MATHEMATICAL MODELING OF THE EVALUATION SYSTEM FOR TECHNOLOGICAL SUITABILITY OF BAST RAW MATERIALS FOR MECHANICAL PROCESSING
Abstract
The article presents the scientific substantiation and practical implementation of mathematical modeling of the evaluation system for technological suitability of bast raw materials for mechanical processing.The necessity of creating an objective system for assessing raw material quality to optimize processing modes and predict final product quality is demonstrated. A mathematical model based on fuzzy set theory and multi-criteria optimization methods is proposed, taking into account both quantitative and qualitative indicators of raw materials.The purpose of the work is to develop a mathematical apparatus for comprehensive assessment of the technological suitability of bast raw materials for mechanical processing, which allows objectively determining the possibilityof its industrial use and optimizing technological modes.The object of research is the process of assessing the technological suitability of bast raw materials (flax straw) and optimization of technological modes of its mechanical processing.The subject of research is mathematical models and methods for comprehensive assessment of the technological suitability of bast raw materials, as well as patterns of relationship between raw material characteristicsand processing efficiency.A comprehensive suitability indicator has been developed that takes into account normalized values of quality indicators with corresponding weight coefficients and a function of technological limitations. Weight coefficients were determined by the hierarchy analysis method based on expert assessments, the consistency of which is confirmed by a high value of Kendall’s concordance coefficient (W = 0.82). A technological suitability scale with four levels was created: high suitability (P > 0.8), medium suitability (0.6 < P ≤ 0.8), low suitability (0.4 < P ≤ 0.6), and unsuitability for processing (P ≤ 0.4). Experimental verification of the model on 150 batches of oil flax of various qualities showed high prediction accuracy (94–95 %). Practical application of the methodology demonstrated an increase in fiber yield by 19.6 %, reduction of contamination by 33.3 %, and improvement of physical and mechanical parameters by 16.8 %.
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