Efficiency Analysis and Optimization of Technological Modes of Drum Drying Stations

Authors

  • I. S. Konokh Kremenchuk Mykhailo Ostrohradskyi National University
  • N. M. Istomina Kremenchuk Mykhailo Ostrohradskyi National University

DOI:

https://doi.org/10.31649/1997-9266-2019-147-6-7-18

Keywords:

optimal control, efficiency, dynamic control

Abstract

Issues of technological systems modes optimization for streaming raw materials processing have a significant impact on the enterprises profitability in the chemical, food and metallurgical industries. At present, the task of moving from the profitability requirements to control actions remains actual. The task requires the development of specialized methods. Such methods should use an efficiency factor with evaluating of a technological operation results by a combination of cost estimates of the output product, costs and time. In the paper it is shown the structure of the channel system for raw materials processing should contain blocks for calculating the consumption costs of raw materials, resources and energy for the processing and transporting parts of the station and for calculating the overall efficiency factor. An example of a production line for drying a granular product in a drum furnace with zone and axial burners using different types of fuel is considered. The mutual influence of the line components on its productivity, quality and costs is taken into account. The non-linear form of the efficiency factors expression required modification for use in the dynamic programming method. For optimal mode searching we chose a dynamic programming method that determines the optimal phase trajectory with maximizes the additive criterion formulated for this method. Cost estimation of the trajectory sections was carried out using a computational model. The model describes the dynamic processes in the station. The search for optimal controls was carried out from the end of a possible trajectory to the beginning in a discretized space of phase coordinates. The phase coordinates are the position of the product portion inside the drum, the moisture content of the product; control variables: drum angular velocity, fuel consumption in each burner. A comparison of the efficiency factor values for the found optimal trajectory and efficiency factor values for trajectories with deviations showed the validity of the chosen approach.

Author Biographies

I. S. Konokh, Kremenchuk Mykhailo Ostrohradskyi National University

Cand. Sc.(Eng.), Associate Professor of the Chair of Information and Control Systems

N. M. Istomina, Kremenchuk Mykhailo Ostrohradskyi National University

Senior Lecturer of the Chair of Information and Control Systems

References

J. Gregory, A. Olivares, “Energy-optimal trajectory planning for the Pendubot and the Acrobot“, Optimal Control Applications and Methods, no. 34(3), pp. 275-295, 2012. https://doi.org/10.1002/oca.2020 .

J. Bing-Feng, B. Xiaolong, J. C. Ju, G. Yaozheng, “Design of Optimal Fast Scanning Trajectory for the Mechanical Scanner of Measurement Instruments,” Scanning, no. 36 (2), pp. 185-193, 2013. doi: 10.1002/sca.21084 .

A. Gasparetto, V. Zanotto, “Optimal trajectory planning for industrial robots,” Advances in Engineering Software, no. 41 (4), pp. 548-556, 2010. https://doi.org/10.1016/j.advengsoft.2009.11.001 .

H. Wang, Y. Tian, C. Vasseur, “Non-Affine Nonlinear Systems Adaptive Optimal Trajectory Tracking Controller Design and Application,” Studies in Informatics and Control, no. 24 (1), pp. 05-12. 2015. https://doi.org/10.24846/v24i1y201501 .

О. Н. Бурмистрова, С. А. Король, «Определение оптимальных скоростей движения лесовозных автопоездов из условия минимизации расхода топлива,» Лесной вестник, № 1, с. 25-28, 2013.

I. Lutsenko, “Identification of target system operations. Development of global efficiency criterion of target operations,” Eastern-European Journal of Enterprise Technologies, vol. 2, iss. 2 (74), pp. 35-40, 2015. https://doi.org/10.15587/1729-4061.2015.38963 .

I. Lutsenko, “Definition of efficiency indicator and study of its main function as an optimization criterion,” Eastern-European Journal of Enterprise Technologies, vol. 6, issue 2 (84). pp. 24-32, 2016. https://doi.org/10.15587/1729-4061.2016.85453 .

Л. Г. Елкина, М. Е. Федотова, «Применение функционально-стоимостного анализа к ресурсосбережению,» Вестник Уфимского государственного авиационного технического университета, том 8, № 1 (17), с. 115-120, 2006.

М. С. Кувшинов, Н. В. Киреева, «Анализ соответствия методов управления затратами актуальным задачам управления,» Экономический анализ: теория и практика, № 17 (368), с. 37-46, 2014.

I. Konokh, I. Oksanych, N. Istomina, “Automatic Search Method of Efficiency Extremum for a Multi-stage Processing of Raw Materials,” Lecture Notes in Computational Intelligence and Decision Making, Springer, pp. 225-241, 2019.

В. С. Яременко, «Огляд наявних мультиагентних систем для задач інтелектуального аналізу даних,» Вчені записки ТНУ імені В. І. Вернадського. Серія: технічні науки, т. 29 (68), ч. 2, №. 3, с. 47-55, 2018.

A. Fariz, J. Abouchabaka, N. Rafalia, “Using multi-agents systems in distributed data mining: a survey,” Journal of Theoretical & Applied Information Technology, no. 73(3), pp. 427-440, 2015.

P. Pawlewski, at al., Trends in practical applications of agents and multiagent systems, Berlin: Springer, 2011. 729 р. https://doi.org/10.1007/978-3-642-12433-4 .

E. Serrano, M. Rovatsos, J.A. Botia, “Data mining agent conversations: a qualitative approach to multiagent systems analysis,” Information Sciences, no. 230, pp. 132-146, 2013.

O. Kazik, K. Peskova, M. Pilat, R. Neruda, “Meta learning in multi-agent systems for data mining,” Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 02. pp. 433-434, 2011.

D. Sharma, F. Shadabi, “Multi-agents based data mining for intelligent decision support systems,” Systems and Informatics (ICSAI), 2nd International Conference on IEEE, pp. 241-245, November, 2014.

И. С. Конох, «Представление образов динамических процессов в системах автоматического управления с помощью самонастраивающихся агентов,» Автоматизированные системы управления приборы автоматики, вып. 167, с. 29-38, 2014.

A. Salvador Palau, M. Dhada, K. Bakliwal, A. Parlikad, “An Industrial Multi Agent System for real-time distributed collaborative prognostics,” Engineering Applications of Artificial Intelligence, vol. 85. pp. 590-606, 2019. https://doi.org/10.1016/j.engappai.2019.07.013 .

F. L. Bellifemine, G. Caire, D. Greenwood, Developing multi-agent systems with JADE. John Wiley & Sons, 2007. Vol. 7.

Y. Chen, J. Lu, X. Yu, D. J. Hill, “Multi-agent systems with dynamical topologies: Consensus and applications,” IEEE circuits and systems magazine, vol. 13, no. 3, pp. 21-34, 2013.

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

G. Sideratos, A. Ikonomopoulos, N. D. Hatziargyriou, “A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks“, Electric Power Systems Research, vol. 178, 106025, 9 p., 2020. https://doi.org/10.1016/j.epsr.2019.106025 .

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

И. С. Конох, Н. Н. Истомина, А. П. Оксанич, «Поиск оптимального закона управления процессами обработки сырья по критерию максимальной эффективности,» Radioelectronics & Informatics, № 1 (84), с. 10-19, 2019.

R. E. Bellman, Dynamic Programming. Princeton University Press, 2003, 401 p.

И. С. Конох, М. В. Самчишин, А. В. Копаевич, «Идентификация влажности гранулированного технического углерода в сушильном барабане для оптимизации управления по критерию максимальной эффективности, » Вісник Кременчуцького національного університету імені Михайла Остроградського, вип. 5 (100), ч. 2, с. 25-34, 2016.

Downloads

Abstract views: 206

Published

2019-12-23

How to Cite

[1]
I. S. Konokh and N. M. Istomina, “Efficiency Analysis and Optimization of Technological Modes of Drum Drying Stations”, Вісник ВПІ, no. 6, pp. 7–18, Dec. 2019.

Issue

Section

Automation and information-measuring equipment

Metrics

Downloads

Download data is not yet available.