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Vision-based adaptive control system for fluidized bed reactors

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Measurement: Journal of the International Measurement Confederation 2025年 255卷

作者： Anweiler, Stanislaw Krok, Marek Kolodziej, Szymon Chacartegui, Ricardo Villanueva, Jose Antonio Becerra Sikora, Malgorzata Wasilewska, Barbara Przysiężniuk, Dawid Faculty of Mechanical Engineering Department of Process and Environmental Engineering Opole University of Technology ul. Prószkowska 76 Opole45-758 Poland Faculty of Electrical Engineering Automatic Control and Informatics Department of Control Science and Engineering Opole University of Technology ul. Prószkowska 76 Opole45-758 Poland Faculty of Mechanical Engineering Department of Vehicles Opole University of Technology ul. Prószkowska 76 Opole45-758 Poland Department of Energy Engineering Escuela Técnica Superior de Ingenieros de Sevilla University of Seville Camino Descubrimientos s/n.- Isla Cartuja Seville41092 Spain Faculty of Mechanical Engineering Koszalin University of Technology Śniadeckich 2 Street Koszalin75-453 Poland Faculty of Production Engineering and Logistics Department of Production Management and Engineering Opole University of Technology ul. Prószkowska 76 Opole45-758 Poland Faculty of Mechanical Engineering Opole University of Technology ul. Prószkowska 76 Opole45-758 Poland

In process engineering, precise, automatic, and dynamic identification of two-phase structures remains a challenging yet essential task, particularly for effectively controlling pneumatic systems handling solid particles. Complex interactions between gas and solid phases and variable flow geometries complicate the precise control of fluidized beds. This study evaluates an adaptive, vision-based flow control system designed for fluidized bed reactors, using real-time image analysis to characterize flow structures. A self-developed air flow valve regulator system, combined with a programmable logic controller (PLC) and dynamic image feedback, enables this innovative control approach. By analyzing grey-level signals within specified regions of interest (ROIs), the system accurately differentiates between critical fluidization regimes: bubbling, plugging, and turbulent flow. The control algorithm dynamically adjusts the flow rate, achieving a mean square error of 6.98 m3/h, underscoring the system's reliability and potential for further optimization. Additionally, safeguards were implemented to prevent material loss at high airflow thresholds, enhancing system stability and safety. The findings demonstrate the potential of adaptive vision-based control for fluidized bed automation, offering an advanced solution for real-time monitoring and precise flow regulation in complex two-phase processes. © 2025 The Author(s)

关键词： Image correlation

Dilemma in All-optical Characterization of Single-layer NiI2 Multiferroics

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arXiv 2023年

作者： Jiang, Yucheng Wu, Yangliu Zhang, Jinlei Wei, Jingxuan Peng, Bo Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China 8 Somapah Road Singapore487372 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The search for two-dimensional multiferroic materials is an exciting yet challenging endeavor. Recently, Song et al. reported the exciting discovery of type-II multiferroic order in an antiferromagnetic (AFM) NiI2 single layer, and concluded the ferroelectric (FE) polarization induced by a helical magnetic order1. Their finding was presented from all-optical experimental evidence, based on the methods of second harmonic generation (SHG) and linear dichroism (LD). However, the all-optical characterizations alone cannot serve as unanimous evidence of the "FE" existence, particularly in conjunction with magnetic orders in a single-layer multiferroic. A main argument is that their observed SHG and LD signals may just be manifested from the magnetic-order induced breaking of spatial-inversion or time-reversal symmetry instead of FE order, and thus cannot endorse the finding of a single-layer multiferroic. Accurate measurements of magnetic and electric orders are very essential to make a reliable judgement of a multiferroic. With the device’s thickness approaching atomic scale, its magnetic or electrical signals will become too weak to be collected by traditional measurements2. Therefore, advanced all-optical probing methods have been © 2023, CC BY-NC-SA.

关键词： Harmonic generation

Distributed formation control of networked mechanical systems

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arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

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Application of Deep Learning Method to Estimate Bottomhole P...

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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Policy Evaluation in Distributional LQR

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arXiv 2023年

作者： Wang, Zifan Gao, Yulong Wang, Siyi Zavlanos, Michael M. Abate, Alessandro Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology Digital Futures StockholmSE-10044 Sweden Department of Computer Science University of Oxford OxfordOX13QD United Kingdom The School of Computation Information and Technology Technical University of Munich Munich80333 Germany The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard reinforcement learning. Meanwhile, a challenge in DRL is that the policy evaluation typically relies on the representation of the return distribution, which needs to be carefully designed. In this paper, we address this challenge for the special class of DRL problems that rely on a discounted linear quadratic regulator (LQR), which we call distributional LQR. Specifically, we provide a closed-form expression for the distribution of the random return, which is applicable for all types of exogenous disturbance as long as it is independent and identically distributed (i.i.d.). We show that the variance of the random return is bounded if the fourth moment of the exogenous disturbance is bounded. Furthermore, we investigate the sensitivity of the return distribution to model perturbations. While the proposed exact return distribution consists of infinitely many random variables, we show that this distribution can be well approximated by a finite number of random variables. The associated approximation error can be analytically bounded under mild assumptions. When the model is unknown, we propose a model-free approach for estimating the return distribution, supported by sample complexity guarantees. Finally, we extend our approach to partially observable linear systems. Numerical experiments are provided to illustrate the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

A 3D Modeling Method for Scattering on Rough Surfaces at the Terahertz Band

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arXiv 2023年

作者： Chen, Ben Guan, Ke He, Danping Xie, Pengxiang Zhong, Zhangdui Dou, Jianwu Mumtaz, Shahid Bazzi, Wael State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China School of Engineering Nottingham Trent University United Kingdom Electrical and Computer Engineering Department American University in Dubai Dubai United Arab Emirates

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

Solar PV Power Forecasting with a Hybrid LSTM-AdaBoost Ensemble

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Solar PV Power Forecasting with a Hybrid LSTM-AdaBoost Ensem...

Utility Conference and Exposition (IUCE), IEEE/IET International

作者： Frimpong Kyeremeh Fang Zhi Yang Yi Eric Gyamfi Isaac Kofi Nti Nanjing Tech University Nanjing China Electrical/Electronic Engineering Department Sunyani Technical University Ghana College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Computer Science University College Dublin Dublin Ireland School of Information Technology University of Cincinnati OH USA

Smart grids aim at achieving unprecedented flexibility in energy management and a resilient quality of supply. However, the inclusion of variable renewable energy (RE) generation units makes the realization of a resilient supply a challenging one. One of the solutions to the achievement of the goal is the ability to predict or forecast power generation as accurately as possible from the various RE in the grid. Despite extensive research on the subject, RE generation forecasting still remains a challenge, and research is ongoing to achieve a near-perfect and efficient prediction. Deep Neural Network (DNN) algorithms have performed efficiently in areas like speech recognition, image classification, as well as forecasting tasks, such as economic time series, but have been sparsely applied in renewable energy power forecasting. This paper proposes a hybrid long short term memory (LSTM)-Adaboost ensemble method for solar power generation forecasting. It also does a comparative study of different LSTM configurations tested on solar PV data from Germany. In particular, the work in this paper looks at how well the LSTM-AdaBoost ensemble model predicts solar power compared to machine learning methods that do not use ensembles.

关键词： Deep learning Renewable energy sources Neural networks Time series analysis Speech recognition Hybrid power systems Ensemble learning

Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction