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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Julius Lorenz Anselm Klose Hannes Lange Tobias Kock Leon Urbas Chair of Process Control Systems TUD Dresden University of Technology Dresden Germany Process to Order Lab TUD Dresden University of Technology Dresden Germany Process Systems Engineering Group TUD Dresden University of Technology Dresden Germany

Large-scale water electrolysis systems are built from a multitude of stack units. These stack units have to be controlled during operation. The question arises how process control of the system can be designed in that way, that scalable, extendable but also optimal operation of the stack units becomes possible. To achieve this goal, existing process control strategies from literature are analyzed. The requirements resulting from the inherent numbering up of stack units are described and an approach for an adaptable optimized process control strategy considering the system properties is proposed. The proposed process control strategy is implemented in a modeled electrolysis system and evaluated regarding the stated criteria. The results could show, that the inherent modularization of electrolysis systems can be used in advantage for a scalable operation.

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Evaluating the moderated-mediation effects of switchingcosts in the link between social capital and NPD performance

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Journal of Management Science and engineering 2024年第2期9卷 161-176页

作者： Yi Li Nelson Oly Ndubisi Gang Li Jinpeng Xu School of Economics&Management Xidian UniversityXi'anChina College of Business&Economics Qatar UniversityDohaQatar School of Management Xi'an Jiao Tong UniversityXi'anChina The State Key Lab for Manufacturing Systems Engineering Xi'anChina The Key Laboratory of the Ministry of Education for Process Control and Efficiency Projects Xi'anChina

In this study,we explore the causes and performance outcomes of switching costs in the context of new product development(NPD)from both the supplier and customer perspectives,and discuss the role that switching costs play as moderators and mediators in the relationship between social capital and NPD *** on data from 214 Chinese manufacturers,we employ the structural equation model to test our conceptual model and *** results indicate that relationship quality and customer involvement positively and negatively affect switching costs,respectively,and that switching costs negatively affect NPD *** costs mediate and moderate the relationship between social capital and NPD ***,switching costs significantly and positively moderate the relationship between relationship quality and NPD *** the other hand,switching costs insignificantly and negatively moderate the relationship between customer involvement and NPD *** theoretical and managerial implications of the findings are discussed.

关键词： Social capital New product development(NPD) Performance Switching costs Moderated mediation analysis China

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

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

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第43届中国控制会议

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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision trees

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

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

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第43届中国控制会议

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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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Optimization of Rice Bran Protein Extraction Using Choline Chloride-Glycerol Deep Eutectic Solvent Using Response Surface Methodology (RSM) 2nd

Optimization of Rice Bran Protein Extraction Using Choline C...

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2nd International Conference on Biomass Utilization and Sustainable Energy, ICoBiomasSE 2022

作者： Kamal Ramlee, Kamalea Amaleena Farhana Muhammad Nor, Intan Nurfazliyana Md Sarip, Mohd Sharizan Mohd Zainudin, Mohd Asraf Abu Bakar, Amirul Ridzuan Nawawi, Mohd Azizi Nik Daud, Nik Muhammad Azhar UniCITI Alam Campus Sungai Chuchuh Perlis Padang Besar02100 Malaysia Kompleks Pusat Pengajian Jejawi 3 Perlis Arau02600 Malaysia Division of Process Control and Process Systems Engineering Department of Chemical Engineering Kyoto University Kyotodaigau-Katsura Nishikyo-ku Kyoto-shi Kyoto615-8510 Japan Faculty of Applied Sciences Universiti Teknologi MARA Shah Alam40450 Malaysia Kompleks Pusat Pengajian Jejawi 3 Perlis Arau02600 Malaysia

ISBN: (纸本)9789819916948

A by-product of the rice milling process, rice bran contains four different types of protein: albumin, globulin, prolamin, and glutelin. These proteins are widely known for being hypoallergenic and having nutritional benefits. In order to increase the value of rice bran, the protein was extracted via deep eutectic solvent (DES). Response Surface Methodology (RSM) was used to assess the impact of three operational conditions, including temperature, extraction duration, and the ratio of rice bran to DES, on the yield of rice bran protein after the precipitates were converted into powder form by freeze drying. At optimal working conditions, which were 60 °C, 2 h, and a 1:9 ratio of rice bran to DES, the method’s results showed that the highest extracted protein of rice bran was 16.254%. Several techniques, including Fourier transform infrared spectroscopy (FTIR) and Kjeldahl studies, have been used to demonstrate the presence of protein in rice bran powder. In the rice bran protein (RBP), amine (1640.70 cm−1), alcohol (3229.74 cm−1), and alkane (2925.37 cm−1) were all detected using FTIR analysis. Furthermore, Kjeldahl analysis revealed that 15.61% of the rice bran powder’s protein content is present. In conclusion, rice bran’s value as a functional meal can be increased by adding protein through the use of a deep eutectic solvent called green solvent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Proteins

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A Multi-Gas Detection System Based on Convolutional Neural Network

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

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第43届中国控制会议

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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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（CO）,carbon monoxide（CO）, nitric oxide（NO）, sulfur dioxide（SO）, and propane（CH）. 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.

关键词： Non-Dispersive Infrared Multi-gas Detection System Pattern Recognition Convolutional Neural Network

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Electrodeposition of Magnesium in Deep Eutectic Solvents Based on Magnesium Chloride Hexahydrate and Choline Chloride with Urea Added 3

Electrodeposition of Magnesium in Deep Eutectic Solvents Bas...

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2023 3rd International Conference on Frontiers of Electrical Power and Energy systems, EPES 2023

作者： Chen, Qing Wang, Qian Lu, Shenghua Ecological and Environmental Protection Company China South-to-North Water Diversion Corporation Limited Beijing100036 China Key Laboratory of Cluster Science Ministry of Education of China Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials School of Chemistry Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Ionic Liquids Clean Process Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China Investment Company China Construction Fourth Engineering Division Corp. Ltd. Guangzhou510665 China

Magnesium (Mg) alloy has small density, large elastic modulus, good heat dissipation and corrosion resistance to organic matter and alkali. At present, magnesium alloy is more and more used in automotive industry, medical devices and aerospace industry. However, the traditional preparation method of Mg has the disadvantages of high investment, high labor intensity and great environmental pollution. Therefore, it is of great significance to develop simple, environment-friendly methods of the magnesium. In this study, urea was added to adjust the electrochemical property of the deep eutectic solvent (DES) mixed by choline chloride (ChCl) and magnesium chloride hexahydrate (MgCl2·6H2O). Cyclic Voltammetry (CV) curves reveals that the addition of urea made the reduction potential of Mg shifted from -0.9 V to -1.3 V. Among the CV curves, one was proposed as the "dividing line", which shows that the electroactive species in the two DESs, ChCl-MgCl2·6H2O and urea-MgCl2·6H2O, are different due to the changes of the component of the DESs. Fourier transform infrared (FTIR) data shows the type of hydrogen bond had been changed with the increase of urea content. Furthermore, the Raman spectra indicates that Mg2+ was coordinated with urea chains, which did not exist in ChCl-MgCl2·6H2O. Moreover, it was found that urea changed the electrochemical performance of the ChCl-Urea-MgCl2·6H2O by changing the hydrogen bond in the system and coordination form of the electroactive species, rather than adsorbing onto the electrode surface. Combined with geometry calculations at the B3LYP/6-311++G (d, p), the most probable mechanism of electrodeposition process was deduced. © Published under licence by IOP Publishing Ltd.

关键词： Cyclic voltammetry

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Experimental and theoretical insights into copper phthalocyanine-based covalent organic frameworks for highly efficien radioactive iodine capture

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Chinese Chemical Letters 2022年第7期33卷 3549-3555页

作者： Xuewei Liu Anrui Zhang Ran Ma Bo Wu Tao Wen Yuejie Ai Mingtai Sun Jie Jin Suhua Wang Xiangke Wang MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environment and Chemical EngineeringNorth China Electric Power UniversityBeijing 102206China Guangdong Provincial Key Laboratory of Petrochemical Pollution Process and Control School of Environmental Science and EngineeringGuangdong University of Petrochemical TechnologyMaoming 525000China

Exploring efficient materials for capturing radioactive iodine in nuclear waste is of great significance for the progress of nuclear energy as well as the protection of ecological *** organic frameworks(COFs)have emerged as promising adsorbents because of their predesignable and functionalizable skeleton ***,it remains a grand challenge to achieve large scale preparation of *** this work,we developed a mild and efficient microwave irradiation method instead of the traditional solvothermal method to prepare copper phthalocyanine-based covalent organic frameworks(Cu_(x)Pc-COFs)within only 15 *** nitrogen-rich 1,2,4,5-tetracarbonitrilebenzene(TCNB)was selected as the solely organic ligand to construct copper phthalocyanine-based 2D conjugated *** resultant Cu_(x)Pc-COFs exhibited excellent iodine enrichment with 2.99 g/g for volatile iodine and 492.27 mg/g for iodine-cyclohexane solution,respectively,outperforming that of many porous *** indicated by spectroscopic analysis and DFT calculations,this impressive adsorption performance can be attributed to the charge transfer arising from nitrogen-rich phthalocyanine structures and electron-richπ-conjugated systems with iodine ***,the strong electrostatic interaction between Cu(Ⅱ)on chelate centers and polyiodide anions(I_(x)^(-))also play an important role in the firmly trapping radioactive ***,this study provides a facile and intelligent approach to implement metal-based COFs for the remediation of toxic radioactive iodine.

关键词： Cu_(x)Pc-COFs Microwave Iodine adsorption Charge transfer Electrostatic interaction

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Robust MPC with Support Vector Clustering-based Parametric Uncertainty Set for Building Thermal control

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IFAC-PapersOnLine 2024年第18期58卷 159-164页

作者： Ilham Naharudinsyah Rene Delfos Tamas Keviczky Process and Energy Department Faculty of Mechanical Engineering Delft University of Technology Delft 2628 CN Netherlands Process and Energy Department Faculty of Mechanical Engineering Delft University of Technology Delft 2628 CN Netherlands Delft Center of Systems and Control Faculty of Mechanical Engineering Delft University of Technology Delft 2628 CD Netherlands

control systems are essential to support the use of building structures as short-term thermal energy storage (TES). Due to modeling and forecast imperfections, the controller must be able to deal with uncertainties. This paper proposes a robust model predictive controller (MPC) with a new uncertainty set construction technique to regulate the heat supply in a building envelope. We extend the Support Vector Clustering-based set construction technique to estimate modeling and forecast uncertainty sets. Subsequently, we integrate the sets into a Min-Max MPC framework to ensure robust feasibility by tightening the constraints. The resulting controller successfully deals with modeling and forecast uncertainties. The quality of the presented framework is compared with a nominal MPC and a robust MPC with different uncertainty set estimates. On the basis of a numerical simulation, we demonstrate that the proposed controller successfully maintains the room temperature within the comfort limits. The result also shows that our MPC is less conservative than the controller designed using a box-shaped non-falsifed parametric uncertainty set.

关键词： Support vector clustering Set-membership estimation Parametric uncertainty Robust model predictive control Thermal energy storage Building energy systems

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