contaminate an entire aquifer or a river system supplying water to a city. Usually such cases are detected only after the damage is done. Using AI can help regulatory authorities or agencies to monitor, detect and assess the risk of such activities, provided that a large amount of data is available which can be mined to discover anomalous activities and identify potential instances of illegal activity. Collecting the data is major task, but the use of connected devices – the Iot – can provide possible solutions. Artificial intelligence is particularly suited to identifying abnormal behaviour within a data set; this type of anomaly detection identifies patterns in a way which a human user cannot do.11 7. Designing for re-use, Intelligent design by machine is common in the electronics repairing and industry, where the design of a complex circuit is recycling. undertaken by specialised computer software. By applying this approach to the design of new items of equipment for commercial or consumer use, and by including a requirement that the item should be easy to assemble, repair and dismantle at the end of its life, the use of AI can greatly assist the design engineer. An indication of how far AI has progressed in being able to manipulate potential “objects” in 3-dimensional space may be found in the recent announcement that Google’s DeepMind and its latest AI program, AlphaFold, had successfully predicted the folded 3D shapes of proteins from the sequence of amino acids which comprise the protein’s basic structure.12

11 Network and performance monitoring, and how anomaly detection is keeping enterprises secure Information age. Information Age; Opinion, 23 October 2018. https://www.information-age.com/network-and-performance-monitoring-123475838/ 12 Google’s DeepMind predicts 3D shapes of proteins. The Guardian, 02 December 2018.

10 5. Conclusion

There is no doubt that Artificial Intelligence and the Internet of Things have an increasing important and innovative role to play in providing more sustainable waste management, dealing with today’s wastes, and moving forward to a zero waste future based on the circular economy.

Environmental Management Services Comhairleoirí Comhshaoil

Environmental and Planning Consultants Outer Courtyard, Tullynally, Castlepollard, County Westmeath Clós Seachtrach, Tulaigh an Eallaigh, Baile na gCros, Co. an Iarmhí Telephone +353 44 966 2222 Mobile +353 86 381 9811 E-mail [email protected]

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