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5th International Conference on control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2023

作者： Mikhailova, Tatiana Mustafin, Ilnur Mikhailov, Vladimir Miftakhov, Eldar Mustafina, Svetlana Ufa University of Science and Technology Department of Mathematical Modeling Ufa Russia Kazan Federal University Institute of Computational Mathematics and Information Technologies Kazan Russia

ISBN: (纸本)9798350309126

This article presents an approach to organizing a database for storing and processing the results of experiments on the polymerization of isoprene using microheterogeneous catalytic systems in a batch mode. The proposed approach can be applied both to work with production data and with the results of computational experiments. The implementation of this approach will automate the process of recording data into a database, which will greatly facilitate the structuring of information. As an example, the work presents the results of a computational experiment on the polymerization of isoprene in a reactor in the presence of a titanium-containing catalyst system. © 2023 IEEE.

关键词： Database systems

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Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Exponentially Weighted Moving Variance (MEWMV) Chart Based on Residual XGBoost Regression for Monitoring Water Quality

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Engineering Letters 2023年第3期31卷 1001-1008页

作者： Sulistiawanti, Nur Ahsan, Muhammad Khusna, Hidayatul Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Water Treatment Plant (WTP) in Palopo City, Indonesia, is one of the national companies engaged in the provision of clean water services for the people of Palopo City. There are 5 WTP managed in Palopo City. Testing the quality of production water at each WTP uses several interrelated quality characteristics, including turbidity, pH, and residual chlorine. The data used is obtained from the results of testing the quality of production water at each WTP in July-December 2021 for a daily period. In this study, the method used to control the quality of production water at each WTP is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to control the process mean and the Multivariate Exponentially Weighted Moving Variance (MEWMV) control chart to control process variability based on residual XGBoost regression to overcome autocorrelation. In phase I, the process has been statistically controlled for both the variance and the process mean. The weighting values for the process variability are ω = 0.4 and λ = 0.4, and for the process mean is λ = 0.3. In phase 2, based on the data analyzed only WTP V in Batupapan whose process variability has been statistically controlled. The other WTPs in phase 2 have not been statistically controlled for both the variability and the process mean. © 2023, International Association of Engineers. All rights reserved.

关键词： Autocorrelation

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Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickelbased superalloys

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npj Computational Materials 2024年第1期10卷 788-798页

作者： Tuhin Mukherjee Junji Shinjo Tarasankar DebRoy Chinnapat Panwisawas Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Next Generation Tatara Co-Creation Centre Shimane UniversityMatsueJapan Department of Materials Science and Engineering Pennsylvania State UniversityUniversity ParkPAUSA School of Engineering and Materials Science Queen Mary University of LondonLondonUK

Acritical issue in laserpowder bed fusion(LPBF)additivemanufacturing is the selective vaporization of alloying elements resulting in poor mechanical properties and corrosion resistance of *** process also alters the part’s chemical composition compared to the *** we present a novel multi-physics modeling framework,integrating heat and fluid flow simulations,thermodynamic calculations,and evaporation modeling to estimate and control the composition change during LPBF of nickel-based *** validation confirms the accuracy of our ***,we quantify the relative vulnerabilities of different nickel-based superalloys to composition change quantitatively and we examine the effect of remelting due to the layer-by-layer deposition during the LPBF *** variations in evaporative flux and compositions for each element were determined,providing valuable insights into the LPBF process and product *** results of this study can be used to optimize theLPBF process parameters such as laserpower,scanning speed,and powder layer thickness to ensure the production of high-quality components with desired chemical compositions.

关键词： corrosion alloying additive

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IoT-Driven Smart automation for Chain Tensioner Arrangement and Hardening in Automotive Systems 4

IoT-Driven Smart Automation for Chain Tensioner Arrangement ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Selvakarthi, D. Arunraja, M. Gnaneshwaran, P. Jaishnavi, S. Kongu Engineering College Department of Electronics and Instrumentation Engineering Perundurai Tamilnadu Erode638060 India

ISBN: (纸本)9798331529635

This research study proposes a smart automation solution for the assembly and hardening analysis of chain tensioners, essential components in automotive systems, particularly in two-wheeler engines. Traditionally, the manual threading of tensioner components onto wire for heat treatment has been a laborious and error-prone process. The proposed IoT-driven automated system is designed for the efficient arrangement of chain tensioner components and subsequent hardening analysis process. The arrangement process, the system conducts hardening analysis utilizing a micro-hardness testing system. This automated solution enhances precision, reduces errors, and increases production speed and consistency and the automated system seamlessly integrates the arranging the chain tensioning processes with the hardening processes, offering real-time monitoring and control through IoT technologies. It significantly reduces manual intervention, enhances efficiency, and ensures consistent and accurate results during the component arrangement and hardening analysis. By streamlining these critical processes, the system contributes to improved productivity, precision, and reliability in the arrangement of chain tensioners, thereby supporting advancements in the automotive system assembly line. © 2024 IEEE.

关键词： Hardness testing

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Adaptive Sliding Mode Quantized control under Hybrid Cyber-Attacks 43

Adaptive Sliding Mode Quantized Control under Hybrid Cyber-A...

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43rd Chinese control Conference, CCC 2024

作者： Mao, Dancheng Niu, Yugang Li, Jiarui Xu, Kun East China University of Science and Technology The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Logistics Engineering College Shanghai Maritime University Department of Electrical Automation Shanghai201306 China

ISBN: (纸本)9789887581581

This work investigates the sliding mode quantized control problem for networked control system (NCS) under Markov-conforming hybrid cyber-attacks. The system state is first quantized via a dynamic uniform quantizer, whose parameter is adaptively updated via a state-dependent adjusting strategy. A one-to-one mapping technique is used to cope with the hybrid attacks obeying Markov chains in sensor-controller (S-C) and controller-actuator (C-A) channel. And then, a sliding mode control (SMC) scheme is designed by means of the available quantized state signals via a compensation method. Furthermore, sufficient conditions are derived to attain the stability of the closed-loop system and the reachability of the specified sliding surface simultaneously. Finally, the practical simulation results are provided to illustrate the proposed security control strategy. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Cyber attacks

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Optimal control of SOEC-Based Hydrogen production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

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IEEE Transactions on Energy Conversion 2025年第2期40卷 724-741页

作者： Zhang, Wei Peng, Jiangkai Luo, Yusheng Liu, Wenxin Westover, Tyler L. Casteel, Micah J. Operation Technology/ETAP IrvineCA92618 United States GoldenCO80401 United States Midcontinent Independent System Operator CarmelIN46032 United States Research Laboratory Department of Electrical and Computer Engineering BethlehemPA18015 United States Idaho National Laboratory Idaho FallsID83415 United States

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. To overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Review: Advances in efficient separation of paraxylene from mixed xylene isomers using SMB technology and MOF materials

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Journal of Industrial and Engineering Chemistry 2025年 148卷 52-68页

作者： Han, Daoyuan Wu, ShiJie Xia, ZeHui Ao, Ran Qi, LinJie Aromatics Operation Department Sinopec JiuJiang Petrochemical Company JiuJiang332000 China

In recent years, the global demand for paraxylene (PX) has increased significantly, prompting extensive research into the refinement of the SMB adsorption process and the selection of optimal adsorbent materials. This paper provides a comprehensive overview of advancements in SMB technology, particularly focusing on developments in scrubbing strategies and modeling over the past few years. Unlike other processes, SMB technology leverages the differential adsorption capacities of components in a mixture across mobile and stationary phases, achieving PX purities as high as approximately 99.7%. The paper also reviews recent progress in the use of MOFs as adsorbents for the separation of xylene isomers, discussing aspects such as adsorption capacity, selectivity, and the underlying separation mechanisms. Compared to traditional adsorbent materials, MOFs offer adjustable pore structures, high surface areas, flexible main body structures, and a variety of species, demonstrating high selectivity coefficients and stable adsorption performance in xylene separation experiments. Overall, MOFs present promising prospects for use in the SMB adsorption process. Consequently, this paper reviews both the production and separation of PX using SMB technology and the advancements in MOFs as adsorbent materials, anticipating their significant impact on future research. © 2025 The Korean Society of Industrial and Engineering Chemistry

关键词： Xylene

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Functional Test-Cost Reduction Based on Fault Tree Analysis and Binary Optimization 43

Functional Test-Cost Reduction Based on Fault Tree Analysis ...

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43rd Chinese control Conference, CCC 2024

作者： Zuo, Xiaojie Wang, Kangcheng Zhao, Yun-Bo Kang, Yu Bai, Peng Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Science and Technology of China Department of Automation Hefei China University of Science and Technology of China Institute of Advanced Technology Hefei China Lcfc Hefei China

ISBN: (纸本)9789887581581

With the rapid increase in the complexity of electronics, the cost of the functional testing process used to ensure product functionality continues to rise. Optimization modeling based on reliability analysis is an effective approach to reduce testing costs. However, the reliability calculations of existing methods often exhibit significant deviations, making it challenging to guarantee the effectiveness of the resulting testing strategies in practical applications. To address this issue, this article proposes an optimization modeling method that integrates statistical analysis and reliability analysis. The expression for the reliability of the system's key stage is formulated by analyzing the system's reliability. Statistical analysis is utilized to exploit the inherent reliability information in the enormous process data to determine the probability of the root causes of system failures. The reliability of the key stage is quantitatively calculated based on the structure of the fault tree tailored for the system. On this basis, a binary optimization model is established to obtain a testing strategy with strong generalization ability and reduce the cost of functional testing. The effectiveness of the proposed method is verified through a simulation case study © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Smart manufacturing

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Integrating Real-Time automation and Big Data Analytics in the Oil and Gas Industry 15

Integrating Real-Time Automation and Big Data Analytics in t...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Amale, Ashvin B. Kantharaj, I.K. Ashok, Bhandari Mahesh Amudha, A. Bareja, Lakshay Raut, Priya M. Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Karnataka Bangalore India Department of IT Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Agadtantra evam vidhi vaidyak Datta meghe ayurvedic medical college hospital and research centre. Wanadongri Nagpur India

ISBN: (纸本)9798350370249

The oil and gas industry is arguably one of the most important industries in this modern world because some say it supports civilization as we know it. With the changing dynamics and increased competition, this sector is growing in demand for convenient solutions to improve operations with higher profits. Implementing real-time automation and big data analytics has become vital for the oil and gas industry. Real-time automation refers to using a control system, sensors, and an IT solution to monitor or control operations in real-time. It leads to faster decision-making and efficiency in drilling, production, and distribution processes. At the same time, big data analytics is about storing and analyzing massive amounts of data from different sources—these are sensor data production processes for the oil and gas industry market trends. Big data analytics can intelligently detect patterns & trends with the help of sophisticated algorithms along with machine learning, based on which necessary insights are drawn that lead to better operations and decision-making. Why these technologies will transform the oil and gas sector: Individually, both powerful new technologies provide many benefits, but integrated, they can potentially change the game in this industry. When combined with real-time automation (robots and material handling equipment), it can lead to actionable insights as things are happening, helping the business get control of its operations and reduce cost, ultimately leading to increased profitability. ©2024 IEEE.

关键词： Profitability

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Industry 5.0 and production Planning and control in Manufacturing Industries

Industry 5.0 and Production Planning and Control in Manufact...

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2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oladeinde, Ademola Henry Ojo, Oluwasenu O. Bells University of Technology Department of Mechanical Engineering P.M.B 1015 Ota Nigeria

ISBN: (纸本)9798350358155

The integration of Industry 5.0 principles into production planning and control in manufacturing industries is surveyed in this study. The industrial environment is changing, and Industry 5.0 is a paradigm that emphasises the harmonious relationship between modern technologies and talented human workers as the industry moves towards increasing automation, connectivity, and human-machine collaboration. The state-of-the-art in Industry 5.0 applications for improving production planning and control procedures is reviewed in this paper. The study elucidated the ways in which manufacturing operations might be optimised through the synergistic utilisation of cyber-physical systems, the Internet of Things (IoT), artificial intelligence, and human expertise. Future directions for research and development in this field were also reviewed, as well as the opportunities and challenges of implementing Industry 5.0 in production planning and control. © 2024 IEEE.

关键词： Smart manufacturing

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