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.
The practical implementation of the concept of electronic government is one of the priorities of Russian state policy. The organization of effective interaction between authorities and citizens is an important element of this concept. In addition to providing public services, it should include the processing of electronic appeals (applications, complaints, suggestions, etc.). Research has shown that the speed and efficiency of appeal processing largely depend on the quality of determining the thematic rubric, i.e. solving the rubrication task. The analysis of citizens' appeals received by the e-mail and official websites of public authorities has revealed several specific features (small size, errors in the text, free presentation style, description of several problems) that do not allow the successful application of traditional approaches to their rubrication. To solve this problem, it has been proposed to use various methods of intellectual analysis of unstructured text data (in particular, fuzzy logical algorithms, fuzzy decision trees, fuzzy pyramidal networks, neuro-fuzzy classifi convolutional and recurrent neural networks). The article describes the conditions of the applicability of six intellectual classifiers proposed for rubricating the electronic citizens’ appeals. They are based on such factors as the size of the document, the degree of intersection of thematic rubrics, the dynamics of their thesauruses, and the amount of accumulated statistical information. For a situation where a specific model cannot make an unambiguous choice of a thematic rubric, it is proposed to use the classifier voting method, which can significantly reduce the probability of rubrication errors based on the weighted aggregation of solutions obtained by several models selected using fuzzy inference.
The book gives current scientific positions of the system analysis methodology on the basis of various models and scales, both in deterministic conditions and under conditions of uncertainty and risks. Models of complex systems are considered. Classification of types of systems modeling is presented. The principles of constructing scales are considered: nominal type, order, intervals models of complex systems are formulated. Stages of construction of mathematical model of system are made. The main types of measurement, ratios, differences and absolute scale. Methods for processing characteristics measured in different scales. Life cycles and empirical laws of evolution, engineering and reengineering of developing management information systems, patterns of evolution of such systems, bifurcation phenomena and the importance of attractor development constraints are analyzed. The problems of management of innovative projects using fuzzy logic methods, fuzzy algorithms (Mamdani, Larsen, Sugeno and Tsukamoto), fuzzy growing pyramidal networks and elements of artificial intelligence are considered. Some methods for analyzing and assessing the sustainability of managing risky investment projects on the basis of the theory of the function of a complex variable, operational calculus, actor-network theory, imitation modeling, and the construction of a stability curve are presented. Empirical laws of evolutionary dynamics of information-control systems are considered. Some methods for modeling defect management processes in a developing information management system are presented, including mathematical modeling using the Kendall method. The stochastic network model of the evolution of the information-control system is analyzed. Interpretation and evaluation of the penetration of defects into the system is given. Management decisions and budgeting of the information management system are analyzed.
Today the knowledge–intensive industry development is carried out by the programs that combine a set of innovation and investment projects aimed at achieving a single goal and implemented in general constraints. The presence of a larger number of project characteristics (in particular, terms, resources, performers, etc.), which must be taken into account when forming the composition of the program, leads to the formulation of the problem of multicriteria optimization. As its solution, it is proposed to use an algorithm of bacterial optimization, supplemented by a procedure for forming initial positions using fuzzy logic methods.
In modern conditions of constant growth in prices for fuel and energy resources, the problem of increasing the energy and resource efficiency of technological processes of industrial enterprises has acquired particular relevance. It is especially acute for energy-intensive industries, which include high-temperature processing of mining and chemical raw materials. To reduce the energy intensity of complex chemical-technological processes, it is proposed to use the possibilities of computer simulation, for example, to optimize the operating regimes of existing equipment. The article has considered the scientific and practical problem of optimizing the charge heating regimes in various zones of the roasting conveyor machine used to produce phosphorite pellets from apatite-nepheline ore waste stored in dumps of mining and processing plants. The specifics of the optimization task (nonlinearity of the objective function, large dimension of the search space, high computational complexity) are significant limitations for the use of traditional deterministic search methods. It led to the choice of population algorithms, which are based on modeling the collective behavior and are distinguished by the possibility of simultaneous processing of several options. The cuckoo search algorithm, which is distinguished by a small number of “free” parameters that affect the convergence, was used to solve the stated optimization task. To select the optimal values of these parameters, it was proposed to use the idea of coevolution, which consists in the parallel launch of several versions of the selected algorithm with different “settings” for each subpopulation. The management of the chemical-technological system for the processing of apatite-nepheline ore waste, taking into account the basis of the results obtained, will minimize the amount of return and ensure an energy-saving operating regime of the roasting conveyor machine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.