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Empirical Modeling of Unsteady Bulging in Continuous Slab Casters ⁎

IFAC-PapersOnLine 2025年第1期59卷 19-24页

作者： Julian Landauer Luca Gasparini Andreas Kugi Andreas Steinboeck Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute TU Wien Austria Automation and Control Institute TU Wien Austria

In the continuous casting of steel slabs, a constant mold level is crucial for the quality of the final products. However, mold level fluctuations may be caused by unsteady bulging, i.e., a time-varying bending of the solidified strand shell between the rolls of the roll array due to variations in the strand shell thickness. A control-oriented model of unsteady bulging is needed to design controllers that should suppress unsteady bulging systematically. Most existing models use complex, detailed, nonlinear formulations, typically requiring high computational costs. Therefore, these models are unsuitable for closed-loop simulations or applications in real-time control. As an alternative, this work presents a novel empirical model that allows parameterization based on usually available measurements from industrial continuous slab casters. The model facilitates fast dynamic simulations of unsteady bulging and its results accurately match measurements.

关键词： Continuous slab casting Steel industry Bulging Mold level Empirical model Frequency response Real-time capability

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Graph convolutional network soft sensor for process quality prediction

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Journal of process control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Model reference adaptive control of unknown control gain and asymmetrical static output constraints 41

Model reference adaptive control of unknown control gain and...

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第41届中国控制会议

作者： Penghao Chen Fei Liu Key Laboratory of Advanced Process Control for Light Industry Ministry of EducationInstitute of AutomationJiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The model reference adaptive control(MRAC) issue is addressed for a class of first-order linear time-invariant system(LTIS) with unknown control gain and asymmetric static output *** output constraints is efficiently handled by incorporating a barrier Lyapunov function(BLF).Completely unknown control gain is handled based on the Nussbaum *** the aid of Lyapunov synthesis method,the adaptive laws of unknown parameters are *** the properties of BLF and Barbalat's Lemma,it is proved that the tracking error converges asymptotically to zero while the output satisfies the *** comparison of the two simulation results is employed to demonstrate the efficacy of the suggested MRAC algorithm.

关键词： Model reference adaptive control Asymmetrical static output constraints Barrier Lyapunov function Unknown control gain Nussbaum function

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General expert consensus on the application of network pharmacology in the research and development of new traditional Chinese medicine drugs

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Chinese Journal of Natural Medicines 2025年第2期23卷 129-142页

作者： Shao Li Wei Xiao Institute of TCM-X/MOE Key Laboratory of Bioinformatics Bioinformatics DivisionBNRist/Department of AutomationTsinghua UniversityBeijing 100084China State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture Jiangsu Kanion Pharmaceutical Co.Ltd.Lianyungang 222047China

The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain ***,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug *** pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research *** emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM *** approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM *** is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug *** goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.

关键词： Network pharmacology Research and development of new traditional Chinese medicine drugs Expert consensus

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Transductive Transfer Broad Learning Soft Sensor for Multimode Quality Prediction 9

Transductive Transfer Broad Learning Soft Sensor for Multimo...

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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Zhu, Jialiang Deng, Hongying Xu, Danya Yang, Tao Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

ISBN: (纸本)9798350342239

In chemical processes, reliable soft sensors are generally established by enough labeled data. However, in most multimode processes, the collection of sufficient labeled data is difficult due to the high cost and complexity. In this work, transductive transfer broad learning (TTBL) is proposed for multimode quality prediction. By transferring the useful information from the related domain, unlabeled data in the prediction domain is utilized for modeling. First, the data feature is extracted by the feature and enhancement nodes. The similarity information of current and related domain data is captured by the k nearest-neighbor graph. Then, label information in the related domain can be transferred and similar information in the same domain can be retained by the manifold regularization framework. Finally, the output weight can be effectively calculated by the ridge regression algorithm. Experimental results on continuous stirred tank reactor datasets show the superiority of TTBL, compared with several common methods. © 2023 IEEE.

关键词： Forecasting

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Semi-Supervised Caustic Concentration Prediction in Alumina Production with Transformers 50

Semi-Supervised Caustic Concentration Prediction in Alumina ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Gao, Suting Yu, Wen Chai, Tianyou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Departamento de Control Automatico CINVESTAVIPN National Polytechnic Institute Mexico City Mexico

ISBN: (纸本)9781665464543

Accurate and real-time prediction of caustic concentration is essential for optimal control in alumina production. However, training effective models is often hampered by the scarcity of labeled assay data. This paper proposes a novel semi-supervised learning approach to address this challenge. We introduce a Transformer-based Auto-Encoder (AE) structure that uses both labeled and unlabeled data for robust caustic concentration prediction. The Transformer encoder extracts key features from various variables and time series data, enabling efficient low-dimensional representation. The decoder then performs a dual task of reconstructing the input data and predicting the caustic concentration. This structure combined with a robust regularization strategy enhances the model’s resilience to data noise and missing values. Consequently, the proposed method achieves accurate caustic concentration prediction using significantly less labeled data. This approach has the potential to significantly improve alumina production control by reducing reliance on expensive and time-consuming labeled data collection. © 2024 IEEE.

关键词： Self-supervised learning

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Real-Time Caustic Ratio control in Alumina Digestion Using Deep Reinforcement Learning 6

Real-Time Caustic Ratio Control in Alumina Digestion Using D...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Yu, Liyi Yu, Wen Cheng, Siyu Jia, Yao Chai, Tianyou Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China Cinvestavipn National Polytechnic Institute Departamento de Control Automatico Mexico City Mexico

ISBN: (纸本)9798350356618

Due to the lag time in the alumina digestion process (ALDP), traditional control methods that depend on delayed measurements often struggle to maintain the optimal caustic ratio, which is crucial for achieving optimal performance. This paper proposes a novel intelligent operational control method to address this challenge. Our multi-stage framework employs a fuzzy inference system (FIS) to generate an initial setpoint based on process knowledge. Subsequently, a bi-directional gated recurrent unit (BiGRU) network predicts the endpoint caustic ratio, assessing the validity of the initial setpoint. Finally, a deep deterministic policy gradient (DDPG) adjustor refines the setpoint in real-time based on the predicted and target values. Validated with industrial data, this method significantly improves the qualification rate of the caustic ratio compared to manual operation. It demonstrates the effectiveness of DDPG for real-time setpoint adjustment, leading to improved product quality in the ALDP. © 2024 IEEE.

关键词： Deep reinforcement learning

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Towards a systematic and knowledge-based requirements and conceptual engineering for modular electrolysis plants

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Energy Informatics 2023年第1期6卷 43页

作者： Markaj, Artan Lorenz, Julius Scholz, Lena Henkel, Vincent Fay, Alexander Institute of Automation Technology Helmut Schmidt University Hamburg Holstenhofweg 85 Hamburg Hamburg22087 Germany Chair of Process Control Systems Dresden University of Technology Georg-Schumann-Strasse 18 Saxony Dresden01062 Germany

The production of green hydrogen and its scale-up require the enginering and installation of new electrolysis plants. Modular electrolysis plants ease the scale-up as they allow to add further modules with growing demand. While many engineering methods focus on the detailed planning of the plants and their automation systems, the early engineering phases are scarcely considered, supported or formalized. However, especially these phases are crucial in the current scale-up of modular electrolysis plants. In this paper, an intention-based engineering approach for the early engineering phases Requirements Engineering and Conceptual Engineering for modular electrolysis plants is presented and evaluated based on three different use cases. The approach is based on Goal-oriented Requirements Engineering from Software Engineering and relies on an early, systematic as well as formalized description and analysis of intentions of different engineering disciplines. © 2023, The Author(s).

关键词： Requirements engineering

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Research and Analysis of the Method for Measuring the Moisture Content of Cottonmaterials at Ultrahigh Frequencies

Research and Analysis of the Method for Measuring the Moistu...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Turkmenov, Khasan E. Ubaydullayeva, Shakhnoza R. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The article provides a theoretical analysis of the ultrahigh frequency (microwave) method of humidity measurement, discusses the problems of instrumentation of the method in question, and proposes a scheme of an experimental installation. Bulk cotton materials were selected as the objects of research. The electromagnetic wave radiation is analyzed during measurement and the evaluation of electrophysical characteristics requires the study of the functional dependence of the amplitude and phase shift on the mass ratio of humidity, mathematical models for various bulk cotton materials describing the joint effect of humidity, material density on the attenuation and phase shift of the passing wave are obtained. © 2023 IEEE.

关键词： Humidity control

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