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Multilevel algorithms for evaluating and making decisions on the optimal control of an integrated system for processing fine ore raw materials

The results of studies aimed at developing multi-level decision-making algorithms for management of energy and resource efficiency, technogenic and environmental safety of a complex multi-stage system for processing fine ore raw materials are presented (MSPFORM). A distinctive feature of such a system is its multidimensionality and multiscale, which manifests itself in the presence of two options for implementing technological processes for processing finely dispersed ore raw materials, the need to take into account the interaction of the aggregates included in the system, as well as the hierarchy of describing the processes occurring in them - mechanical, thermophysical, hydrodynamic, physical and chemical. Such a variety of processes characterizes the interdisciplinarity of research and the complexity of obtaining analytical, interconnected mathematical models. This situation inspired the analyze use of artificial intelligence methods, such as deep machine learning and fuzzy logic, to describe and analyze processes. The scientific component of the research results consists in the developed generalized structure of the MSPFORM, the conceptual basis of multilevel algorithms for evaluating and making decisions on the optimal control of this system, the proposed composition of the parameters and the form of the optimization criterion. The task of the study was to analyze possible options for the processing of ore raw materials, to develop a concept for the construction of the MSPFORM allowing the possibility of optimizing its functioning according to the criterion of energy and resource efficiency while meeting the requirements of environmental safety. The application of evolutionary algorithms for solving the problem of optimizing the MSPFORM according to the criterion of minimum energy consumption is announced and its stages are specified. The structure of the block of neuro-fuzzy analysis of information about the parameters of processes in MSPFORM is presented, which is based on the use of deep recurrent and convolutional neural networks, as well as a fuzzy inference system. The results of a simulation experiment on approbation of the software implementation of this block in the MatLab environment are presented. Read more...

Neuroregulator of the complex technological system for processing ore waste

The study is devoted to improving the management system of a complex technological system for processing ore waste. Such waste accumulates in large volumes in the territories adjacent to the mining and processing plants, posing a great environmental threat to both the population and the environment due to dust formation and the penetration of harmful compounds into the soil and groundwater. Therefore, the task of improving the management systems for the processing of ore waste, as one of the priorities, is on the current agenda of the management of mining and processing plants. The complexity of the technological system is manifested in the presence of two processing lines that differ in the set of units, and the choice of line depends on the granulometric composition of ore waste. The scientific novelty of the research results is the proposed structure of the neural network controller based on the reference model for the technological system, which is used as deep recurrent neural networks. The general structure of the neuroregulator includes several local neurocontrollers for each of the units of the technological system. Recurrent neural networks make it possible to create high-precision digital copies of individual units of two processing lines and use them to simulate the response of control objects when setting up controllers. Approbation of the proposed structure of the neuroregulator was carried out in the MatLab-Simulik environment, neural networks were designed using the Deep Network Designer tool. The results of testing showed that the speed of the control system is increased compared to other architectures of neuroregulators available in the Simulik environment, which can positively affect the operation of the entire technological system in transient conditions, in particular, reduce technological losses. Read more...