Authors

Spatial data processing and visualization using hybrid computing cluster

Using population algorithms to optimize the temperature regime of roasting phosphorite pellets

The problem of rational energy resource use is especially acute for energy- intensive industries, which include high-temperature processing of mining chemical raw materials (for example, the production of phosphorite pellets from apatite-nepheline ore waste by drying and roasting). In this regard, the temperature modes of roasting conveyor machine should ensure not only the completion of the ongoing chemical-technological processes and the required product quality, but also energy and resource saving. Thus, there is an urgent scientific and practical task of optimizing charge heating modes based on the results of modeling heat and mass transfer processes occurring in various zones of the roasting conveyor machine. The impossibility of carrying out expensive full-scale experiments leads to the need to use computer simulation methods. Nonlinearity, large dimension of the search space, high computational complexity make it difficult to use traditional deterministic search methods. Under these conditions, the stochastic methods that deliberately introduce an element of randomness into the search algorithm show good results. Today, population algorithms based on modeling the collective behavior of living organisms and characterized by the ability to simultaneously process several options have become widespread. To solve the optimization problem, it is proposed to use a modified Cuckoo search algorithm (by introducing fuzzy elements), which provides a comprehensive account of a huge number of parameters set for each vacuum chamber of the roasting conveyor machine. The control of the chemical-energy-technological system for the processing of apatite-nepheline ores waste, taking into account the obtained data and based on the existing neural network model of the high-temperature process, will make it possible to minimize the amount of return and provide energy-saving conditions for the operation of roasting units. Read more...

Underwater pipeline recognition using vectorized images

Underwater pipelines, being critical infrastructure for the transportation of hydrocarbons and other resources, require regular inspection of their condition, taking into account the economic and environmental nature of the consequences of possible accidents. Therefore, one of the key technological challenges today is the development of reliable methods for recognizing underwater pipelines for the purpose of their inspection using video information received by an autonomous unmanned underwater vehicle. A method is proposed for recognizing and tracking an underwater pipeline using optical images using an autonomous underwater vehicle, based on a multi-stage computational data processing scheme, including: vectorization of initial images on a contour basis, selection of visible boundaries of the pipeline in images and calculation of its spatial centerline. The method is based on the use of the author's modification of the Hough Transform algorithm with adaptive limitation of the analysis area and a new version of the author's method for constructing contours using the Otsu's method. The contours obtained using the method have minimal redundancy and sufficient accuracy to identify visible pipeline boundaries using a modified Hough algorithm. The method is characterized by low computational costs in comparison with analogues. The easy calculation of the centerline is carried out on the basis of the application of the local recognition algorithm previously developed by the authors. Computational experiments were conducted to obtain comparative estimates of reliability and computational performance in relation to the contour algorithms of Canny, K-means, Otsu and the boundary detection method (modification of the Hough method). Including comparison assessments with some analogues. The obtained assessments of the effectiveness of the proposed solutions confirmed their effectiveness. Read more...