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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Budynkova, Elizaveta Baybulatov, Artur Joint Stock Company «aLFA-BANK» Business Process Automation Directorate Moscow Russia Ivery Prangishvili Laboratory 31 'Distributed Data-Analysis and Control Systems' V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

ISBN: (纸本)9781665412308

Today, Industrial control System (ICS) databases are incredibly complicated with quite prolonged iterative lifecycle where every new iteration is driven by a new version of the database assignment. For the correct databases development, the assignments should be consistent and correspond to special requirements, but this is not always the case. Therefore, the checking, or verification, of the assignments is becoming especially important. However, the appropriate problem of databases assignments verification is not widely discussed, and available verification methods are not suitable enough for solving the problem. The article clarifies the place of the assignments verification in the ICS databases lifecycle and presents a novel algorithm for the assignments verification. The particular implementation of the algorithm is also considered. As an example, the results of the verification of the assignments for the NPP ICS databases are presented. © 2021 IEEE.

关键词： Database systems

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Impeller Position in a Magnetically Levitated Rotodynamic Blood Pump and Its Impact on In-Silico Hemocompatibility

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ASAIO Journal 2025年 2025 Apr 3页

作者： Grujic, Marko Schoefbeck, Rosmarie Thamsen, Bente Aigner, Philipp Röhrich, Michael Jakubek, Stefan Zimpfer, Daniel Granegger, Marcus Christian Doppler Laboratory for Mechanical Circulatory Support Department of Cardiac Surgery Medical University of Vienna Vienna Austria Department of Anaesthesia Intensive Care Medicine and Pain Medicine Medical University of Vienna Vienna Austria Division of Control and Process Automation Institute of Mechanics and Mechatronics Vienna University of Technology Vienna Austria

In magnetically levitated rotodynamic blood pumps (RPBs), the impeller position depends on a balance of electromagnetic and fluid dynamic forces. The aim of this study was to describe the impeller position of the HeartMate 3 over a wide range of operating conditions and assess its potential impact on hemocompatibility. Three-dimensional impeller positions were measured using a transparent HeartMate 3 pump casing, laser distance measurements, and a high-speed camera. Accompanying computational fluid dynamic (CFD) hemocompatibility predictions of a displaced and centered impeller at a typical operating point were compared. Impeller positions vary substantially with different operating points with a maximum axial displacement of 223 µm at 7 L/min and 7,000 rpm and a maximum radial displacement of 145 µm at 0 L/min and 7,000 rpm. In CFD, a displaced impeller had only a minor influence on global pump parameters ( © ASAIO 2025.

关键词： Computational fluid dynamics

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A Norm Optimal Iterative Learning control Approach With Efficient Computation

A Norm Optimal Iterative Learning Control Approach With Effi...

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Data Driven control and Learning Systems (DDCLS)

作者： Luyuan Gao Zhihe Zhuang Hongfeng Tao Yiyang Chen Wojciech Paszke Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi PR China School of Mechanical and Electrical Engineering Soochow University Suzhou PR China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach.

关键词： Uncertain systems Uncertainty Optimal control Cost function Numerical simulation Computational efficiency Numerical models

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Demand Response Optimization for the Enhancement of the Distribution System's Operation

Demand Response Optimization for the Enhancement of the Dist...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Maria Fotopoulou George Tsekouras Dimitrios Rakopoulos Vassiliki Kontargyri Department of Electrical and Electronics Engineering University of West Attica Athens Greece Chemical Process and Energy Resources Institute Centre for Research and Technology Hellas Athens Greece Control Systems and Industrial Automation Laboratory National and Kapodistrian University of Athens Athens Greece

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

Following the latest energy market trends, active distribution systems incorporate an ascending mix of flexible loads, which can participate in Demand Response (DR) strategies. The purpose of this research paper is to present a DR strategy for the Distribution System Operator (DSO) in order to enhance the system's operation and mitigate power quality issues, such as voltage drops, and congestion. The proposed algorithm aims at smoothing the load curve, taking into consideration the available flexible load and the limitations of the system. The algorithm is demonstrated on a model of the distribution system of Skiathos island, Greece, in the context of ENFLATE, a Horizon Europe project, and future scenarios with photovoltaic (PV) penetration are studied as well. According to the results, the proposed DR algorithm manages to reduce the voltage drop, eliminate the number of events where the voltage drop exceeds the limits and reduce the losses of the distribution system up to 8%-9%, depending on the PV penetration.

关键词： Electric potential Smoothing methods Power quality Europe Voltage Artificial neural networks Transformers Demand response Load modeling Context modeling

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Augmented Yield Analysis for Refined Elementary Mode Set of Penicillin Metabolic Network

Augmented Yield Analysis for Refined Elementary Mode Set of ...

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第33届中国过程控制会议

作者： Chenxi Tao Quan Li Zhonggai Zhao Fei Liu School of Internet of Things Engineering Jiangnan University Key Laboratory of Advanced Control of Light Industry Process (Ministry of Education) Institute of AutomationJiangnan University

Penicillin is usually produced by microbial growth and metabolism in a fermentation *** analysis and understanding of metabolic behavior are the keys to improving penicillin *** refined elementary model set(REMS) is a subset of the Elementary Models(EMs) necessary to describe specific metabolic *** plays an important role in mechanistic modeling and optimization of penicillin fermentation *** conventional yield analysis(YA) method can easily derive REMS using yield information,but the metabolic behavior described by the results may seriously deviate from *** this paper,we propose an augmented yield analysis(AYA) method to address this ***,basic metabolic pathways and metabolites characterizing their activities are identified;second,the production fluxes of the identified metabolites are combined with yield information to form an augmented yield space,into which the EMS is recoded;finally,the REMS is formed by a minimal set of EMs which can meet the preset requirements for metabolic *** REMS of the penicillin metabolic network is determined by AYA,and it can describe the actual metabolic behavior of penicillin,thereby verifying the effectiveness of AYA.

关键词： Penicillin metabolic network elementary mode analysis refined elementary mode set metabolic information augmented yield analysis

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Enhanced Soft Sensor with Qualified Augmented Data Using Centroid Measurement Criterion

Enhanced Soft Sensor with Qualified Augmented Data Using Cen...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Dai, Yun Yu, Qing Yang, Tao Yao, Yuan Liu, Yi 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 Hsinchu30013 Taiwan

ISBN: (纸本)9781665402453

Development of reliable soft sensors using limited labeled samples is not an easy task in industrial processes. A selective generative adversarial network (SGAN)-based support vector regression (SGAN-SVR) soft sensor is proposed for quality prediction using limited labeled training data. Specifically, SVR is considered as a base prediction model. The Wasserstein GAN (WGAN) is adopted to capture the distribution of available labeled data and generate virtual candidates. Subsequently, using a proposed similarity measurement strategy, those synthetic data with more information are selected and introduced into the training set. Using the designed data augmentation approach, the SGAN-SVR model can achieve better prediction performance compared with the SVR soft sensor. The quality prediction results on an industrial polyethylene process demonstrate the effectiveness and advantages of the proposed method. © 2021 IEEE.

关键词： Generative adversarial networks

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Monitoring Wastewater Treatment processes Using a Maximum Likelihood-Based Narx Neural Network

SSRN

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SSRN 2023年

作者： Liu, Wentao Xiong, Weili Sun, Wenxin Chen, Hongtian Key Laboratory of Advanced Process Control for Light Industry Ministry of Education School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

Wastewater treatment processes (WWTPs) contribute significantly to water recycling and energy conservation, because their failures can lead to the potential risk of adverse environmental impacts. WWTPs are acknowledged to be largely nonlinear systems with a series of biological reactors. Additionally, the presence of process uncertainties and measurement noises in WWTPs makes it difficult for the purpose of fault detection. This paper focuses on the data-driven realization of fault detection for sludge bulking and toxic impact in WWTPs. The fault detection model employs NARXbased neural networks to construct the residual generator through the maximum likelihood approach. The maximum likelihood-based NARX neural network can optimize both the model parameters and covariance matrix related to stochastic noises, based on which the model delivers more robustness performance against uncertainties than the existing work. Therefore, the nonlinearity and unknown system uncertainties of WWTPs can be processed simultaneously. The superiority and effectiveness of the maximum likelihood-based NARX neural network are validated through the benchmark simulation model no.1, where the successful detection rates for detecting sludge bulking faults and toxic impact faults are proved to be significantly higher than the traditional methods. © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Observer-Based Distributed Sensor Fault-Tolerant Model Predictive control for Intermittent Faults ⁎

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IFAC-PapersOnLine 2021年第3期54卷 251-256页

作者： Guannan Xiao Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

For distributed control systems with intermittent sensor faults, a distributed fault and state combined observer is designed, and an observer switching mode strategy is proposed to improve the observer convergence performance, then a distributed fault-tolerant model predictive is designed based on the observer. In the proposed observer, the distributed system observer matrices, and state and fault estimation are computed in a totally distributed way with some transition matrices, which can consider the coupled states. The switching mode observer can be more targeted. The two modes: state only estimation, both state and fault estimation are switched due to the absence or presence of the sensor faults. The approach is shown effective through a simulated example.

关键词： Model predictive control distributed control fault-tolerant control fault estimation distributed observer

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Deep Graph Network for process Soft Sensor Development

Deep Graph Network for Process Soft Sensor Development

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Jia, Mingwei Dai, Yun Xu, Danya Yang, Tao Yao, Yuan Liu, Yi 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 Hsinchu30013 Taiwan

ISBN: (纸本)9781665402453

In the (bio)chemical processes, traditional hardware sensors are difficult to directly measure the quality of critical products due to their time-varying, non-linear, and dynamic characteristics. This makes process soft sensor modeling methods important. Since the process variables can be regarded as natural graph data, this work introduces graphs in the soft sensor modeling area. A soft sensor model based on the graph neural network (GNN) is proposed. The model can learn the topological structure of graph data between each unit variable. Moreover, it characterizes variable relationships from the spatial and temporal dimensions to the output prediction by introducing the spatial-temporal convolutional layer. The effectiveness and advantages of the GNN-based soft sensor model are verified using a simulated fermentation process. © 2021 IEEE.

关键词： Graph neural networks

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Consensus Robustness under PI Protocols with Undirected Graphs

Consensus Robustness under PI Protocols with Undirected Grap...

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American control Conference (ACC)

作者： Qi Mao Dan Ma Yanling Ding Hui Peng Jie Chen Department of Electrical Engineering City University of Hong Kong Hong Kong China College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China

This paper concerns robust consensus problems for continuous-time first-order multi-agent systems with respect to uncertain gain and phase of the agent’s frequency response varying within certain ranges. We consider dynamic output feedback control protocol in the form of proportional-integral (PI) control. The agents can in general be nonminimum phase and unstable systems, which are interconnected by an undirected graph network. We seek to determine the largest ranges of gain and phase variations, referred to as the gain consensus margin (GCM) and the phase consensus margin (PCM), respectively, so that consensus can be achieved robustly within these ranges. Our main results consist of explicit analytical expressions of the GCM and PCM, which demonstrate how the agent’s unstable pole and nonminimum phase zero, as well as the network connectivity may fundamentally confine the gain and phase variation ranges so that consensus can or cannot be maintained.

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