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2022 Chinese Automation Congress, CAC 2022

作者： Chen, Hengfei Liu, Xianda Sheng, Chuan Pan, Hao College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher. © 2022 IEEE.

关键词： Network security

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Tunable and Functional Ionic Liquids Embedded Hyper-Crosslinked Polymers as H2o-Tolerant Adsorbents for Highly Efficient Gaseous Toluene Capture

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SSRN 2024年

作者： Nie, Xiongfei Qiu, Xinjie Zhang, Ruina Ke, Quanli Liu, Huayan Zhang, Xiangping Lu, Hanfeng Cui, Guokai Zhang, Suojiang Innovation Team of Air Pollution Control Institute of Catalytic Reaction Engineering State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology College of Chemical Engineering Zhejiang University of Technology Hangzhou310014 China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

Developing advanced sorbents for efficient aromatic VOCs sorption from humid conditions are highly desired. In this work, a series of tunable and functional ionic liquids embedded hyper-crosslinked polymers (ILHCPs) containing different structures of anion-functional ionic liquids (ILs) with 1,4-dichloroxylene (DCX) as the crosslinker were designed and prepared for highly efficient gaseous toluene adsorption under humidity. These ILHCPs could achieve high surface area (600~900 m2 g−1), high micropore volume (0.1~0.3 cm3 g−1), small average pore size (4~5 nm), and large water contact angle (up to 110°) through tuning the anion structures and the initial IL:DCX molar ratio. Gaseous toluene adsorption isotherms on typical ILHCPs were tested under 298, 308, and 318 K, and a dual-site Langmuir-Freundlich (DSLF) model was used for well describing adsorption isotherms, which can be divided into two contributions for site I and site II. The isosteric heats of gaseous toluene adsorption were calculated. Furthermore, toluene adsorption under different humidity conditions were also tested. Compared with DCX-based self-condensed HCP, toluene adsorption capacity of ILHCPs remained steadily (270 mg g−1) even under 80% humidity. To our best knowledge, this is the first example of tuning functional ILHCPs for efficient H2O-tolerant gaseous toluene adsorption. The method may also open a door to achieving high adsorption efficiency of other volatile organic compounds by functional IL-based porous materials. © 2024, The Authors. All rights reserved.

关键词： Gas adsorption

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Batch-to-Batch Optimization with Model Adaptation Leveraging Gaussian Processes: The Case of Optogenetically Assisted Microbial Consortia

Batch-to-Batch Optimization with Model Adaptation Leveraging...

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International Conference on control, Automation and systems ( ICCAS)

作者： Sebastián Espinel-Ríos Rudolph Kok Steffen Klamt José L. Avalos Rolf Findeisen Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Chemical and Biological Engineering Princeton University Princeton United States Institute for Automation Engineering Otto von Guericke University Magdeburg Magdeburg Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Microbial consortia are promising biotechnological production systems with the potential to divide complex metabolic pathways into smaller submodules, as well as make products and consume substrates that monocultures cannot. Maintaining optimal cell population levels and preventing mono culture formation challenge bioproduction by microbial consortia. Optogenetics allows for regulating the expression of key growth-regulatory genes through light to modulate cell population levels. Model-based dynamic optimization can determine optimal light trajectories and inoculum sizes that maximize product synthesis while satisfying system constraints, e.g., safety, economic, or technical aspects. Further-more, closed-loop dynamic optimization can address system uncertainty to a certain extent; however, its implementation is challenging due to limited online sensors. Alternatively, here we propose to perform open-loop optimization with batch-to-batch model adaptation based on Gaussian processes for maximizing bioproduction by optogenetically assisted consortia. The proposed approach enables knowledge transfer from existing to new models, improving predictability and optimization performance in each batch while avoiding costly and time-consuming modeling experiments. Compared to closed-loop optimization, this strategy is easier to implement as it does not rely on online monitoring, contributing to the state of the art in optimizing bioproduction by microbial consortia. We outline the applicability of the approach using simulation experiments of an optogenetically assisted consortium for the biosynthesis of the flavonoid naringenin, considering both parameter and model structure uncertainty.

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Recent advances in catalytic filters for integrated removal of dust and NOx from flue gas:fundamentals and applications

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Resources chemicals and Materials 2022年第3期1卷 275-289页

作者： Changming Li Lin Huangfu Jianling Li Shiqiu Gao Guangwen Xu Jian Yu State Environmental Protection Key Laboratory of Food Chain Pollution Control School of Ecology and EnvironmentBeijing Technology and Business UniversityBeijing 100048China. State Key Laboratory of Vanadium and Titanium Resources Comprehensive Utilization Pangang Group Research Institute Co.LtdPanzhihua 617000China State Key Laboratory of Multi-phase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China. School of Chemical Engineering Shenyang University of Chemical TechnologyShenyang 110142LiaoningChina

Catalytic filters including catalytic bag filters and catalytic filter candles, which couple the filters with denitrification catalysts to obtain the ability to simultaneously remove SOx, NOx and dust, have become the promising applied technology for the integrated flue gas treatment because of their huge advantages in reducing the initial investment, floor occupancy and maintenance cost. In this review, we will summarize the recent advances in the development of catalytic filters in terms of their process principles, filter material, denitrification catalysts, structure-function relationships and industrial applications. Moreover, suggestions about the current challenges and future opportunities are also given from the viewpoints of catalysts and filter material design, catalytic filter preparation methods, and their poisoning and regeneration, etc. With the further development of theory and engineering research, the extensive industrial application of catalytic filters in the field of multiple pollutants flue gas treatment is highly anticipated in the future.

关键词： Catalytic filter candles Catalytic bag filters Integrated flue gas treatment Industrial application

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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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Development of CAE and CAD Sy1stem for Six-Axis Manipulator control System

Development of CAE and CAD Sy1stem for Six-Axis Manipulator ...

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Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Hao-Jyun Jhang Chih-Cheng Chen Wun-Jie Zheng Chiu-Hung Chen Cheng-Fu Yang Wen-Yuan Yu Department of Automatic Control Engineering Feng Chia University Taichung Taiwan School of Ocean Information Engineering Jimei University Xiamen China Department of Chemical and Materials Engineering Feng Chia University Taichung Taiwan Artificial intelligence of intelligent manufacturing systems (AIMS) Research Center National Tsing Hua University Hsinchu Taiwan

Automated mechanical equipment is adjusted as the production line changes with the manipulator. Through real-time equipment monitoring and management, it is ensured that the equipment runs and adjust at any time. Modern equipment monitoring and control are used to accurately judge equipment usage status with the help of sensors, big data, the Internet, and artificial intelligence. The computer-aided design (CAD) is used to establish a six-axis manipulator including the dimensions of each axis, the speed, and acceleration of the motor, and kinematics. All the data is collected in one archive to create a 3D model and simulate and analyze its performance with the computer-aided engineering (CAE) method.

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Key Parameters Prediction for Intelligent control and Energy Optimization Based on MIC-BWO-XGBoost 2

Key Parameters Prediction for Intelligent Control and Energy...

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2nd International Conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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Health assessment method of industrial robot reducer based on deep belief network 11

Health assessment method of industrial robot reducer based o...

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11th IEEE Annual International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2021

作者： Ji, Chengcheng Wang, Kai Yuan, Decheng Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China

ISBN: (纸本)9781665425278

Industrial robots are the most representative equipment in smart manufacturing system. Reducers, which are one of the key components of industrial robots, account for a significant portion of failures in industrial robots. It is thus important to evaluate health status of reducers during the operation of industrial robots. A deep-level probability directed graph model-Deep Belief Network (DBN) is used to assess health status for industrial robot reducer in this paper. First, in the pre-training stage of the deep belief network, the weights closer to the optimal are trained layer by layer through the Restricted Boltzmann Machine (RBM) from bottom to top. Secondly, in the fine-tuning stage of the deep belief network, the weights are tuned through the backpropagation algorithm. Finally, the deep belief network model is verified through experiments, which shows that the DBN has a high diagnosis accuracy for health assessment of industrial robot reducer. © 2021 IEEE.

关键词： Industrial robots

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Challenges and opportunities for the control of energy storage systems. a focus on the zinc-air batteries

Challenges and opportunities for the control of energy stora...

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2021 IEEE AFRICON, AFRICON 2021

作者： Olaru, Sorin Stoican, Florin Kheawhom, Soorathep University Paris-Saclay CentraleSupélec Laboratory of Signals and Systems Gif sur Yvette France University Politehnica of Bucharest Faculty of Automatic Control and Computers Bucharest060042 Romania Chulalongkorn University Faculty of Engineering Dpt. Chemical Engineering Bangkok Thailand

ISBN: (纸本)9781665419840

The paper aims to review recent developments and points to the challenges and opportunities for the instrumentation, control and management technologies in relationship with the emergence of novel Energy Storage systems. It exemplifies this trend with a technology that has received an increasing interest in recent studies, the Zinc-Air batteries. © 2021 IEEE.

关键词： Zinc

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Degradation of prometryn during UV/persulfate process: Kinetics and disinfection by-product formation

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Journal of Water Process engineering 2024年 60卷

作者： Hu, Chen-Yan Liu, Hao Xu, Ling Lin, Yi-Li Wang, Qiang-Bing Huang, Dan-Dan Wu, Yi-Hui Dong, Zi-Yi Ji, Sheng-Jie College of Environmental and Chemical Engineering Shanghai Engineering Research Center of Energy- Saving in Heat Exchange Systems Shanghai University of Electric Power Shanghai 200090 China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092 China Department of Safety Health and Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung 824 Taiwan

Prometryn is one of the commonly used triazine herbicides and is also widely used for algae removal in aquaculture. This study investigated the degradation kinetics of prometryn via UV/persulfate (PS) oxidation and confirmed its adherence to pseudo-first-order kinetics, with a calculated rate constant (k_obs) of 0.2358 min⁻¹. The impact of PS concentration, water matrix (including background ions of Cl⁻ and Br⁻ and natural organic matter (NOM)), and solution pH on prometryn degradation were assessed. Results indicated an increase in prometryn degradation with raising concentrations of PS, Cl⁻, and Br⁻, especially evident at a PS concentration of 400 μm where complete degradation was achieved. Conversely, degradation decreased with increasing NOM concentration and pH. Radical scavenging experiments affirmed the promotion of prometryn degradation in UV/PS by both HO• and SO₄⁻•, with SO₄⁻• contributing over 70 % of the total free radical contribution. Evaluation of disinfection by-product (DBP) formation revealed trichloromethane (TCM) as the predominant DBP in non‑bromine water and tribromomethane (TBM) in bromine water. Thus, this study offers a theoretical foundation for UV/PS degradation of triazine herbicides in aqueous environments. The obtained results hold significance for controlling DBPs in subsequent water treatment processes, ensuring the provision of safe drinking water to the public and meeting the sustainable development goal 6. © 2024 Elsevier Ltd

关键词： Contribution of radicals Disinfection by-products Kinetics Prometryn UV/persulfate

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