Fuzzy model of a multi-stage chemical-energy-technological processing system fine ore raw materials

IF 0.4 Q4 MATHEMATICS, APPLIED
M. Dli, A. Puchkov, Nikolay N. Prokimnov, Boris V. Okunev
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Abstract

The results of the study, the purpose of which was to build a software model of a multi-stage integrated system for processing finely dispersed ore raw materials, are presented. The role of such raw materials can be processed waste at mining and processing plants of apatite-nepheline and other types of ores, which accumulate in large volumes in tailing dumps. They create a significant environmental threat in the territories adjacent to the plants due to weathering, dust formation, penetration into the soil and aquifers of chemical compounds and substances hazardous to human health. Therefore, the improvement of existing production processes, the development of new technological systems for mining and processing plants, including the application of the principles of the circular economy, waste recycling, justifies the relevance of the chosen research area. The proposed program model is based on the use of trainable trees of systems (blocks) of fuzzy inference of the first and second types. This approach made it possible to avoid unnecessary complication of the bases of fuzzy inference rules when using only one fuzzy block when building a multi-parameter model of the entire multi-stage complex system. The use of several fuzzy inference blocks that describe the behavior of individual units of the system and their configuration in accordance with the physical structure of the system allows the use of relatively simple sets of rules for individual blocks. The joint selection of their parameters when training a tree of fuzzy blocks makes it possible to achieve high accuracy of the solutions obtained. The novelty of the research results is the proposed software fuzzy model of an integrated system for processing finely dispersed ore raw materials. The results of a simulation experiment conducted in the MatLab environment using a synthetic data set generated in Simulink are presented. The results showed that the trained fuzzy model provides good fidelity of the parameters and variables from the test part of the synthetic set.
细矿原料多级化工-能源-工艺处理系统的模糊模型
本文给出了研究结果,其目的是建立一个多阶段集成系统的软件模型,用于处理细粒分散的矿石原料。这些原料的作用可以是在磷灰石-霞石和其他类型的矿石的采矿和加工厂处理的废物,这些废物在尾矿堆中大量堆积。由于风化、粉尘形成、对人类健康有害的化合物和物质渗入土壤和含水层,它们对植物附近的领土造成重大环境威胁。因此,改进现有的生产过程,发展采矿和加工厂的新技术系统,包括应用循环经济、废物回收的原则,证明所选择的研究领域具有相关性。所提出的程序模型是基于使用第一类和第二类模糊推理系统(块)的可训练树。该方法在建立整个多阶段复杂系统的多参数模型时,可以避免只使用一个模糊块时模糊推理规则基的不必要的复杂性。使用几个模糊推理块来描述系统中单个单元的行为及其与系统物理结构相一致的配置,从而允许对单个块使用相对简单的规则集。在训练模糊块树时,对它们的参数进行联合选择,使得得到的解具有较高的精度。研究结果的新颖之处在于提出了细粒分散矿石原料加工集成系统的软件模糊模型。给出了利用Simulink生成的合成数据集在MatLab环境下进行的仿真实验结果。结果表明,训练后的模糊模型对综合集测试部分的参数和变量具有较好的保真度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
0.70
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0.00%
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