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      BIN-CT: Urban Waste Collection based in Predicting the Container Fill Level

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          Abstract

          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.

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          Recycling and recovery routes of plastic solid waste (PSW): a review.

          Plastic solid waste (PSW) presents challenges and opportunities to societies regardless of their sustainability awareness and technological advances. In this paper, recent progress in the recycling and recovery of PSW is reviewed. A special emphasis is paid on waste generated from polyolefinic sources, which makes up a great percentage of our daily single-life cycle plastic products. The four routes of PSW treatment are detailed and discussed covering primary (re-extrusion), secondary (mechanical), tertiary (chemical) and quaternary (energy recovery) schemes and technologies. Primary recycling, which involves the re-introduction of clean scrap of single polymer to the extrusion cycle in order to produce products of the similar material, is commonly applied in the processing line itself but rarely applied among recyclers, as recycling materials rarely possess the required quality. The various waste products, consisting of either end-of-life or production (scrap) waste, are the feedstock of secondary techniques, thereby generally reduced in size to a more desirable shape and form, such as pellets, flakes or powders, depending on the source, shape and usability. Tertiary treatment schemes have contributed greatly to the recycling status of PSW in recent years. Advanced thermo-chemical treatment methods cover a wide range of technologies and produce either fuels or petrochemical feedstock. Nowadays, non-catalytic thermal cracking (thermolysis) is receiving renewed attention, due to the fact of added value on a crude oil barrel and its very valuable yielded products. But a fact remains that advanced thermo-chemical recycling of PSW (namely polyolefins) still lacks the proper design and kinetic background to target certain desired products and/or chemicals. Energy recovery was found to be an attainable solution to PSW in general and municipal solid waste (MSW) in particular. The amount of energy produced in kilns and reactors applied in this route is sufficiently investigated up to the point of operation, but not in terms of integration with either petrochemical or converting plants. Although primary and secondary recycling schemes are well established and widely applied, it is concluded that many of the PSW tertiary and quaternary treatment schemes appear to be robust and worthy of additional investigation.
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            The Truck Dispatching Problem

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              Product services for a resource-efficient and circular economy – a review

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                Author and article information

                Journal
                03 July 2018
                Article
                1807.01603
                edc602ba-232e-4800-9c40-707e7ecf8bee

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                9 pages, double column, 4 figures, 2 tables
                cs.AI cs.LG stat.ML

                Machine learning,Artificial intelligence
                Machine learning, Artificial intelligence

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