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Shaping energy cost management in process industries through clustering and soft sensors

FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Lu, Yu Fang, Gang Huang, Daoping Cai, Baoping Chen, Hongtian Liu, Yiqi South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Guangzhou Peoples R China China Univ Petr Coll Mech & Elect Engn Qingdao Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada

With the ever-increasing growth of energy demand and costs, process monitoring of operational costs is of great importance for process industries. In this light, both financial budget management and local operational optimization supposed to be guaranteed properly. To achieve this goal, a support vector machine recursive feature elimination (SVM-RFE) method together with clustering algorithm was developed to extract features while serving as importance measurements of each input variable for the sequential prediction model construction. Then, the four variants of autoregressive and moving average (ARMA), i.e., ARMA with exogenous input (ARMAX) based on recursive least squares algorithm (RLS), ARMAX based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN) and nonlinear auto-regressive neural network with exogenous input (NARXNN), were applied, respectively, to predict the costs incurred in the daily production for process industries. The methods were validated in the Benchmark Simulation Model No.2-P (BSM2-P) and a practical data set about steel industry energy consumption from an open access database (University of California, Irvine (UCI)), respectively. The nonlinear model, NARXNN, was validated to achieve better performance in terms of mean square error (MSE) and correlation coefficient (R), when used for multi-step prediction of the aforementioned datasets with strong nonlinear and coupled characteristics.

关键词： industry operational costs prediction ARMAX NARNN NARXNN SVM-RFE combined with clustering algorithm

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Approximate Stiffness Element Method for Complex Section Beam Modeling

Approximate Stiffness Element Method for Complex Section Bea...

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2020 International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2020

作者： Huang, Xinshen Li, Hao Zhang, Zhenkun Zheng, Jianguo Pei, Hailong Guangzhou Customs Technology Center Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou China

In this paper, a finite element method of simulating complex section beam element with approximate stiffness element is presented. By establishing an approximate stiffness element, the basic mechanical properties of the element are equivalent to the original complex section beam element's. The approximate stiffness element has several sub-section beam elements, which intersect with other beam elements in space. The problem that missing of intersection points among complex section beam and other beams, which is caused by using conventional beam element, is solved. The error between the approximate stiffness element and the original complex section beam element is analyzed by theoretical deduction, and the feasibility and accuracy of the approximate stiffness element method are verified by the physical test of a bus body. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Stiffness

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A Hierarchical Soft-Sensor Using Spatiotemporal Information Transformation and ARMA With Application in Wastewater Treatment

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Fang, Gang Liu, Yiqi Cai, Baoping Chen, Hongtian Huang, Daoping South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Guangzhou 510640 Peoples R China China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Shandong Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

A large amount of quality-related but difficult-to-measure variables usually make industrial processes unable to be operated safely and efficiently. Nonlinearity, time delay, and dynamics usually add further complexity and difficulties to this issue. To achieve accurate and reliable prediction of such quality-related but difficult-to-measure variables, a novel hierarchical soft sensor, termed spatiotemporal information transformation autoregressive moving average (SIT-ARMA), is proposed in this article to enable single-step and multistep-ahead prediction of these hard-to-measure variables. Inspired by hierarchical ensemble learning, this method applies the spatiotemporal information transformation (SIT) model to model the original dataset and the autoregressive moving average (ARMA) model to model the residuals from the SIT model. Furthermore, to increase the robustness of the SIT-ARMA model, this article uses the median and the median absolute deviation (MAD) to approach the Cauchy distribution, which can identify and smooth the outliers of the prediction derived by the SIT model. In addition, the moving window (MV) approach is applied to the SIT-ARMA model to update the parameters online and improve the adaptive performance of the model. The superiority of the proposed SIT-ARMA method has been verified through two studies of wastewater treatment plants (WWTPs) with different process characteristics.

关键词： Multistep-ahead prediction soft sensor spatiotemporal information transformation (SIT) wastewater treatment

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OD-SLAM: Real-Time Localization and Mapping in Dynamic Environment through Multi-Sensor Fusion 5

OD-SLAM: Real-Time Localization and Mapping in Dynamic Envir...

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5th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xu, Hua Yang, Chenguang Li, Zhijun South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781728164793

Only when the robot knows its position can it execute some specific functions. But in many cases, the robot faces an unknown environment and doesn't know where it is. Visual SLAM system uses the camera on the robot to capture the image of the surrounding environment for positioning. This is a method that relies on external information for positioning. When the external information is dynamic, the pose estimated by the traditional visual SLAM system is inaccurate. So a novel SLAM system is proposed in this paper, which mainly uses a neural network and multi-sensor fusion to realize robot positioning in a dynamic environment. The SLAM system is developed based on ORB-SLAM2. It recognizes highly dynamic objects in the image through the neural network and removes them from the image. Because the neural network can't accurately identify dynamic objects every time, it will affect the pose estimation of the SLAM system. Therefore, the SLAM system proposed in this paper uses odometer to constrain features extracted in the image, so as to obtain static features, and finally estimate a more accurate pose. Finally, the experimental results show that our system has better real-time and accuracy compared with the traditional visual SLAM systems and other dynamic SLAM systems.

关键词： Real time systems

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On Iterative Parameter Identification of FIR systems with Batched Possibly Incorrect Binary-Valued Observations

On Iterative Parameter Identification of FIR Systems with Ba...

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IEEE Conference on Decision and control

作者： Jian Guo Wenchao Xue Ting Wang Ji-Feng Zhang Yanjun Zhang Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing P. R. China Institute of Artificial Intelligence University of Science and Technology Beijing Beijing P. R. China State Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing Institute of Technology Beijing P. R. China

This paper considers the problem of parameter identification for a binary output finite impulse response (FIR) system with measurement error, where the measurement error makes the binary measurement values take opposite values with a certain probability. First, the maximum likelihood estimation (MLE) of the parameters is given and an iterative algorithm with projection based on the Expectation-Maximization algorithm is presented to calculate the MLE. Furthermore, the necessary and sufficient condition for the likelihood function to have a unique maximum point is obtained. It is proved that the iterative estimation error converges to zero at an exponential rate under persistently excitation input conditions. Finally, some numerical simulation results based on a typical system show the effectiveness of the proposed algorithm.

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Observer-based Dynamic Surface control for Robotic Manipulator with Fixed-time Convergence 7

Observer-based Dynamic Surface Control for Robotic Manipulat...

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7th International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2020

作者： Zhu, Chengzhi Zuo, Lei Wang, Ning Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510640 China School of Electronic and Control Engineering Chang'an University Xi'an710064 China School of Information Engineering Chang'an University Xi'an710064 China

ISBN: (纸本)9781728162461

A state feedback dynamic surface tracking control algorithm is proposed for parametric linearized manipulator model in this paper. Different from ordinary finite-time control schemes, fixed-time convergence can be achieved in the proposed algorithm via both of high and low-degree terms in the controller such that the settling time can be preestablished by the designed parameters independent of the system initial conditions. Moreover, the problem of the 'explosion of complexity' and the singularity can be avoided by constructing an auxiliary controller with a first-order filter. The semi-global fixed-time uniform convergence of all the signals in the closed-loop system can be proved by Lyapunov theorem. In order to avoid the employment of acceleration signals and obtain better tracking performance during the procedure, a kind of observer based on uniform exact differentiators was used to compensate the uncertainties online. Finally the superiority of the proposed method has been verified by simulations based on a 2-DOF robot manipulator. © 2020 IEEE.

关键词： State feedback

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Reinforcement Learning on Robot with Variational Auto-Encoder 11th

Reinforcement Learning on Robot with Variational Auto-Encode...

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11th International Conference on Modelling, Identification and control (ICMIC)

作者： Chen, Yiwen Yang, Chenguang Feng, Ying South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England

ISBN: (纸本)9789811504747;9789811504730

Reinforcement learning enables robot to learn plentiful skills through training. The control based on end-to-end reinforcement learning output joint angle to the robot with image as input. However, using the deep network for end-to-end reinforcement learning makes it hard to converge and need much training time. And it's also difficult to expand to other tasks because of custom-designed network. In this paper, we propose a reinforcement learning structure with variational auto-encoder that can be applied to different goals and reduce training time. Firstly the auto-encoder is trained with images that capture random robot actions. Then the reinforcement learning network is trained with the latent space vectors from auto-encoder instead of raw image. After finish training, the robot can reach a state similar to the state in expected image that we input.

关键词： Reinforcement learning Variational auto-encoder Goal

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Effluent quality soft sensor for wastewater treatment plant with ensemble sparse learning-based online next generation reservoir computing

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WATER RESEARCH X 2024年 25卷 100276页

作者： Fang, Gang Huang, Daoping Wu, Zhiying Chen, Yan Li, Yan Liu, Yiqi South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

Real-time monitoring of key quality variables is essential and crucial for stable and safe operations of wastewater treatment plants (WWTPs). Next generation reservoir computing (NG-RC) has recently garnered significant attention in quality prediction, such as COD and BOD, as an effective alternative to traditional reservoir computing (RC), then is able to act as a data-driven soft sensor to twin a hardware sensor for quality variable measurements. Unlike RC, NG-RC does not require random sampling matrices to define the weights of recurrent neural networks and has fewer hyperparameters. However, NG-RC is usually used online but trained offline, thus leading to model degradation under dynamic scenarios. This paper proposes a sparse online NG-RC approach to meet the real-time requirements of WWTPs and mitigate the impact of measurement noise on the model. First, inspired by the Woodbury matrix identity, an incremental strategy is designed, using sequentially arriving data blocks to learn the output weights of NG-RC online. Then, an ensemble sparse strategy is combined to alleviate overfitting issues of the prediction model. Moreover, a soft sensor based on the ensemble sparse online NG-RC is developed to perform real-time prediction of quality indicators in wastewater treatment processes. Finally, two datasets from actual WWTPs are used to validate the effectiveness of the proposed model.

关键词： Soft sensor Effluent variable prediction Next generation reservoir computing Sparse identification Wastewater treatment plants

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Towards stable and efficient nitrogen removal in wastewater treatment processes via an adaptive neural network based sliding mode controller

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WATER RESEARCH X 2024年 24卷 100245页

作者： Liu, Yiqi Zhang, Jing Qiu, Zhuyi Zhang, Yigang Yu, Guangping Ye, Hongtao Cai, Zefan South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Sch Automat Sci & Engn Guangzhou 510640 Peoples R China South China Univ Technol Guangdong Engn Technol Res Ctr Unmanned Aerial Veh Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Guangzhou Inst Ind Intelligence Guangzhou 511458 Peoples R China Guangxi Univ Sci & Technol Sch Automat Liuzhou 545036 Peoples R China ShunDe Polytech Coll Intelligent Manufacture Foshan 528333 Guangdong Peoples R China

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration (SNH) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the SNH with nitrate nitrogen (SNO) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

关键词： Wastewater treatment processes Sliding mode control BSM2 Adaptive neural network

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NeurAR: Neural Uncertainty for autonomous 3D Reconstruction with Implicit Neural Representations

arXiv

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arXiv 2022年

作者： Ran, Yunlong Zeng, Jing He, Shibo Chen, Jiming Li, Lincheng Chen, Yingfeng Lee, Gimhee Ye, Qi The College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Fuxi AI Lab NetEase Inc. Hangzhou China The Department of Computer Science National University of Singapore Singapore

Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality;secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://***/NeurAR/. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

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