The fourth digital revolution of industry makes substantive changes to the rate and methodology of work performance. Machines and robots do the majority of work in robotized and automated factories, while people only supervise them. After an increase of production efficiency, quality control became a critical point. Therefore, quality control systems of computer visions are increasingly installed. The branch of chemical industry requires measurements of quality at as great a frequency as possible. Consequently, indirect measurements are effectively used at this point. This research presents the method of indirect particle measurement. Particles are measured using digital image processing. The algorithm is used for particle measurement to automatically adjust the measurement results. Numerical intelligence is added to the algorithm to increase the accuracy of correction results. The research deals with the problem of matching the results of indirect measurements and the results of the control equipment. For data analysis, fertilizer diameter, mean diameter, aspect ratio, symmetry, sphericity, convexity and some other parameters are used. The mismatch of the artificial neural network results with the control equipment results is slightly higher than 1%.
Fertilizer manufacturing in the chemical industry is closely related with agricultural production. More than a half of raw materials for food products are grown by fertilizing plants. The demand of fertilizers has been constantly increasing along the growth of human population. Fertilizer manufacturers face millions of losses each year due to poor quality products. One of the most common reasons is wrong decisions in control of manufacturing processes. Operator’s experience has the highest influence on this. This paper analyzes the pellet measurement data, collected at the fertilizer plant by using indirect measurements. The results of these measurements are used to construct the model of equipment status control, based on the fuzzy logic. The proposed solution allows to respond to changes in production parameters in a 7–10 times faster manner. On average, a manufacturer with production volumes of up to 80 tonnes/hour could have lost about 8400 tonnes/year of high-quality production. The publication seeks symmetry between human and system decision making.
Growing population and decreasing amount of cultivated land conditions the increase of fertilizer demand. With the advancements of computerized equipment, more complex methods can be used for solving complex mathematical problems. In the fertilizer industry, the granulometric composition of products matters as much as the quality of production of chemical composition products. The shape and size of pellets determines their distribution over cultivated land areas. The effective distance of field spreading is directly related to the size and shape parameters of a pellet. Therefore, the monitoring of production in production lines is essential. The standard direct methods of the monitoring and control of granulometric composition requires too much time and human resources. These factors can be eliminated by using imaging measuring methods that have a variety of benefits, but require additional research in order to assure and determine the compliance of real-time results with results of the control equipment. One of the fastest, most flexible and largest amount of data providing methods is the processing and analysis of digital images. However, then we face the issue of the suitability of 2D images to be used for the evaluation of granulometric compositions, where processing of digital images provides only two dimensions of a pellet: length and width. This study proposes a method of evaluating an irregular pellet. After experimental research we determined < 2% of discrepancy when compared to the real volume of a pellet.
The article concerns the computer-assisted vision system created for the detection of the disposable anesthetic respiratory masks. This article provides the classification of defects which may be common to both rubber and plastic parts of the masks. The defects were divided into groups and the nature of them was investigated. The algorithms and methods for the detection of defective products were based on the segmentation of image and the detection of uneven contours. The experiment results are presented in this work. With reference to the results, the most effective masks' filters were identified. The achieved specificity of the algorithm is 100 % and the sensitivity is 100 %.
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