A. Safonyk, O. Rogov, M. Trokhymchuc

Èlektron. model. 2023, 45(2):03-15

https://doi.org/10.15407/emodel.45.02.003

ABSTRACT

The main goal of this article is to design a multifactorial model for rapid evaluation of the effective operation of reactors for biological wastewater treatment, which is based on: changes in the concentration of organic pollutants in the bioreactor over time; changes in the concentration of activated sludge in the bioreactor over time; changes in the concentration of activated sludge in the reactor over time, taking into account the unevenness of the flow of wastewater to treatment facilities; the process of transporting the substrate to the bioreactor (it is possible to obtain different amounts at different times). The software implementation of the proposed algorithm for finding the appropriate model problem in the Python environment has been developed. The results of computer experiments on the study of the effectiveness of wastewater treatment in biological treatment reactors for different operating modes of the installations are given. The obtained results will be useful during calculations in the case of designing biological treatment facilities or during the reconstruction of existing bioreactors for their promising operation in new operating conditions.

KEYWORDS

mathematical model; biological wastewater treatment; non-uniformity conditions.

REFERENCES

1. Yun Y., Lee E., Kim K., Han J. (2019), Sulfate reducing bacteria-based wastewater treatment system integrated with sulfi de fuel cell for simultaneous wastewater treatment and electricity generation, Chemosphere, 233, pp. 570–578.
   https://doi.org/10.1016/j.chemosphere.2019.05.206
2. Ghangrekar M.M., Shinde V.B. (2007), Performance of membrane-less microbial fuel cell treating wastewater and effect of electrode distance and area on electricity production, Bioresource Technology, 97, 2879–2885.
   https://doi.org/10.1016/j.biortech.2006.09.050
3. Seung Hyuk Baek, Seok Ku Jeon, Krishna Pagilla (2009). Mathematical modeling of aerobic membrane bioreactor (MBR) using activated sludge model no. 1 (ASM1). Journal of Industrial and Engineering Chemistry, 15(6), pp. 835-840.
   https://doi.org/10.1016/j.jiec.2009.09.009
4. Gladys Jiménez-García, Rafael Maya-Yescas (2019). Chapter Two - Mathematical mode­ling of mass transport in partitioning bioreactors. Advances in Chemical Engineering, 54, pp. 53-74.
   https://doi.org/10.1016/bs.ache.2019.01.001
5. Hong-Gui Han, Chen-Xuan Sun, Xiao-Long Wu, Hong-Yan Yang, Nan Zhao, Jie Li, Jun-Fei Qiao (2023). Dynamic–static model for monitoring wastewater treatment processes, Control Engineering Practice, 132, 105424. 
   https://doi.org/10.1016/j.conengprac.2022.105424
6. Peng Chang, Xun Bao, FanChao Meng, RuiWei Lu (2023). Multi-objective Pigeon-inspired Optimized feature enhancement soft-sensing model of Wastewater Treatment Process, Expert Systems with Applications, 215, 119193.
   https://doi.org/10.1016/j.eswa.2022.119193
7. Pezhman Kazemi, Christophe Bengoa, Jean-Philippe Steyer, Jaume Giralt (2021). Data-driven techniques for fault detection in anaerobic digestion process, Process Safety and Environmental Protection, 146, pp. 905-915.
   https://doi.org/10.1016/j.psep.2020.12.016
8. Hongjun Xiao, Daoping Huang, Yongping Pan, Yiqi Liu, Kang Song (2017). Fault diagnosis and prognosis of wastewater processes with incomplete data by the auto-associative neural networks and ARMA model, Chemometrics and Intelligent Laboratory Systems, 161, 2017, pp. 96-107.
   https://doi.org/10.1016/j.chemolab.2016.12.009
9. Laurent Lardon, Ana Punal, Jean-Philippe Steyer (2004). On-line diagnosis and uncertainty management using evidence theory––experimental illustration to anaerobic digestion processes, Journal of Process Control, 14(7), pp. 747-763.
   https://doi.org/10.1016/j.jprocont.2003.12.007
10. Sánchez-Fernández, F.J. Baldán, G.I. Sainz-Palmero, J.M. Benítez, M.J. Fuente (2018). Fault detection based on time series modeling and multivariate statistical process control, Chemometrics and Intelligent Laboratory Systems, 182, pp. 57-69. 
    https://doi.org/10.1016/j.chemolab.2018.08.003
11. Doris Brockmann, Yves Gérand, Chul Park, Kim Milferstedt, Arnaud Hélias, Jérôme Hamelin (2021), Wastewater treatment using oxygenic photogranule-based process has lower environmental impact than conventional activated sludge process, Bioresource Technology, 319, pp. 124-204.
    https://doi.org/10.1016/j.biortech.2020.124204
12. Andrii Safonyk, Viktor Zhukovskyy, Anna Burduk (2020). Modeling of biological waste­water treatment process taking into account reverse effect of concentration on diffusion coefficient. Conference Paper 10th International Conference on Advanced Computer Information Technologies (ACIT2020), pp. 29-35. 
    https://doi.org/10.1109/ACIT49673.2020.9208814
13. Safonyk A., Bomba A., Tarhonii I. (2019) Modeling and automation of the electrocoagulation process in water treatment, Advances in Intelligent Systems and Computing, 871, pp. 451-463. 
    https://doi.org/10.1007/978-3-030-01069-0_32
14. Safonyk A., Martynov S., Kunуtskіy S. (2019) Modeling of the contact removal of iron from groundwater, International Journal of Pure and Applied Mathematics, 32, pp. 71-82.
    https://doi.org/10.12732/ijam.v32i1.7

Full text: PDF

W. Gharibi ¹, PhD, Prof., A. Hahanova ², Cand. T. Sc, Ass. Prof., V. Hahanov ²,
D. Sc., Prof., S. Chumachenko ², D. Sc., Prof., E. Litvinova ², D. Sc., Prof., I. Hahanov ²

¹ The University of Missouri-Kansas City MO 64110 USA,
  This email address is being protected from spambots. You need JavaScript enabled to view it.

² Kharkiv National University of Radio Electronics,
  Ukraine, 61166, Kharkiv, Nauka Avenue, 14,
  (057) 7021 326, This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2023, 45(2):16-33

https://doi.org/10.15407/emodel.45.02.016

ABSTRACT

The main idea is to create vector-logic computing that uses only read-write transactions on address memory to process large data. The main task is to implement new simple and reliable models and methods of vector computing based on primitive read-write transactions in the technology of vector flexible interpretive simulation of digital system faults. Vector-logic computing is a computational process based on read-write transactions over bits of a binary vector of functionality, where the input data is the addresses of the bits. A vector method for the synthesis of deductive matrices for transporting input fault lists is proposed, which has a quadratic computational complexity. The method is a development of the deductive vector synthesis algorithm based on the truth table. The deductive matrix is intended for the synthesis and verification of tests using parallel simulation of faults, as addresses, based on a read-write transaction of deductive vector cells in memory.

KEYWORDS

vector computing, vector form of logic, matrix of deductive vectors, vector method for synthesizing a deductive matrix, read-write transaction, vector model of defects, functions and structures, deductive parallel fault simulation.

REFERENCES

1. Gharibi V., Khakhanova A.V., Hahanov V.I., Chumachenko S.V., Litvinova E.I., Hahanov I.V. (2023), Vector-deductive Memory-based Transactions for Fault-as-address Si­mulation. Electronic modeling, no. 1, pp. 12-23.
   https://doi.org/10.15407/emodel.45.01.003
2. Shannon C.E. (1958, 1993), Von Neumann's Contributions to Automata Theory Bulletin American Mathematical  Society,   64, Claude E. Shannon: Collected Papers. IEEE, pp. 831-835.
   https://doi.org/10.1090/S0002-9904-1958-10214-1
3. Davis M. (1989), Emil Post's contributions to computer science. Fourth Annual Symposium on Logic in Computer Science, pp. 134-136.
   https://doi.org/10.1109/LICS.1989.39167
4. What’s New in the 2022 Gartner Hype Cycle for Emerging Technologies, Accessed: https://www.gartner.com/en/articles/what-s-new-in-the-2022-gartner-hype-cycle-for-emerging-technologies, 02/08/2023.
5. Wang P. et al. (2018), RC-NVM: Enabling Symmetric Row and Column Memory Accesses for In-memory Databases. 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA), Vienna, Austria, pp. 518-530.
   https://doi.org/10.1109/HPCA.2018.00051
6. Takahashi N., Ishiura N. and Yajima S. (1994), Fault simulation for multiple faults by Boolean function manipulation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. April, vol. 13, no. 4, pp. 531-535.
   https://doi.org/10.1109/43.275363
7. Srivastava M., Goyal S.K., Saraswat A. and Gangil G. (2020), Simulation Models for Different Power System Faults. 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), pp. 1-6. 
   https://doi.org/10.1109/ICADEE51157.2020.9368915
8. Menon and Chappell. Deductive Fault Simulation with Functional Blocks. IEEE Transactions on Computers. 1978, vol. C-27, no. 8, pp. 689-695. 
   https://doi.org/10.1109/TC.1978.1675175
9. Pomeranz I. and Reddy S.M. (2001), Forward-looking fault simulation for improved static compaction. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Oct. 2001, vol. 20. no. 10, pp. 1262-1265. 
   https://doi.org/10.1109/43.952743
10. Navabi Z. (2011), Digital System Test and Testable Design. Using HDL Models and Architectures. Springer.
    https://doi.org/10.1007/978-1-4419-7548-5
11. Hahanov V., Chumachenko S., Iemelianov I., Hahanov V., Larchenko L. and Daniyil T. (2017), Deductive qubit fault simulation. 2017 14th International Conference: The Expe­rience of Designing  and Application of  CAD Systems in Microelectronics (CADSM), pp. 256- 
    https://doi.org/10.1109/CADSM.2017.7916129
12. Hahanov V., Gharibi W., Litvinova E. and Chumachenko S. (2019), Qubit-driven Fault Simulation. 2019 IEEE Latin American Test Symposium (LATS), pp. 1-7.
    https://doi.org/10.1109/LATW.2019.8704583
13. Gharibi W., Devadze D., Hahanov V., Litvinova E. and Hahanov I. (2019), Qubit Test Synthesis Processor for SoC Logic. 2019 IEEE East-West Design & Test Symposium (EWDTS), Batumi, Georgia, pp. 1-5. 
    https://doi.org/10.1109/EWDTS.2019.8884476
14. Hahanov V. et al. (2021), Vector-Qubit models for SoC Logic-Structure Testing and Fault Simulation. 2021 IEEE 16th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), pp. 24-28. 
    https://doi.org/10.1109/CADSM52681.2021
15. Hahanov V.I., Hyduke S.M., Gharibi W., Litvinova E.I., Chumachenko S.V. and HahanovaV. (2014), Quantum Models and Method for Analysis and Testing Computing Systems. 2014 11th International Conference on Information Technology: New Generations, Las Vegas, NV, pp. 430-434. 
    https://doi.org/10.1109/ITNG.2014.125
16. Karavay M., Hahanov V., Litvinova E., Khakhanova H. and Hahanova I. (2019), Qubit Fault Detection in SoC Logic. 2019 IEEE East-West Design & Test Symposium (EWDTS), Batumi, Georgia, pp. 1-7. 
    https://doi.org/10.1109/EWDTS.2019.8884475
17. Hahanov V., Gharibi W., Litvinova E. and Chumachenko S. (2019), Qubit-driven Fault Simulation. 2019 IEEE Latin American Test Symposium (LATS), Santiago, Chile, pp. 1-7. 
    https://doi.org/10.1109/LATW.2019.8704583
18. Karavay M., Hahanov V., Litvinova E., Khakhanova H. and Hahanova I. (2019), Qubit Fault Detection in SoC Logic. 2019 IEEE East-West Design & Test Symposium (EWDTS), Batumi, Georgia, pp. 1-7. 
    https://doi.org/10.1109/EWDTS.2019.8884475
19. Hahanov V. Cyber Physical Computing for IoT-driven Services. New York: Springer. 2018. 
    https://doi.org/10.1007/978-3-319-54825-8
20. Reinsalu U., Raik J., Ubar R. and Ellervee P. (2011), Fast RTL Fault Simulation Using Decision Diagrams and Bitwise Set Operations. 2011 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, Vancouver, BC, pp. 164-170. 
    https://doi.org/10.1109/DFT.2011.42
21. Pomeranz I. and Reddy S.M. (1998), A synthesis procedure for flexible logic functions. Proceedings Design, Automation and Test in Europe. Pp. 973-974. Doi: 10.1109/DATE. 1998.655995.
22. D.B. (1972), A Deductive Method for Simulating Faults in Logic Circuits. IEEE Transactions on Computers. May 1972. Vol. C-21, No. 5, pp. 464-471. 
    https://doi.org/10.1109/T-C.1972.223542
23. Vinod N. et al. (2020), Performance Evaluation of LUTs in FPGA in Different Circuit Topologies. 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1511-1515. 
    https://doi.org/10.1109/ICCSP48568.2020.9182074
24. Efanov D. Pogodina T. (2023), Properties Investigation of Self-Dual Combinational Devi­ces with Calculation Control Based on Hamming Codes. Informatics and Automation. Vol. 22, Iss. 2, pp. 349-392.
    https://doi.org/10.15622/ia.22.2.5

Full text: PDF

M.S. Yaroshynskyi, O.V. Sirotkin, D.P. Sinko, S.B. Hunko, D.O. Manoliuk

Èlektron. model. 2023, 45(2):34-43

https://doi.org/10.15407/emodel.45.02.034

ABSTRACT

Classifications are widely used in semantic networks and decision support systems based on formal knowledge and are part of computer ontologies. Classifications and computer ontologies built on them are the result of the work of one or more experts. As a result, such classifications reflect the subjective view of the author or authors on the world and the relationship between the classes (concepts) of the created classification. In the work, the authors propose an approach that will allow assessing how correctly the classification is constructed.

KEYWORDS

classification, algebra of classifications, isomorphism of classifications, correctness of classifications, rules of division of classes.

REFERENCES

1. Korotkov, E.M. (2015), “Management systems research”, Yurait,, Russia, 228 p.
2. Basipov A.A., Demich O.V. (2012), “Semantic search: problems and technologies”, Vestnik Astrakhanskogo hosudarstvennogo tekhnycheskogo unyversiteta Seriia Upravlenie I vichislitelnaia tekhnika i informatika, no. 1, pp. 104-11.
3. Ivlev Yu.V. (2008), “Logics. Short course. Study guide”, Prospekt,, Russia, 304 p.
4. Shatalkin A.I. (2012), “Taxonomy. Basis, principles, and rules”, KMK,, Russia, 600p.
5. V. (2013), “Mathematical fundamentals of the theory of machine learning and forecasting”, MCNMO, M., Russia, 387p.
6. Goldblat R. (1983), “Topos. Categorical analysis of logic”, Mir, M., Russia, 488 p.
7. Berztis, A.T. (1974), “Data structures”, Statistika, M., Russia, 408
8. Adamek J. H, Strecker G.E. (2009), “Abstract and Concrete Categories. The Joy of Cats”, Dover Pub Inc., 517 p.
9. Kravtsov. H.A. “Model of computations over classifications”, Elektronne modelyuvannya, Vol. 38, no. 1, pp.73-87.
   https://doi.org/10.15407/emodel.38.01.073

Full text: PDF

A.O. Taranowski, V.D. Samoylov

Èlektron. model. 2023, 45(2):44-60

https://doi.org/10.15407/emodel.45.02.044

ABSTRACT

Since its launch there has been a plethora of publications about what ChatGPT can and can’t be used for. While OpenAI remained silent about the exact place of ChatGPT in its framework of language models. Just ‘Try ChatGPT’ appeared to be not the best way to consider its applications for less evident purposes that just composing an essay. The author has strong belief that knowing what something really is adds a lot to one’s understanding of what that something is capable of. This article therefore first considers what ChatGPT really is following its missing manual. That allows than overcoming some crucial limitations of ChatGPT in order to propose, substantiate, and experimentally support its applicability to multiple-choice test items construction on the way to increase automation and optimisation in knowledge assessment systems in order to reach the level of that being expertless. The approach proposed is heavily based upon GUI client for ChatGPT while benefits of being used through API are also explained in the light of integration into existing knowledge assessment systems, additional effects, and future transition to GPT-4.

KEYWORDS

artificial intelligence, large language models, knowledge assessment.

REFERENCES

1. Bulakh, I.Ye., Mruha, M.R. (2006), Stvoriuiemo yakisnyi test [Creating a quality test], Maisterklas, Kyiv, Ukraine [in Ukrainian]
2. Abramovych,R.P. (2020), Metody ta zasoby konstruiuvannia kompiuternykh system pidgotovky operatyvno-dyspetcherskogo personal nyzhchykh rivniv v energetytsi. Dysertatsiya na zdobuvannia naukovogo stupenia kandidata tekhnichnykh nauk: 05.13.05 [Methods and tools of designing computer aided systems for training lower levels operational and dispatcher personnel in Energy Engineering. Dissertation for the candidate of technical science degree: 05.13.05], Kyiv [in Ukrainian].
3. “IntroducingChatGPT”, OpenAI, available at: https://openai.com/blog/chatgpt (accessed 8 April 2023).
4. “What is ChatGPT?”, OpenAI, available at: https://openai.com/en/articles/6783457-what-is-chatgpt (accessed 8 April 2023).
5. “Models”, OpenAI, available at: https://platform.openai.com/docs/models (accessed 8 April 2023).
6. “Model index for researchers”, OpenAI, available at:https://platform.openai.com/docs/model-index-for-researchers (accessed 8 April 2023).
7. “Product”,OpenAI, available at: https://openai.com/product (accessed 8 April 2023).
8. “What is ChatGPT Plus?”, OpenAI, available at:https://help.openai.com/en/articles/6950777-what-is-chatgpt-plus (accessed 8 April 2023).
9. “Introducing ChatGPT and Whisper APIs”, OpenAI, available at: https://openai.com/blog/introducing-chatgpt-and-whisper-apis (accessed 8 April 2023).
10. “What is ChatGPT?”,OpenAI, available at: https://platform.openai.com/docs/chatgpt-education/what-is-chatgpt (accessed 8 April 2023).
11. “Large language model”, Wikipedia, available at: https://en.wikipedia.org/wiki/Large_language_model (accessed8 April 2023).
12. Gozalo-Brizuela, and Garrido-Merchán, E.C. (2023), ChatGPT is not all you need. A State of the Art Review of large Generative AI models, available at: https://arxiv.org/pdf/2301. 04655.pdf (accessed9 April 2023).
13. “GPT‑4”, OpenAI, available at: https://openai.com/research/gpt-4 (accessed 8April2023).
14. “An important next step on our AI journey”, available at: https://blog.google/technology/ai/bard-google-ai-search-updates (accessed9 April 2023).
15. “OpenAI and Microsoft extend partnership”, OpenAI, available at: https://openai.com/blog/openai-and-microsoft-extend-partnership (accessed9 April 2023).
16. “Microsoft and OpenAI extend partnership”, Microsoft, available at: https://blogs.microsoft.com/blog/2023/01/23/microsoftandopenaiextendpartnership (accessed9 April 2023).
17. “Terms of use”,OpenAI, available at: https://openai.com/policies/terms-of-use (accessed 9 April 2023).
18. Samoilov,D., Abramovich, R.P., Lepatiev A.O. (2020), “Computer technologies for the development of training systems for the energy industry”, Elektronne modelyuvannya, Vol. 42, no. 3, pp. 89-97.
    https://doi.org/10.15407/emodel.42.03.089
19. “Elekrtyschnyi generator” [Electric generator], Wikipedia, available at: https://uk.wikipedia.org/wiki/Електричний_генератор (accessed9 April 2023).
20. “Electric generator”, Wikipedia,available at: https://en.wikipedia.org/wiki/Electric_generator (accessed 9 April 2023).
21. Illustration to multimedia training course,Computer‑based personnel training courses ASOT, available at: http://asot.com.ua/img/portfolio/mtc/mtc_screenshot_4.png (accessed 10 April 2023).
22. Multimedia training courses and banks of test questions,Computer‑based personnel training courses ASOT, available at: http://asot.com.ua (accessed 10 April 2023).
23. Zakon Ukraiyny “Pro vykorystannia Iadernoiy tnergiiy ta radiatsiinu bezpeku” [Law of Ukraine On Use of Nuclear Power Use and Radiation Safety], Article 22, available at: https://zakon.rada.gov.ua/laws/show/39/95-вр [in Ukrainian] (accessed 10 April 2023).

Full text: PDF