Multicomponent Fuzzy Model for Evaluating the Energy Efficiency of Chemical and Power Engineering Processes of Drying of the Multilayer Mass of Phosphorite Pellets
Abstract A multicomponent fuzzy model was proposed for evaluating the energy efficiency of the chemical and power engineering processes of the drying of a dynamic multilayer mass of phosphorite pellets in a complex multistage chemical and power engineering system (roasting conveyor machine). The developed model includes a set of fuzzy component models for analyzing the chemical and power engineering processes of pellet drying corresponding to the results of the decomposition of these processes, a set of neuro-fuzzy production models for evaluating the energy efficiency of the individual stages of the chemical and power engineering processes of pellet drying, and a neuro-fuzzy production model of generalized evaluation of the energy efficiency of the chemical and power engineering process of pellet drying. The use of the proposed model makes it possible to evaluate the energy efficiency of both the individual stages and, in general, the chemical and power engineering process of phosphorite pellet drying under conditions of uncertainty of their thermophysical characteristics and the processes themselves; to perform online structural adjustment and parametric adaptation of the model when the mode and chemical and power engineering process of pellet drying are changed; to perform online evaluation of the energy efficiency of the chemical and power engineering process of pellet drying; and to provide quality improvement and speed of decision making on optimization of the chemical and power engineering process of pellet drying to increase the energy efficiency of these processes..
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2018 |
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Erschienen: |
2018 |
Enthalten in: |
Zur Gesamtaufnahme - volume:52 |
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Enthalten in: |
Theoretical foundations of chemical engineering - 52(2018), 5 vom: Sept., Seite 786-799 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Bobkov, V. I. [VerfasserIn] |
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Links: |
Volltext [lizenzpflichtig] |
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BKL: | |
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Themen: |
Chemical and energy engineering process |
doi: |
10.1134/S0040579518050317 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
SPR017985935 |
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520 | |a Abstract A multicomponent fuzzy model was proposed for evaluating the energy efficiency of the chemical and power engineering processes of the drying of a dynamic multilayer mass of phosphorite pellets in a complex multistage chemical and power engineering system (roasting conveyor machine). The developed model includes a set of fuzzy component models for analyzing the chemical and power engineering processes of pellet drying corresponding to the results of the decomposition of these processes, a set of neuro-fuzzy production models for evaluating the energy efficiency of the individual stages of the chemical and power engineering processes of pellet drying, and a neuro-fuzzy production model of generalized evaluation of the energy efficiency of the chemical and power engineering process of pellet drying. The use of the proposed model makes it possible to evaluate the energy efficiency of both the individual stages and, in general, the chemical and power engineering process of phosphorite pellet drying under conditions of uncertainty of their thermophysical characteristics and the processes themselves; to perform online structural adjustment and parametric adaptation of the model when the mode and chemical and power engineering process of pellet drying are changed; to perform online evaluation of the energy efficiency of the chemical and power engineering process of pellet drying; and to provide quality improvement and speed of decision making on optimization of the chemical and power engineering process of pellet drying to increase the energy efficiency of these processes. | ||
650 | 4 | |a chemical and energy engineering process |7 (dpeaa)DE-He213 | |
650 | 4 | |a drying of pellets |7 (dpeaa)DE-He213 | |
650 | 4 | |a evaluation of energy efficiency |7 (dpeaa)DE-He213 | |
650 | 4 | |a multicomponent fuzzy model |7 (dpeaa)DE-He213 | |
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650 | 4 | |a neuro-fuzzy production model |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Meshalkin, V. P. |e verfasserin |4 aut | |
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