Науковий вісник НГУ

Designing the predictive control of a drum dryer using multi-agent technology

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №4 2024

Last Updated on 28 August 2024

Published on 30 November -0001

Hits: 2266

Authors:

I.S.Konokh, orcid.org/0000-0001-5930-1957, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N.M.Istomina*, orcid.org/0000-0002-6811-8115, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.I.Lomonos, orcid.org/0000-0002-5001-1280, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (4): 121 - 127

https://doi.org/10.33271/nvngu/2024-4/121

Abstract:

Purpose. To increase the efficiency of drying lines for bulk products by automating control using intelligent technology to determine the state of the product and predict its initial moisture content by analyzing a series of control signals and messages in the time domain.

Methodology. The author’s model of a drum dryer with axial and furnace burners for drying charge used for the production of iron ore concentrate – pellets – was used. The model was used to generate training and control examples. The performance of the multi-agent technology and the accuracy of predicting the initial moisture content were researched.

Findings. The article analyses the factors that complicate the high-quality automatic control of the process of drying bulk products in drum dryers rotating in a furnace with burners. A model of an intelligent predictor is proposed, which identifies the state of the product and predicts its output moisture content on the basis of available control and feedback signals. The operability of the multi-agent system model and of the calculating algorithms for the predicted moisture value was proved. The possibility of using the technology to ensure automatic control of the technological process and high-quality stabilization of the controlled parameter are demonstrated.

Originality. The predictor is implemented as a peer-to-peer multi-agent system. This multi-agent system stores and works signal vectors with values placed by the time delays between the change in the corresponding signal and the change in product moisture at the dryer outlet. Each agent contains a description of a specific situation in the dynamics. The technology provides for automatic adjustment of the multi-agent system by analyzing arrays of signals over a long time period and generating new agents in cases where a situation is detected which cannot be described by an array of existing agents.

Practical value. The technology provides the initial moisture content calculation by an array of agents and allows the dryer automatic control by levelling the time delay in the feedback channel.

Keywords: bulk products drying, automated control, model experiments

References.

1. Konokh, I., Oksanych, I., & Istomina, N. (2020). Automatic Search Method of Efficiency Extremum for a Multi-stage Processing of Raw Materials. Advances in Intelligent Systems and Computing, 1020, 225-241. https://doi.org/10.1007/978-3-030-26474-1_17.

2. Yusupbekov, A. N., Sevinov, J. U., & Botirov, T. V. (2021). Synthesis Algorithms for Neural Network Regulator of Dynamic System Control. 14^th International Conference on Theory and Application of Fuzzy Systems and Soft Computing, 723-730. https://doi.org/10.1007/978-3-030-64058-3_90.

3. Endo, A., Cartagena, O., Ocaranza, J., Sáez, D., & Muñoz, C. (2022). Fuzzy and Neural Prediction Intervals for Robust Control of a Greenhouse. 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8. https://doi.org/10.1109/fuzz-ieee55066.2022.9882701.

4. Areed, F. F. G., El-Kasassy, M. S., & Mahmoud, Kh. A. (2012). Design of Neuro-Fuzzy Controller for a Rotary Dryer. International Journal of Computer Applications, 37(5), 34-41. https://doi.org/10.5120/4606-6584.

5. Khin, A. M., & Thwe, A. M. (2020). Fuzzy Logic Based Dryer Controller. International Journal of Advances in Scientific Research and Engineering, 6(4), 106-112. https://doi.org/10.31695/ijasre.2020.33795.

6. Mamboundou, J., & Langlois, N. (2011). Indirect adaptive model predictive control supervised by fuzzy logic. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2979-2986. https://doi.org/10.1109/fuzzy.2011.6007612.

7. Garcia-Magarino, I. (2011). A set of method fragments for developing multi-agent systems assisted with model transformations. 11 ^th International Conference on Intelligent Systems Design and Applications, 30-35. https://doi.org/10.1109/isda.2011.6121626.

8. Goodrich, M. A., Adams, J. A., & Scheutz, M. (2022). Autonomy Reconsidered: Towards Developing Multi-agent Systems. Lecture Notes in Networks and Systems, 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_38.

9. Zeng, X., & Yu, C. (2014). Multivariable IMC-PID within air-conditioned room temperature and relative humidity control system. Proceedings of the 33 ^rd Chinese Control Conference, 3609-3613. https://doi.org/10.1109/chicc.2014.6895539.

10. Demazeau, Y., Dignum, F., Corchado, J. M., Bajo J., Corchuelo R., Corchado E., Fernández-Riverola F., …, & Campbell, A. (2011). Trends in practical applications of agents and multiagent systems. Berlin: Springer. https://doi.org/10.1007/978-3-642-12433-4.

11. Sharma, D., & Shadabi, F. (2014). Multi-agents based data mining for intelligent decision support systems. 2 ^nd International Conference on Systems and Informatics (ICSAI 2014), Shanghai, China, 241-245. https://doi.org/10.1109/icsai.2014.7009293.

12. Konokh, I. S., Istomina, N. M., & Sribnyi, S. D. (2019). Using multiagent systems for identification and automatic control tasks. Bulletin of the Kherson National Technical University, 3(70), 49-62. https://doi.org/10.35546/kntu2078-4481.2019.3.5.

13. Palau, A. S., Dhada, M. H., Bakliwal, K., & Parlikad, A. K. (2019). An Industrial Multi Agent System for real-time distributed collaborative prognostics. Engineering Applications of Artificial Intelligence, 85, 590-606. https://doi.org/10.1016/j.engappai.2019.07.013.

14. Benedetti, M., Cesarotti, V., Introna, V., & Serranti, J. (2016). Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study. Applied Energy, 165, 60-71. https://doi.org/10.1016/j.apenergy.2015.12.066.

15. Sideratos, G., Ikonomopoulos, A., & Hatziargyriou, N. D. (2020). A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks. Electric Power Systems Research, 178, 106025. https://doi.org/10.1016/j.epsr.2019.106025.

16. Quanmin, Zh., Weicun, Zh., Jianhua, Zh., & Bei, S. (2019). U-neural network-enhanced control of nonlinear dynamic systems. Neurocomputing, 352, 12-21. https://doi.org/10.1016/j.neucom.2019.04.008.

17. Ketata, R., Rezgui, Y., & Derbel, N. (2011). Stability and robustness of fuzzy adaptive control of nonlinear systems. Applied Soft Computing, 11(1), 166-178. https://doi.org/10.1016/j.asoc.2009.11.007.

18. Romagnoli, J. A., & Palazoglu, A. (2016). Process Optimization and Control. In Introduction to Process Control, (2 ^nd ed.). CRC Press. https://doi.org/10.1201/b11674-30.

19. Minh Khue, N. T. (2012). Developing an Intelligent Multi-Agent System based on JADE to solve problems automatically. 2012 International Conference on Systems and Informatics (ICSAI2012), 684-690. https://doi.org/10.1109/icsai.2012.6223087.

20. Chen, Y., Lu, Yu, J. X., & Hill, D. J. (2013). Multi-Agent Systems with Dynamical Topologies: Consensus and Applications. IEEE Circuits and Systems Magazine, 13(3), 21-34. https://doi.org/10.1109/mcas.2013.2271443.

• < Prev
• Next >