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14th Asian control conference (ASCC)

作者： Qian, Binjie Chen, Qifu Wu, Min Cao, Weihua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

Blast furnace ironmaking is a key process in iron and steel production, and the fuel ratio has an important impact on the smelting efficiency, cost and environmental impact of the blast furnace. This study aims to provide new ideas and methodological support for the optimized management and control of the blast furnace ironmaking process by exploring the effects of different factors on the fuel ratio. We firstly sorted out the factors affecting the fuel ratio based on the blast furnace process mechanism, and then deeply analyzed the causal relationship of each parameter on the fuel ratio based on the transfer entropy value, and screened out the parameters that did not have a significant effect on the fuel ratio. Taking the remaining key parameters as inputs, the LSTM and GRU models, which can capture time series information, are used to predict the mean and variance of the fuel ratio, respectively, and the two are combined to compute the final fuel ratio, which overcomes the problem of poor model generalization brought by small fluctuations in the training data of fuel ratio. The experimental results show that the convergence speed of the model is significantly improved after parameter screening, and the fuel ratio can be effectively predicted within a certain error range.

关键词： Fuel ratio causal analysis transfer entropy data forecasts LSTM GRU

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Physics-based model as a versatile tool towards advanced process control of the naphtha distillation unit

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2025年 218卷 53-65页

作者： Stabrov, Sergey Shevlyagina, Svetlana Russian Acad Sci Inst Automat & Control Proc Far Eastern Branch Vladivostok 690041 Russia Far Eastern Fed Univ Vladivostok 690922 Russia

Following the recent successes with low input data variability and soft sensor design under feed composition changes, this study proposes, among other things, the use of a physics-based approach to improve multivariable model predictive control (MPC) of naphtha distillation. Unlike the industrial settings, where the influence of other manipulated variables is difficult to exclude due to the actions of the human operator, a physically based modeling provides close to an ideal step-by-step and one-by-one testing of the chemical process, resulting in improved accuracy of the transfer function matrix used for MPC design. The proposed approach has been tested on canonical and alternative control schemes used in stabilized naphtha production. Importantly, the physicsbased model resolved all the issues associated with unavailability to reach the set points in controlling the quality of end products when compared with MPC built on the industrial data only irrespective of the control scheme considered. As a result, the steady-state controllability analysis and the closed-loop process behavior highlight that an alternative control structure with transfer function matrix obtained on a physics-based model is a better choice for the industrial case study. Thus, the developed strategy for MPC design was approved as relevant for the cases when a preliminary control scheme requires an update or optimized control scheme without affecting production is of great demand.

关键词： Model predictive control Physics-based model Naphtha distillation Dynamic simulation controllability analysis

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modeling and control of Continuous Countercurrent Tangential Chromatography

Modeling and Control of Continuous Countercurrent Tangential...

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American control conference (ACC)

作者： Lu, Amos E. Dighe, Anish Braatz, Richard D. MIT 77 Massachusetts Ave Cambridge MD 02139 USA

ISBN: (纸本)9798350382662;9798350382655

A model-based control system is developed for continuous countercurrent tangential chromatography. The mechanistic model is formulated as a distributed parameter system. The computational cost of the mechanistic model is reduced by reformulating the partial differential equations as ordinary differential equations via the method of characteristics. The model parameters are fit to experimental data for the capture of a monoclonal antibody from clarified bioreactor material. An control problem is formulated for the objective of maximizing system productivity subject to a constraint on the protein recovery, and analyzed to provide insight into the process parameters that strongly affect the closed-loop performance.

关键词： Chromatographic analysis

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Transferred Bosting Learning for Transient Stability analysis of Power Systems 24

Transferred Bosting Learning for Transient Stability Analysi...

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International conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Zhang, Qiqi State Grid Shanghai Municipal Elect Power Co Shanghai Peoples R China

ISBN: (纸本)9798400716638

The power system holds a crucial role in the energy supply system, being strategically important for national economic development and social activities. Transient stability analysis and preventive control are pivotal in ensuring the secure and stable operation of power systems. In the face of the intricate and ever-changing power system operation modes, the robust adaptive capabilities of online transient stability analysis and preventive control are essential cornerstones for ensuring the system's safety and stability. The rapid advancements in power big data and artificial intelligence technology present new opportunities for online transient stability analysis. This paper focuses on data-driven analysis, particularly in scenarios involving changes in power system topology. We present an online transient stability analysis method using transfer boosting learning, incorporating the concept of data transfer learning into the transient stability analysis modeling process. By effectively leveraging information from existing transient stability analysis data, we facilitate the construction and updating of transient stability analysis models under new fault and topology scenarios. This method achieves the model's easy scalability and updateability. Experimental validation on power data confirms the method's effectiveness.

关键词： power system transfer bosting learning online transient stability analysis

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Active Fault-Tolerant control to Prevent Hanger Bending During Configuration Transformation of 3D Cable System in Suspension Bridges

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3249-3249页

作者： Bai, Yunteng Wang, Xiaoming Zhao, Zhiyan Wang, Huan Changan Univ Sch Highway Xian 710018 Peoples R China State Grid Xixian New Area Elect Power Supply Co Xian 712000 Peoples R China Sichuan Highway Planning Survey Design & Res Inst Chengdu 610041 Peoples R China

Effectively preventing hanger bending damage during the configuration transformation of the spatial main cables in suspension bridges is a critical challenge, particularly under the influence of construction errors. This study proposes an active fault-tolerant control method that integrates real-time data feedback, tolerance interval inversion technology, and a suspended lateral bracing (SLB) system to mitigate the risk of hanger bending damage in real time. The method establishes a dynamic inversion mechanism, utilizing data feedback, constraint function reconstruction, and secondary optimization to compensate for construction errors. This ensures that hangers remain undamaged throughout the transformation process. Construction errors are quantified as intervals, with the lower bound of the reliability interval used to account for extreme disturbances. This transforms the inversion process into a multi-objective optimization problem constrained by the worst reliability conditions. By integrating finite element analysis (FEA), reliability analysis, surrogate modeling, and interval analysis, the proposed approach establishes a direct relationship between design variables and the lower bound of the reliability interval. Case study results demonstrate that the proposed method not only ensures structural performance and hanger safety, but also significantly enhances the constructability of the configuration transformation. Additionally, it provides a larger fault tolerance margin, thereby improving the overall efficiency and safety of the construction process.

关键词： self-anchored suspension bridge configuration transformation hanger bending damage active fault-tolerant control suspended lateral bracing system multi-objective optimization

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data-driven Thermal modeling for Electrically Excited Synchronous Motors - A Supervised Machine Learning Approach

Data-driven Thermal Modeling for Electrically Excited Synchr...

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Transportation Electrification conference and Expo (ITEC)

作者： Tatari, Farzaneh Trapp, Davis Schneider, Jason Aligoudarzi, Mohsen Mirza Drive Syst Design Inc Control Syst Farmington Hills MI 48335 USA

ISBN: (纸本)9798350317671;9798350317664

This paper proposes a data-driven supervised machine learning (ML) for online thermal modeling of electrically excited synchronous motors (EESMs). EESMs are desired for EVs due to their high performance, efficiency, and durability at a relatively low cost. Therefore, obtaining precise EESM temperature estimations are significantly important, because online accurate temperature estimation can lead to EESM performance improvement and guaranteeing its safety and reliability. In this study, in addition to the default inputs' data, EESM losses data is leveraged to improve the performance of the proposed ML approach for thermal modeling. Exponentially weighted moving averages and standard deviations of the inputs are also incorporated in the learning process to consider the memory effect for modeling a dynamical thermal model. Using the experimental data of an EESM prototype, the performance of ordinary least squares (OLS) method is evaluated through a complete training, testing and cross-validation process. Finally, simulation results will provide the key performance metrics of OLS for EESM thermal modeling.

关键词： data-driven learning Electrically excited synchronous motors (EESMs) Machine learning (ML) Thermal modeling

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EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Khan, Zahid Saghir, Aamir Katona, Attila Kosztyan, Zsolt T. Univ Pannonia Dept Quantitat Methods Egyet Str 10 Veszprem Hungary Mirpur Univ Sci & Technol Dept Stat Mirpur Pakistan

The Maxwell distribution is extensively employed in statistical modeling to analyze data, notably in the fields of chemistry, astrophysics, demography, actuarial sciences, economics, industry, and engineering. Recently, the application of the Maxwell data generating process (DGP) has focused extensively on the domain of statistical control charts. The V chart and V SQ chart are often used to identify unexpected changes in the distributional shift of the Maxwell process. The V SQ chart offers considerably better performance than the V chart for detecting moderate to large shifts in the scale parameter. However, the V SQ chart adopts the fundamental structure of the Shewhart monitoring scheme and is insensitive to small alterations in the target parameter. We propose anew control chart, namely, the Maxwell exponentially weighted moving average (MXEWMA) chart, for improved monitoring of quality attributes that are assumed to conform to Maxwell data generation. The factors used to design the parameters of the proposed chart are computed at different false alarm probabilities and across various sample sizes. The effectiveness of the suggested scheme is considered in terms of the different features of the run length (RL) distribution, including the average, median and standard deviation. A comparative study of the MXEWMA chart with the existing V SQ chart was performed across various sample sizes. The comparative analysis showed that the MXEWMA chart is an effective alternative and performs well in detecting reasonably small shifts in the parameter. Simulated data are employed to describe the computational procedure of the MXEWMA scheme. The simulation analysis demonstrated that the MXEWMA chart outperforms the existing method in identifying slight changes in the studied parameters. A real dataset is also considered to support the theoretical part of the work.

关键词： Maxwell quality characteristic Skewed data Estimation control chart Run length Simulation

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Optimization of an Air Pressure System: A Multi-Objective control and modeling Approach

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IEEE ACCESS 2024年 12卷 96691-96703页

作者： Huilcapi, Victor Castillo, Christian Sanchez, Daniel Cajo, Ricardo Univ Politecn Salesiana Fac Ingn Guayaquil 090109 Ecuador Escuela Super Politecn Litoral Fac Ingn Elect & Comp ESPOL Campus Gustavo Galindo Guayaquil 9000 Ecuador

This paper presents the application of a methodological approach based on multi-objective optimization for identifying a nonlinear model and designing controllers for an air pressure system. First, the parameters of the system's nonlinear model are determined based on experimental data obtained from the process. Subsequently, the methodology is extended to tuning cascade PI controllers, where experimental tests are performed in the laboratory. This methodology allows a designer to have a detailed view of the proposed multi-objective problem (both in the modeling and controlling the air pressure system). Depending on the level of detail required, the design objectives of the multi-objective problem are proposed. In this case, two objectives are proposed to evaluate the model's performance and three objectives for evaluating the control system's quality. Among the advantages offered by this methodology is the easy understanding of the conflicts that arise between the design objectives, which allows for selecting an optimal solution according to the preferences established by a designer and based on detailed information on the system's performance. In this case, a conflict between the dynamics of the pressures of the air pressure system could be evidenced, both in the modeling and in the control. The proposed methodology also allows the analysis of the behavior of the air pressure system in a global framework without loss of information, which would not be feasible in a traditional single-objective analysis that agglutinates all the information in a single design objective. Although the decision-making stage of the proposed methodology is more complex than a single-objective analysis, it is justified as it provides detailed and complete information to a designer to make accurate and effective decisions on how to model and control the compressed air pressure system effectively.

关键词： Optimization methods Atmospheric modeling Tuning Vectors Evolutionary computation Visualization PI control Pareto optimization Pressure measurement Air quality Air pressure systems multi-objective optimization methodology multi-objective evolutionary algorithm (ev-MOGA) level diagram (LD) cascade PI controller nonlinear model decision-making processes

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Applications and prospects of machine learning for aerosol jet printing: A review

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IISE TRANSACTIONS 2024年第10期56卷 1038-1057页

作者： Guo, Shenghan Ko, Hyunwoong Wang, Andi Arizona State Univ Sch Mfg Syst & Networks Mesa AZ 85281 USA

Aerosol Jet Printing (AJP) is an additive manufacturing process that deposits ink-like materials suspended as an aerosol mist. AJP creates three-dimensional (3D) functional structures onto flat or conformal surfaces in complex shapes without the aid of additional tooling, enabling the manufacturing of extremely fine electrical interconnects with freeform structures. Due to the novelty and complexity of AJP, physical understanding is rather limited, hindering physics-based process modeling and analysis. Fortunately, the data resources from AJP applications, e.g., 3D Computer-Aided-Design data, Standard Triangle Language files, in-situ images of part, and nozzle motion records, provide an unparalleled opportunity for developing data-driven, Machine Learning (ML) methods to characterize AJP processes, support process control, and facilitate product improvement. To thoroughly identify the newfound opportunities, this study reviews state-of-the-art ML methods used in AJP applications, investigates open issues in AJP, and outlooks future development of ML-based research topics for AJP. It sheds light on how to maximize the value of ML on AJP data to develop scalable, generalizable decision-making methods. More future works along the direction will be motivated.

关键词： Aerosol jet printing data-driven machine learning process control quality improvement

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An Improved Model Predictive Torque control for Switched Reluctance Motors Based on Variable Hyperbolic Tangent Function modeling

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 5029-5041页

作者： Xu, Shuai Chen, Hanyang Wang, Peixin Nie, Rui Si, Jikai Zhengzhou Univ Elect & Informat Engn Dept Zhengzhou 450001 Peoples R China

In this article, an improved model predictive torque control (MPTC) method is proposed for switched reluctance motors (SRMs) based on variable hyperbolic tangent function modeling. First, the complete modeling of flux-linkage characteristics is developed based on finite measurement data using a variable hyperbolic tangent function. It could be found that only two parameters are necessary to obtain the corresponding flux-linkage at a given rotor position, significantly reducing the storage space required during the implementation process. Additionally, based on the inductance characteristics, an improved switching table is proposed with three switching states. Meanwhile, the operation principles of MPTC are presented with the improved switching table. Finally, the simulation analysis and experimental results are conducted on a four-phase 8/6 SRM to validate the effectiveness of the proposed improved MPTC. The verification results demonstrate that the proposed method can effectively reduce storage space, lower switching frequency, and exhibit minimal torque ripple.

关键词： Torque Switches Reluctance motors Torque control Torque measurement Inductance Analytical models Adaptation models Rotors Predictive models Hyperbolic tangent function improved switching table model predictive torque control (MPTC) switched reluctance motor (SRM) torque ripple reduction

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