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Two-tier control structure design methodology applied to heat exchanger networks

Chinese Journal of Chemical engineering 2022年第7期35卷 231-244页

作者： Siwen Gu Lei Zhang Yu Zhuang Weida Li Jian Du Cheng Shao Institute of Chemical Process Systems Engineering School of Chemical EngineeringDalian University of TechnologyDalian 116024China Jiangsu Collaborative Innovation Center for Cultural Creativity School of Photoelectric EngineeringChangzhou Institute of TechnologyChangzhou 213032China Institute of Advanced Control Technology School of Control Science and EngineeringDalian University of TechnologyDalian 116024China

Because of its paramount importance in the successful industrial control strategy of a given heat exchanger network(HEN),the control structure designs for providing appropriate manipulated variable(MV)and controlled variable pairings have received considerable ***,quite frequently HENs with such control structures face the problem of hard constraints,typically holding the HENs at less controlled operating *** both the MV pairings and the above control pairings should be considered to design a control *** paper investigates the systematic incorporation of the two pairings,and presents a methodology for designing such two-tier control *** is developed based on the sequential strategy,coupling an indirect-tier with direct-tier control structure design,wherein the intention is realized in the former stage and the latter is implemented for further *** MV identification and pairing are achieved through variations in heat load of heat exchangers to design the indirect-tier control *** the direct-tier control structure is followed the relative gain array pairing *** the proposed methodology,on the one hand,it generates an explicit connection between the MV pairings and the HEN configuration,and the quantitative interaction measure is improved to avoid the multiple solutions to break the relationship among all the control pairings into individuals;on the other hand,a two-tier control structure reveals control potentials and control system design requirements,this may avoid complex and economically unfavourable control and HEN *** application of proposed framework is illustrated with two cases involving the dynamic simulation analysis,the quantitative assessment and the random test.

关键词： Heat exchanger networks(HENs) controllability Two-tier control structure Manipulated variable pairing control pairing

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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43rd Chinese control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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Multi-objective optimization design of high-speed elevator car based on aerodynamic characteristic surrogate model 4

Multi-objective optimization design of high-speed elevator c...

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4th International Conference on Mechanical engineering, Intelligent Manufacturing, and Automation Technology, MEMAT 2023

作者： Su, Wanbin Li, Ning Jiang, Yefeng Pan, Yijun Chai, Min Zheng, Shuihua Jiaxing Special Equipment Inspection and Testing Institute Jiaxing314050 China Institute of Process Equipment and Control Engineering College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9781510674820

With the increase of elevator operating speed, it is greatly affected by air disturbance, which leads to the increase of elevator cost and energy consumption, and has a huge impact on the safety, reliability and comfort of elevator operation. To improve the aerodynamic characteristics of the cabin flow field, this paper parameterizes the flow guide hoods with a structure similar to an ellipse, selecting eight design parameters. Taking the aerodynamic drag coefficient and the yawing moment coefficient as the optimization objectives, four surrogate models were constructed by using the optimal Latin hypercube design sample points and the prediction accuracy was compared. Finally, the EBF surrogate model with higher fitting accuracy was selected, and combined with the NSGA-II optimization algorithm to carry out multi-objective optimization of the flow guide hood, obtaining a series of Pareto solutions, and finally comparing and analyzing the aerodynamic performance of the flow guide hood before and after optimization. Finally, on the basis of the optimized flow guide hood, the aerodynamic characteristics of the cabin under actual operating conditions were analyzed. The results show that the installation of the flow guide hood can reduce the drag coefficient of the cabin;within a certain range, increasing the total height of the flow guide hood, the vertex offset and the inertia of the flow guide hood in the X direction can improve the aerodynamic performance of the ultra-high speed elevator system. The running scenarios such as start acceleration, smooth running, deceleration and stop will affect the aerodynamic performance of the cabin, and this study provides a reference for the optimization design of the flow guide hood and the operating conditions of the elevator. © 2024 SPIE.

关键词： Multiobjective optimization

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Hemorrhage Detection by Magnetic Induction Tomography with Helmholtz Coil 42

Hemorrhage Detection by Magnetic Induction Tomography with H...

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42nd Chinese control Conference, CCC 2023

作者： Zhu, Zilong Tan, Chao Chen, Yixuan Dong, Feng School of Electrical and Information Engineering Tianjin Key Laboratory of Process Measurement and Control Tianjin University Tianjin300072 China

ISBN: (纸本)9789887581543

Due to the nonradioactive, noninvasive, low cost and portable features, magnetic induction tomography (MIT) is a promising technology in medical imaging. The conventional MIT sensor array generates divergent excitation magnetic field, resulting in poor sensitivity intensity and uniformity. To improve the intensity and uniformity of the sensitivity in MIT for hemorrhage detection, a local MIT method with Helmholtz coil is proposed. The Helmholtz coil provides a uniform background magnetic field in the measured region. To obtain sufficient independent measurements for hemorrhage reconstruction, the open planar MIT sensor array with gradiometers is applied in combination with the Helmholtz coil. The structure and excitation-detection strategy of the MIT sensor array are designed. The distribution of the magnetic field, phase sensitivity and the phase response of hemorrhages under the designed MIT sensor array are analyzed. The results indicate that the open planar MIT sensor array with Helmholtz coil can effectively improve the intensity and uniformity of the phase sensitivity. In addition, it can also enhance the intensity of the hemorrhage signals for different depths and volumes of the hemorrhages. The proposed method provides a solution to improve the detection accuracy and the detection depth of the hemorrhage by MIT. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Magnetic fields

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A Multi-Gas Detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Air quality

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RIS-assisted Channel Prediction Model for Mine V2X Systems 5

RIS-assisted Channel Prediction Model for Mine V2X Systems

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Cheng, Yuanxun Hu, Qingsong Luo, Yujia Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering Xuzhou China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

ISBN: (纸本)9798331533991

The complex and dynamic environment of mines leads to time-variability and uncertainty in channel propagation, which affects the stability and reliability of Vehicle-to-Everything (V2X)communication in mine networks. Channel prediction can help mitigate these issues. Introducing reconfigurable intelligence surface (RIS) into the mine V2X system can enhance signal coverage and quality in the complex mine environment. To address the issue of long-term error accumulation in existing channel prediction models, this paper analyzes the prediction error of channel state information (CSI) in mine V2X systems. It was observed that the error distribution exhibits characteristics of an approximate normal distribution, revealing its predictability. Utilizing this characteristic, this paper proposes an error correction strategy to suppress long-term error accumulation during the prediction process. Furthermore, an adaptive method for optimizing RIS phases is designed based on the Snow Ablation Optimizer. Specifically, this paper utilizes a metaheuristic optimization algorithm and deep learning techniques to propose a mine V2X channel prediction model that incorporates the error correction strategy. Experimental results show that optimizing the RIS phase can effectively enhance channel gain, and the mine V2X channel prediction model incorporating the error correction strategy can effectively suppress cumulative errors during the prediction process, thereby improving prediction accuracy. © 2024 IEEE.

关键词： Normal distribution

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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Model Predictive control Based Reference Generation for Optimal Proportional Integral Derivative control 32

Model Predictive Control Based Reference Generation for Opti...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gashi, Fatos Abuibaid, Khalil Ruskowski, Martin Wagner, Achim KaiserslauternD-67663 Germany RPTU Kaiserslautern-Landau Machine Tools and Control Systems Department of Mechanical and Process Engineering KaiserslauternD-67663 Germany

ISBN: (纸本)9798350395440

We introduce an alternative approach towards optimal proportional integral derivative (PID) control, consisting of model predictive control (MPC) based reference generation. To this end, we have integrated the reference as part of optimization variables of the resulting problem, where a deliberate sequence of errors is induced to obtain an optimal PID control action. In addition, the desired behavior of the PID controller is achieved without the need for internal modification of the PID gains. To better highlight the ability of coping with poor PID tuning, several test cases consisting of progressively degraded PID gains are presented. Validation of the proposed strategy is displayed by comprehensive simulations using two different plants. © 2024 IEEE.

关键词： Model predictive control

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Monocular 3D Object Detection Based on Pseudo-LiDAR Point Cloud for Autonomous Vehicles 41

Monocular 3D Object Detection Based on Pseudo-LiDAR Point Cl...

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第41届中国控制会议

作者： Yijing Wang Sheng Xu Zhiqiang Zuo Zheng Li Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Pseudo-LiDAR point clouds are generated from monocular *** with the point cloud from LiDAR,it can provide denser *** to the inaccuracy of depth estimation,there is still a performance gap between the pseudo-LiDAR point cloud and the LiDAR *** this paper,we propose an approach to eliminate the performance degradation caused by deviation of depth estimation and realize 3 D object detection based on pseudo-LiDAR point *** results on KITTI 3 D benchmark illustrate that in contrast to other methods,our scheme can achieve more reliable performance on both object localization and shape estimation.

关键词： 3D object detection pseudo-LiDAR point cloud image morphing depth estimation

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Learning to traverse challenging terrain using BGRU-Attention

Learning to traverse challenging terrain using BGRU-Attentio...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Liu, Huazheng Xu, Yanbin Dong, Jiajun Dong, Feng Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China

ISBN: (纸本)9798350325706

In this paper, we present a model-free deep reinforcement learning (DRL) algorithm to control the locomotion of a quadruped robot adaptively at challenging terrain. This approach is used to improve the robot's ability to traverse unstructured ground. Combining Bidirectional Gate Recurrent Unit (BGRU) and attention mechanism, we train the quadruped robot to climb up and down stairs, traverse various field terrain and bricks. We train the controller in simulation using knowledge distillation, and deploy on real robots without any adjustment. In the simulation, the performance of our Student Module even exceeds that of Teacher Module. The experimental results demonstrate that our proposed approach can make the quadruped robot traverse obstacles swiftly. © 2023 IEEE.

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