Because of the cost and complexity of implementing an optical paper sorting system, the demand for an intelligent system for waste paper sorting has increased. This research focused on the development of a smart intelligent system (SIS) for recyclable waste paper sorting. The basis for selecting the regions of interests (ROIs) is the margin area of a paper object image because almost all printed documents keep the margin area intact. The paper grade is identified using a proximity search. The SIS with the HSI colour space offered maximum success rates of 99 %, 82 % and 89 %, while with the RGB model, the classification success rates were 94 %, 93 % and 98 % for white paper, old newsprint paper and old corrugated cardboard, respectively. The SIS is clearly superior to other prevailing techniques because of the faster decision making and lower cost of implementation.
Sensor-based sorting is a well-established single particle separation technology. It has found wide application as a quality assurance and control approach in food processing, mining, and recycling. In order to assure high sorting quality, a high degree of control of the motion of individual particles contained in the material stream is required. Several system designs, which are tailored to a sorting task at hand, exist. However, the suitability of a design for a sorting task is assessed by empirical observation. The required thorough experimentation is very time consuming and labor intensive. In this paper, we propose an instrumented bulk material particle for the characterization of motion behavior of the material stream in sensor-based sorting systems. We present a hardware setup including a 9-axis absolute orientation sensor that is used for data acquisition on an experimental sorting system. The presented results show that further processing of this data yields meaningful features of the motion behavior. As an example, we acquire and process data from an experimental sorting system consisting of several submodules such as vibrating conveyor channels and a chute. It is shown that the data can be used to train a model which enables predicting the submodule of a sorting system from which an unknown data sample originates. To our best knowledge, this is the first time that this IIoT-based approach has been applied for the characterization of material flow properties in sensor-based sorting.
The technological innovation and the relentless marketing of new electronic products with improved performance generate increasing quantities of Waste from Electrical and Electronic Equipment (WEEE). In this scenario, End-Of-Life (EOL) flat monitors and screens represent a category generating, as a consequence of the rapid change in technology, an important amount of waste. Considering future estimations, the implementation of an adequate recycling infrastructure is necessary. An efficient, reliable and low-cost analytical tool is thus needed to perform detection/control actions in order to assess: i) waste composition and ii) physical-chemical attributes of the resulting materials. The knowledge of these information is a requirement to set-up and to implement correct recycling actions.
In this study, a cascade identification approach, based on Near InfraRed (NIR) – HyperSpectral Imaging (HSI), was carried out. More in detail, a four-steps classification was designed, implemented and set-up in order to recognize different materials occurring in a specific WEEE stream: EOL milled monitors and flat screens. Adopting the proposed approach, different material categories are correctly recognized and classified. Obtained results can be useful not only to set-up a quality control system, but also to improve sorting actions in this specific recycling sector.
Optical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type.
Sensor-based ore sorting is not a new technology. It has been around since more than 70 years, mainly for diamond concentration, where it was applied to eliminate the security risk of diamonds being stolen from the previously applied grease-tables . Despite a few installations in uranium ore processing, it had no further widespread acceptance in the minerals industry, mainly due to low design capacity. Besides that, sensor-based colour sorters were used in the food industry for small particle sizes (e. g., rice cleaning). It is fact that the first machine designs appropriate for coarse bulk materials were not developed for the minerals industry, but for the upcoming recycling industry for plastics, glass, paper, metals in the late 1980s. In this sector, besides some magnetic separators, all the work was done by manual hand-picking, and it needed automation. After some years of optimization, these machines showed reliable performance under harsh conditions in scrap yards and recycling plants. Then, finally, the minerals industry, which at first was not convinced that this rather complicated machines were suited to be used with minerals, began with the first applications. These first installations of sensor-based ore sorters around the late 1990, all of them equipped with line-scan optical cameras, were mainly in industrial minerals, such as calcite, magnesite, quartz or rock salt. Since then, the technology has seen an enormous development in terms of available sensors, design capacity and availability, and the number of installations for minerals is growing – steadily but slower than expected, considering the many advantages it brings.
Technical and legal challenges cause the implementation of Autonomous Driving in road traffic to still be a long way off. However, the introduction of driver assistance functions enables cars’ automation for low speeds already nowadays. The concept of Autonomous Transport (AT) combines automated driving with Automated Guided Vehicle’s technology. In this paper, we assess risks that emanate from AT and show fields of action for its implementation with respect to the standards for functional safety. We set up requirements for the reliability of cars’ electric power supply, actuators and sensors. Concepts for their cost-efficient fulfillment are derived. The realization of collision avoidance and navigation without additional attachments is discussed.