The world is a gigantic landfill! Everyday tons of waste are generated from various households, hospitals, industries, construction and demolition sites and more. While today we have numerous ways to get rid of the accumulated waste, it still ends up affecting the safety and sustainability of the ecological system. Therefore, the best alternative is to reuse and recycle as much waste as possible. And offering an extra pair of hand in this are waste sorting and recycling robots.
Waste recycling is an important way of saving energy and materials in the production process. In general cases recyclable objects are mixed with unrecyclable objects, which raises a need for identification and classification. This paper proposes a convolutional neural network (CNN) model to complete both tasks. The model uses transfer learning from a pretrained Resnet-50 CNN to complete feature extraction. A subsequent fully connected layer for classification was trained on the augmented TrashNet dataset . In the application, sliding-window is used for image segmentation in the pre-classification stage. In the post-classification stage, the labelled sample points are integrated with Gaussian Clustering to locate the object. The resulting model has achieved an overall detection rate of 48.4% in simulation and final classification accuracy of 92.4%.
Waste management is one of the world's most pressing issues, however Peruza and Dots, two Latvian companies, have created a prototype through the use of AI to increase the efficiency of plastic waste. Peruza, an equipment manufacturing and process engineering company, has collaborated with Dots, a technology company also based in Latvia, to create a prototype system which is able to recognize and collects various types of packaging materials in an effort to contribute to the EU's upcoming single plastic directive. Peruza had stated that 10 countries within Europe have already implemented deposit return schemes and that we should expect more to come in the future. This is in line with the EU's goal for 2030 for all packaging used in the market to be recyclable or reusable. Robert Dlohi, CEO of Peruza, stated, "The problem of existing devices is that they can collect individual types of packaging. They use a barcode for recognition. Our system can currently recognize, collect and sort 5.0 liter and/or 1.5 liter Pet bottles, and 5 liter plastic bottles, 1.5 liter plastic canisters, as well as aluminum cans, cardboard Tetra Pak, glass bottles and jars."
This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single- and multi-objective heuristics based on the PageRank method and two mutiobjective evolutionary algorithms are proposed. Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives. The computed results improve over the current planning in Montevideo and provide a reasonable budget cost and quality of service for Bahia Blanca.
Rolnick, David, Donti, Priya L., Kaack, Lynn H., Kochanski, Kelly, Lacoste, Alexandre, Sankaran, Kris, Ross, Andrew Slavin, Milojevic-Dupont, Nikola, Jaques, Natasha, Waldman-Brown, Anna, Luccioni, Alexandra, Maharaj, Tegan, Sherwin, Evan D., Mukkavilli, S. Karthik, Kording, Konrad P., Gomes, Carla, Ng, Andrew Y., Hassabis, Demis, Platt, John C., Creutzig, Felix, Chayes, Jennifer, Bengio, Yoshua
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
Automated facial recognition poses one of the greatest threats to individual freedom and should be banned from use in public spaces, according to the director of the campaign group Liberty. Martha Spurrier, a human rights lawyer, said the technology had such fundamental problems that, despite police enthusiasm for the equipment, its use on the streets should not be permitted. She said: "I don't think it should ever be used. It is one of, if not the, greatest threats to individual freedom, partly because of the intimacy of the information it takes and hands to the state without your consent, and without even your knowledge, and partly because you don't know what is done with that information." Police in England and Wales have used automated facial recognition (AFR) to scan crowds for suspected criminals in trials in city centres, at music festivals, sports events and elsewhere.
The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system, based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is the cost reduction of the waste collection service by means of the minimization in distance traveled by any truck to collect a container, hence the fuel consumption. At the same time the quality of service to the citizen is increased avoiding the annoying overflows of containers thanks to the accurate fill level predictions performed by BIN-CT. In this article we show the features of our software system, illustrating it operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary visits to containers, reduces the distance traveled to collect a container and therefore we obtain a reduction of total costs and harmful emissions thrown to the atmosphere.
Municipal solid waste management (MSWM) is a challenging issue of urban development in developing countries. Each country having different socio-economic-environmental background, might not accept a particular disposal method as the optimal choice. Selection of suitable disposal method in MSWM, under vague and imprecise information can be considered as multi criteria decision making problem (MCDM). In the present paper, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methodology is extended based on credibility theory for evaluating the performances of MSW disposal methods under some criteria fixed by experts. The proposed model helps decision makers to choose a preferable alternative for their municipal area. A sensitivity analysis by our proposed model confirms this fact.
Software as a service platform Jodone's latest design makes sorting recyclables from trash faster, more efficient and ultimately, more profitable. Made for use with industry standard robots from multiple suppliers, the interface turns the acts of recognizing and categorizing recyclables into a game. As waste travels along a conveyer belt, workers swipe a touch screen to classify items as recyclable. The instructions are sent wirelessly to robotic arms that then pick and sort the appropriate pieces. In the laboratory, the collaborative process achieved a recycling rate eight times higher than humans alone and with a 95 percent accuracy rate.
Béjar, Ramón (Universitat de Lleida) | Fernández, César (Universitat de Lleida) | Mateu, Carles (Universitat de Lleida) | Manyà, Felip (IIIA-CSIC) | Sole-Mauri, Francina (RosRoca Envirotec) | Vidal, David (RosRoca Envirotec)
One of the most challenging problems on modern urban planning and one of the goals to be solved for smart city design is that of urban waste disposal. Given urban population growth, and that the amount of waste generated by each of us citizens is also growing, the total amount of waste to be collected and treated is growing dramatically (EPA 2011), becoming one sensitive issue for local governments. A modern technique for waste collection that is steadily being adopted is automated vacuum waste collection. This technology uses air suction on a closed network of underground pipes to move waste from the collection points to the processing station, reducing greenhouse gas emissions as well as inconveniences to citizens (odors, noise, . . . ) and allowing better waste reuse and recycling. This technique is open to optimize energy consumption because moving huge amounts of waste by air impulsion requires a lot of electric power. The described problem challenge here is, precisely, that of organizing and scheduling waste collection to minimize the amount of energy per ton of collected waste in such a system via the use of Artificial Intelligence techniques. This kind of problems are an inviting opportunity to showcase the possibilities that AI for Computational Sustainability offers.