Implementation of Automated Lines for Sorting and Recycling Household Waste as an Important Goal of Environmental Protection

Abstract

The principles of selection and creation of rational methods of disposal and recycling of wastes are based on the fact that the problem of waste is an interconnected environmental-economic and technological problem, and the waste itself should be considered as technogenic raw materials of complex organo-mineral composition. Therefore, the main purpose of the work is to define methods for recycling waste and their environmental aspects. To achieve this goal, the methods of analysis, spectroscopy were used. It is determined that the final treatment of waste, to date, means either their disposal in a landfill or incineration, which adversely affects the environment. Methods for identifying the material of the object, as well as algorithms for the selection and recognition of objects by processing data from the computer vision system are considered. High accuracy is shown in 94.12% identification of plastic polymers.

MRS-YOLO: A High-Precision Model for Efficient Waste Detection and Classification

“…In Equation (7), ω represents the weights associated with different pooling operations. These weights play a crucial role in significantly reducing the computational cost of subsequent operations.…”

“…Manual sorting[4], still prevalent today, is hindered by inefficiency and high costs, posing obstacles to recycling efforts [5]. Therefore, there is an urgent need for intelligent, automated technologies to enhance sorting speed while maintaining precision [6], critical for optimizing recycling and environmental protection [7]. Existing waste detection [8] and recycling systems primarily rely on target detection algorithms [9] to identify garbage.…”

Simulation of operation of neural network with purpose of utilisation of hydraulic actuators in complicated technical conditions

“…The first restriction means that there exists the following requirement: risk of the inefficient utilisation must not exceed a certain level. If values have membership functions of the triangular form, then it is possible to write down the risk function in the following manner (24): (24) The designations (r mini , r i , r maxi ) in the formula (24) as the fuzzy (triangular) number require special explanations. These designations are minimal value, the most expected value, and maximum value, respectively.…”

“…Artificial neural networks are irreplaceable for the high-quality processing of colossal data streams, without which it is very difficult, and sometimes even impossible to adequately evaluate the market situation and make the right decision. All this necessitates further study, development and implementation of the apparatus of artificial neural networks in practice [23,24] .…”