Comprehensive Knowledge-Driven AI System for Air Classification Process
Air classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging,...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/15/1/45 |
| _version_ | 1797498544315170816 |
|---|---|
| author | Henryk Otwinowski Jaroslaw Krzywanski Dariusz Urbaniak Tomasz Wylecial Marcin Sosnowski |
| author_facet | Henryk Otwinowski Jaroslaw Krzywanski Dariusz Urbaniak Tomasz Wylecial Marcin Sosnowski |
| author_sort | Henryk Otwinowski |
| collection | DOAJ |
| description | Air classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging, the development of an efficient modeling system is of great practical significance. The paper introduces a novel, knowledge-based classification (FLClass) system of bulk materials. A wide range of operating parameters are considered in the study: the mean mass and the Sauter mean diameter of the fed material, classifier rotor speed, working air pressure, and test conducting time. The output variables are the Sauter mean diameter and the cut size of the classification product, as well as the performance of the process. The model was successfully validated against experimental data. The maximum relative error between the measured and predicted data is lower than 9%. The presented fuzzy-logic-based approach allows an optimization study of the process to be conducted. For the considered range of input parameters, the highest performance of the classification process is equal to almost 362 g/min. To the best of our knowledge, this paper is the first one available in open literature dealing with the fuzzy logic approach in modeling the air classification process of bulk materials. |
| first_indexed | 2024-03-10T03:34:50Z |
| format | Article |
| id | doaj.art-834aae75697446a0847ed928f032fff2 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T03:34:50Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-834aae75697446a0847ed928f032fff22023-11-23T11:47:12ZengMDPI AGMaterials1996-19442021-12-011514510.3390/ma15010045Comprehensive Knowledge-Driven AI System for Air Classification ProcessHenryk Otwinowski0Jaroslaw Krzywanski1Dariusz Urbaniak2Tomasz Wylecial3Marcin Sosnowski4Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Armii Krajowej 21, 42-201 Czestochowa, PolandFaculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, PolandFaculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Armii Krajowej 21, 42-201 Czestochowa, PolandFaculty of Production Engineering and Materials Technology, Czestochowa University of Technology, Armii Krajowej 19, 42-201 Czestochowa, PolandFaculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, PolandAir classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging, the development of an efficient modeling system is of great practical significance. The paper introduces a novel, knowledge-based classification (FLClass) system of bulk materials. A wide range of operating parameters are considered in the study: the mean mass and the Sauter mean diameter of the fed material, classifier rotor speed, working air pressure, and test conducting time. The output variables are the Sauter mean diameter and the cut size of the classification product, as well as the performance of the process. The model was successfully validated against experimental data. The maximum relative error between the measured and predicted data is lower than 9%. The presented fuzzy-logic-based approach allows an optimization study of the process to be conducted. For the considered range of input parameters, the highest performance of the classification process is equal to almost 362 g/min. To the best of our knowledge, this paper is the first one available in open literature dealing with the fuzzy logic approach in modeling the air classification process of bulk materials.https://www.mdpi.com/1996-1944/15/1/45classification modelseparation controlfuzzy logicmachine learningartificial intelligence |
| spellingShingle | Henryk Otwinowski Jaroslaw Krzywanski Dariusz Urbaniak Tomasz Wylecial Marcin Sosnowski Comprehensive Knowledge-Driven AI System for Air Classification Process Materials classification model separation control fuzzy logic machine learning artificial intelligence |
| title | Comprehensive Knowledge-Driven AI System for Air Classification Process |
| title_full | Comprehensive Knowledge-Driven AI System for Air Classification Process |
| title_fullStr | Comprehensive Knowledge-Driven AI System for Air Classification Process |
| title_full_unstemmed | Comprehensive Knowledge-Driven AI System for Air Classification Process |
| title_short | Comprehensive Knowledge-Driven AI System for Air Classification Process |
| title_sort | comprehensive knowledge driven ai system for air classification process |
| topic | classification model separation control fuzzy logic machine learning artificial intelligence |
| url | https://www.mdpi.com/1996-1944/15/1/45 |
| work_keys_str_mv | AT henrykotwinowski comprehensiveknowledgedrivenaisystemforairclassificationprocess AT jaroslawkrzywanski comprehensiveknowledgedrivenaisystemforairclassificationprocess AT dariuszurbaniak comprehensiveknowledgedrivenaisystemforairclassificationprocess AT tomaszwylecial comprehensiveknowledgedrivenaisystemforairclassificationprocess AT marcinsosnowski comprehensiveknowledgedrivenaisystemforairclassificationprocess |