REVIEW PAPER
Feedback system for reactor process analysis
 
More details
Hide details
1
WSEI University
 
2
Wyższa Szkoła Biznesu - National Louis University
 
 
Submission date: 2024-06-03
 
 
Acceptance date: 2024-07-24
 
 
Publication date: 2024-08-20
 
 
Corresponding author
Krzysztof Król   

WSEI University
 
 
JoMS 2024;57(Numer specjalny 3):450-466
 
KEYWORDS
TOPICS
ABSTRACT
The article discusses an approach to analyzing processes in industrial reactors using advanced systems with feedback. The authors describe the system preparation and image reconstruction algorithms of ultrasonic tomographs (USTs) that have applications in industry. The research presented in this paper focuses on developing effective strategies for controlling and monitoring chemical reaction processes in reactors. Using advanced data processing techniques, the authors propose systems with feedback that enable accurate analysis and optimization of reactor operating conditions. A key aspect is ultrasonic tomographic imaging, which provides precise data on the state of the process in the reactor. In summary, the paper presents a state-of-the-art approach to analyzing chemical reaction processes in industrial reactors using advanced feedback systems and ultrasonic imaging technologies. The proposed solutions are essential for improving efficiency and process control in the chemical industry.
 
REFERENCES (7)
1.
Hastie, T., Tibshirani, R., Friedman, J. (2009). The elements of statistical learning, Springer-Verlag New York Inc.
 
2.
Kłosowski, G., Rymarczyk, T., Cieplak, T., Niderla, K., Skowron, Ł. (2020). Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography, Sensors, 20, No. 11 3324.
 
3.
Kłosowski, G., Rymarczyk, T., Niderla, K., Kulisz, M., Skowron, Ł., Soleimani, M. (2023). Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography – a hybrid approach. Eksploatacja i Niezawodność – Maintenance and Reliability.25(1):11.doi:10.17531/ein.2023.1.11.
 
4.
Koulountzios, P., Rymarczyk, T., Soleimani, M. (2022). A 4-D Ultrasound Tomography for Industrial Process Reactors Investigation, in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-14, Art no. 4502714, doi 10.1109/TIM.2022.3164166.
 
5.
Kozłowski, E., Borucka, A., Świderski, A. (2020). Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploatacja i Niezawodność – Maintenance and Reliability. 2020;22(2):192-200. doi:10.17531/ein.2020.2.2.
 
6.
Majerek, D., Rymarczyk, T., Wójcik, D., Kozłowski, E., Rzemieniak, M., Gudowski, J., Gauda, K. (2021). Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography. Energies.; 14(22):7549.https://doi.org/10.3390/en1422....
 
7.
Rymarczyk, T., Kłosowski, G., Kania, K. Rymarczyk, P., Mazurek, M. (2020).Tomographic ultrasonic sensors in industrial applications, Przeglad Elektrotechniczny, vol. 97, no. 1, pp. 166–169.
 
eISSN:2391-789X
ISSN:1734-2031
Journals System - logo
Scroll to top