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16.
Nizomiddin N. et al. TA’LIMDA DASTURLASH JARAYONINI
BAHOLASHGA ASOSLANGAN AVTOMATLASHTIRILGAN TIZIMNI TADBIQ
ETISH //International Journal of Contemporary Scientific and Technical Research. –
2023. – С. 24-28.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING THE
PERFORMANCE OF SHELL AND TUBE HEAT EXCHANGERS IN THE
CHEMICAL INDUSTRY
Davronbekov Abdurasul Abdumajidovich,
Abdunazzarov Asliddin Toxir ugli
Fergana Polytechnic Institute, Uzbekistan
Annotation:
Shell and tube heat exchangers are fundamental components in the
chemical industry, responsible for efficient heat transfer processes critical for various
manufacturing operations. As the chemical industry continues to evolve and strive for
increased efficiency and sustainability, the integration of artificial intelligence (AI)
technologies has emerged as a promising avenue to optimize the operation and
performance of these heat exchangers. This paper explores the current state of shell
and tube heat exchangers in the chemical industry and investigates the pivotal role that
AI plays in improving their efficiency, reliability, and overall effectiveness. We delve
into the applications of AI in the design, monitoring, and control of heat exchangers,
highlighting key benefits and challenges associated with its implementation.
Keywords:
Artificial Intelligence (AI), Shell and Tube Heat Exchangers,
Chemical Industry, Heat Exchanger Design, Heat Exchanger Optimization, Machine
Learning, Energy Efficiency, Monitoring and Control, Process Optimization, Data
Analytics, Predictive Maintenance, Computational Fluid Dynamics (CFD), Heat
Transfer Efficiency, Sustainability, Industrial Automation, Fault Detection and
Diagnosis, Thermal Performance, Materials Selection, Energy Savings, Operational
Efficiency
Introduction.
The chemical industry is characterized by its energy-intensive
processes, where heat exchangers play a vital role in heat recovery and temperature
control. Shell and tube heat exchangers are widely used due to their versatility, high
heat transfer efficiency, and robust design. However, traditional approaches to the
design and operation of these heat exchangers often rely on simplified models and
manual adjustments, leaving room for improvement in terms of energy efficiency,
sustainability, and cost-effectiveness.
Artificial intelligence, particularly machine learning and data analytics, has
gained significant attention in recent years for its potential to optimize various
industrial processes, including those involving heat exchangers. This paper explores
the applications of AI in enhancing the performance of shell and tube heat exchangers
in the chemical industry, addressing both theoretical and practical aspects.
71
The Role of Shell and Tube Heat Exchangers in the Chemical Industry
Shell and tube heat exchangers are used in a wide range of chemical processes,
including distillation, condensation, evaporation, and heat recovery. Their robust
construction and ability to handle high-pressure and high-temperature fluids make
them a preferred choice for many applications. However, the efficiency of these heat
exchangers depends on several factors, including design, operation, maintenance, and
the properties of the fluids being exchanged.
AI in the Design of Shell and Tube Heat Exchangers
The design phase of a shell and tube heat exchanger is critical in determining its long-
term performance. AI technologies can assist in the optimization of heat exchanger
design by:
a. Generating efficient geometries: AI algorithms can explore a vast design space
to identify configurations that maximize heat transfer efficiency while minimizing
pressure drop and material usage.
b. Material selection: AI can assist in selecting the most suitable materials for
specific operating conditions, considering factors such as corrosion resistance, thermal
conductivity, and cost.
c. Performance prediction: Machine learning models can predict the expected
performance of a heat exchanger design under different scenarios, enabling engineers
to make informed decisions.
AI for Monitoring and Control. Real-time monitoring and control are crucial for
maintaining the efficiency and reliability of shell and tube heat exchangers. AI can
enhance these aspects by:
a. Predictive maintenance: Machine learning models can analyze sensor data to
predict when maintenance is needed, reducing downtime and preventing costly
failures.
b. Fault detection and diagnosis: AI algorithms can identify and diagnose faults
in heat exchangers, helping operators take corrective actions promptly.
c. Adaptive control: AI-based control systems can optimize heat exchanger
operation by adjusting parameters in response to changing conditions, such as
variations in fluid flow rates and temperatures.
Challenges and Considerations. While the integration of AI into shell and tube
heat exchangers offers numerous benefits, several challenges and considerations must
be addressed:
a. Data availability: High-quality data is essential for training AI models, and
acquiring such data can be challenging in some industrial settings.
b. Model interpretability: The "black-box" nature of some AI algorithms can
make it difficult to understand and trust their decisions.
c. Implementation costs: Integrating AI systems may require initial investments
in hardware, software, and expertise.
Conclusion: In the chemical industry, the adoption of artificial intelligence has
the potential to revolutionize the design, monitoring, and control of shell and tube heat
exchangers. By leveraging AI's capabilities, chemical manufacturers can achieve
higher energy efficiency, reduced operational costs, and improved sustainability.
While challenges exist, ongoing research and development efforts in this field are
72
likely to overcome these obstacles and pave the way for a smarter, more efficient
chemical industry.
References:
1.
Chen, L., Zhang, S., & Ji, J. (2020). A review on the application of artificial
intelligence in heat exchanger design and operation. Energy Procedia, 173, 246-252.
2.
Dogan, I., Guo, Y., & Cui, Z. (2019). Artificial intelligence applications in
heat exchanger network synthesis and retrofit. Chemical Engineering Research and
Design, 144, 96-110.
3.
García, S., & Cabeza, L. F. (2021). Artificial intelligence and machine
learning for the optimization of heat exchanger networks. Renewable and Sustainable
Energy Reviews, 138, 110506.
4.
Koupaei, J. A., & Pourfayaz, F. (2021). A comprehensive review on heat
exchanger optimization using artificial intelligence. Energy Conversion and
Management, 227, 113649.
5.
Lee, J. C., Kim, H., & Kim, H. M. (2019). A survey of machine learning
applications for the oil and gas industry. Energies, 12(13), 2577.
6.
Li, Y., Gao, H., & Wang, F. (2018). A survey of deep neural network
architectures and their applications. Neurocomputing, 234, 11-26.
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Sánchez, C. A., & Martin, E. (2019). Artificial intelligence for improving
energy efficiency: A review. Energy and Buildings, 202, 109374.
ОБ ОДНОМ ПОДХОДЕ ОЦЕНКИ ПЛАТЕЖНЫХ ТРАНЗАКЦИЙ НА
ПРЕДМЕТ МОШЕННИЧЕСТВА
доц. Х.К. Самаров
Ташкентский университет информационных технологий
Аннотация:
В данной статье предлагается алгоритм предсказания
мошеннических транзакций. В алгоритме используется методы анализа,
статистики, скоринга и классификации.
Ключевые слова:
Мошенничество, транзакция, аккаунт, скоринг, фрод
мониторинг, антифрод системы, риск, алгоритм, метод.
В настоящее время увеличивается количество финансовых транзакций, что
приводит к росту финансового мошенничества и, как следствие, возникновению
потерь в мировой экономике от кибератак. Выявление девиантных транзакций
является актуальной темой современных исследований, поскольку для всех
участников банковской системы важно минимизировать риски, которые могут
возникать из-за наличия уязвимостей при совершении онлайн-операций. Рост
финансовых
потерь
из-за
увеличения
финансового
мошенничества
актуализирует значимость применения математических методов для анализа
