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检索条件"任意字段=Symposium on Dynamics and Control of Process Systems"

共 1794 条记录，以下是81-90 订阅

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Utilise Statistical Analysis to Develop an Efficient Parameter Estimation Retuning App for the Direct Torque control of AC Induction Motors 2

Utilise Statistical Analysis to Develop an Efficient Paramet...

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2nd IEEE International Power Electronics and Application symposium, PEAS 2023

作者： Sloan, Maximilian Hanlon Abobghala, Abdelmenen Ahmed, Mahmud Mohamed Fanan, Anwar Mehdawi, Meftah Abuhelala, Mohamed National College for Nuclear at Lakes College Electrical Engineering Department Workington United Kingdom Tte Technical Engineering Middlesbrough College Electrical Engineering Department Middlesbrough United Kingdom Higher Institute of Science and Technology Computer Science Department Tripoli Libya University Campus North Lincolnshire Dn Colleges Group Engineering Department Scunthrope United Kingdom

ISBN: (纸本)9798350313765

Induction motors are the most widely adopted alternating current machines in industry. Requirements for precise and efficient control of such motors has led to developments in high performance drives, namely direct torque control. The induction motors parameters must be ascertained to accurately tune the drive to its optimum performance, failing to do so would result in an inadequate response from the drive, leading to sluggish dynamics and premature deterioration of the motor and drive. The process of optimizing the drive parameters is laborious, costly and time consuming. MathWorks provides software to model and modify the parameters of a direct torque control drive, however the trial and error retuning methods are tedious. This paper presents the development of an AC induction motor and direct torque control drive parameter retuning app which can accurately predict the motor and drive parameters and then simulate its performance using solely the motor nominal power rating. The app utilises the AC4 block and pre-set induction motor models from within the Math Works Specialised Power systems library. The effectiveness of the app was first tested with known data points and then at random query points and operating conditions, all of which produced satisfactory results in significantly reduced time. © 2023 IEEE.

关键词： direct torque control drive parameter estimation Math Works Motor drives power systems

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Optimal control of Water Quality in a Recirculating Aquaculture System 13th

Optimal Control of Water Quality in a Recirculating Aquacult...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： dos Santos, Allyne M. Attramadal, Kari J. K. Skogestad, Sigurd Norwegian Univ Sci & Technol NTNU Dept Chem Engn NO-7491 Trondheim Norway Norwegian Univ Sci & Technol NTNU Dept Biotechnol & Food Sci NO-7491 Trondheim Norway

This study compares nonlinear model predictive controls, using setpoint tracking objective function (NMPC), and economic objective function (E-NMPC), with a hierarchical control structure of PI controllers applied to the water treatment of a Recirculating Aquaculture System (RAS), which consists of a tank, a biofilter, a stripper and an oxygen cone. Two of the three control structures used in this work consist of an optimization layer on top of proportional integral (PI) control loops or NMPC. The optimization layer reduces the degrees of freedom with an economic objective function by deactivating some of the manipulated variables when choosing between buffer and base addition and location of addition to the system. The third structure consists of a single layer with E-NMPC. The PI control structure is easy to design once we know the relationship between the variables. The PI controllers' performance were satisfactory, but a small back-off would be needed, if the constraints would not be soft constraints, or use of buffer with base would be necessary to improve speed and avoid constraint violation, as some components took more than 6 days to reach the optimal concentration. The NMPC and the E-NMPC performed better: the NMPC was much faster on conducting the controlled variables to optimal conditions, despite not providing optimal economic cost during the trajectory;and the E-NMPC provided a mixture of smooth trajectory and optimal cost. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license(https://***/licenses/by-nc-nd/4.0/)

关键词： process control applications control of constrained systems dynamics and control Hierarchical multilevel andmultilayer control Optimal operation of water resources systems Model predictive control of water resources systems

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Dynamic process Monitoring Based on Integrated Statistic of Principal Component Analysis and Slow Feature Analysis 13th

Dynamic Process Monitoring Based on Integrated Statistic of ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Ji, Cheng Tao, Tingting Wang, Jingde Sun, Wei Beijing Univ Chem Technol Coll Chem Engn North Third Ring Rd 15 Beijing 100029 Peoples R China

process monitoring has attracted extensive interest for real-time operating evaluation due to the expectation of a safe and higher-quality production in chemical industry. Principal component analysis (PCA), an effective method for data dimensionality reduction, has been widely utilized in static process monitoring. However, industrial process data generally show dynamic characteristics because certain sequences of process variables are autocorrelated due to internal mechanisms. Recently, slow feature analysis (SFA) has been proved to have a good performance in extracting the slowly changing features in dynamic processes. To monitor both static and dynamic relations, a novel dynamic process monitoring method based on integrated statistic of PCA and SFA is proposed by extracting data feature of static and dynamic variables respectively. Variables are first grouped into static and dynamic categories according to their autocorrelation. For static part, PCA is applied to extract static variable features and calculate T-2 and SPE statistics, while SFA is adopted in dynamic part for the extraction of dynamic variable features, and T-2, Te-2, S-2 and Se-2 statistics can be calculated accordingly. At last, these statistics can be integrated by a support vector data description (SVDD) to give a final process monitoring result. The performance of the proposed method is compared with other dynamic process monitoring methods on the benchmark Tennessee Eastman process (TEP). Copyright (C) 2022 The Authors.

关键词： process dynamics Feature extraction Mutual information Fault detection Support vector data description

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Quantum Computing and Resilient Design Perspectives for Cybersecurity of Feedback systems 13th

Quantum Computing and Resilient Design Perspectives for Cybe...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Rangan, Keshav Kasturi Abou Halloun, Jihan Oyama, Henrique Cherney, Samantha Assoumani, Ilham Azali Jairazbhoy, Nazir Durand, Helen Ng, Simon Ka Wayne State Univ Dept Chem Engn & Mat Sci Detroit MI 48202 USA

Cybersecurity of control systems is an important issue in next-generation manufacturing that can impact both operational objectives (safety and performance) as well as process designs (via hazard analysis). Cyberattacks differ from faults in that they can be coordinated efforts to exploit system vulnerabilities to create otherwise unlikely hazard scenarios. Because coordination and targeted process manipulation can be characteristics of attacks, some of the tactics previously analyzed in our group from a control system cybersecurity perspective have incorporated randomness to attempt to thwart attacks. The underlying assumption for the generation of this randomness has been that it can be achieved on a classical computer;however, quantum computers can also create random behavior in the results of computations. This work explores how errors in quantum hardware that can create non-deterministic outputs from quantum computers interact with control system cybersecurity. These studies serve as a reminder of the need to incorporate cybersecurity considerations at the process design stage. Copyright (C) 2022 The Authors.

关键词： quantum computing control resilience cybersecurity

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Surrogate-assisted fault detection framework for dynamic process 13th

Surrogate-assisted fault detection framework for dynamic pro...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Kiran, Baru Chandra Dutta, Arnab BITS Pilani Chem Engn Dept Hyderabad Campus Hyderabad 500078 Telangana India

Digitalization is undoubtedly going to play a very important role in revolutionizing the future outlook of process industries. Data-driven models using process data can have diverse applications. The objective of this article is to develop a surrogate assisted framework that can aid in fault detection. In this context, we first develop a surrogate model that captures the dynamics of a process and then use the dynamic surrogate model in conjunction with a binary classification model to perform fault detection for a process. We have implemented a multivariate kriging-based approach to develop the surrogate for a dynamic process and the binary classifier is developed using the support vector machine (SVM) algorithm. We have applied this methodology to a three tank benchmark system. The process data required to train the models are obtained by simulating the three tank system in MATLAB. We have considered both process faults like leakage and plugging of tanks as well as sensor faults. To evaluate the performance and robustness of the methodology, we have tested the framework on different sets of process inputs, types of faults, sequences of faults, and varying magnitudes of faults as compared to those used during the training of these models. Results obtained indicate a minimum fault detection rate of 97.3% and a maximum false alarm rate of 4%. Thus, it is evident that the surrogate-assisted fault detection framework resulted in satisfactory performance. Copyright (C) 2022 The Authors.

关键词： Dynamic surrogate Kriging Fault detection process faults Sensor faults Binary classification Support vector machine

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Adaptive Contraction-based control of Uncertain Nonlinear processes using Neural Networks 13th

Adaptive Contraction-based Control of Uncertain Nonlinear Pr...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Wei, Lai McCloy, Ryan Bao, Jie UNSW Sydney Sch Chem Engn Sydney NSW 2052 Australia

Driven by the flexible manufacturing trend in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems (e.g., using process models with parametric uncertainties) with adaptable performance. The proposed adaptive control approach incorporates into the control loop an adaptive neural network embedded contraction-based controller (to ensure convergence to time-varying references) and an online parameter identification module coupled with reference generation (to ensure modelled parameters converge those of the physical system). The integrated learning and control approach involves training a state and parameter dependent neural network to learn a contraction metric parameterized by the uncertain parameter and a differential feedback gain. This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online. As uncertain parameter estimates converge to the corresponding physical values, offset-free tracking, simultaneously with improved convergence rates, can be achieved, resulting in a flexible, efficient and less conservative approach to the reference tracking control of uncertain nonlinear processes. An illustrative example is included to demonstrate the overall approach. Copyright (C) 2022 The Authors.

关键词： Artificial intelligence and machine learning Dynamic modelling and simulation for control and operation Modeling and identification process control

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Predictive control of Batch Crystallization process Using Machine Learning 13th

Predictive Control of Batch Crystallization Process Using Ma...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Zheng, Yingzhe Wu, Zhe Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117585 Singapore

This work develops a framework for building machine learning models and machine learning-based predictive control schemes for batch crystallization processes. We consider a seeded fesoterodine fumarate cooling crystallization and dissolution in a batch reactor and present the methodology and implementation of simulation, modeling, and controller design. Specifically, to address the experimental data scarcity problem, we first develop a population balance model (PBM) based on published kinetic parameters to describe the formation of crystals via nucleation, growth, and agglomeration. Then, recurrent neural network (RNN) models are developed using data from extensive simulations of the semi-empirical PBM under various operating conditions to capture the process dynamic behavior. The model predictive control (MPC) scheme using RNN models is developed to optimize the crystallization process in terms of product yield, crystal size, and energy consumption, while accounting for the constraints on the manipulated inputs. Through open- and closed-loop simulations, it is demonstrated that the RNN models well capture the process dynamics, and the RNN-based MPC achieves desired product yield and crystal size with significantly improved computational efficiency. Copyright (C) 2022 The Authors.

关键词： Batch crystallization processes Model predictive control Machine learning Recurrent neural networks

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Fault Detection in a Fluid Catalytic Cracking process using Bayesian Recurrent Neural Network 13th

Fault Detection in a Fluid Catalytic Cracking Process using ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Taira, Gustavo R. Park, Song W. Zanin, Antonio C. Porfirio, Carlos R. Univ Sao Paulo Polytech Sch Dept Chem Engn Sao Paulo Brazil Petrobras SA Santos SP Brazil

process safety is still an issue in modern chemical industries. Accidents in chemical processes are still frequent and cause great losses for chemical industries. In this context, there is a demand for the development of intelligent fault detection and diagnosis (FDD) methods that can help operators manage chemical process faults. Since a large amount of process data has become available for monitoring systems as a result of the huge deployment of computer systems and information technologies in chemical industries, the study of data-based FDD methods has become the focus of this research area. Therefore, this work proposes to investigate the performance of a promising Bayesian recurrent neural network-based method in the detection of faults in a real chemical process. The case study is related to the detection of a specific type of fault in a real fluid catalytic cracking process. The method presented satisfactory performance during testing experiments, with a good accuracy detection and a very small number of false-negative cases. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-ne-nd/4.0/)

关键词： process safety fault detection fluid catalytic cracking process machine learning Bayesian recurrent neural network

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Development of an Open-Source Software Tool to Analyze Closed-Loop control systems 8

Development of an Open-Source Software Tool to Analyze Close...

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8th International symposium on Accreditation of Engineering and Computing Education (ICACIT)

作者： Meneses, H. Cambronero, K. Louzao, J. Arrieta, O. Vilanova, R. Univ Costa Rica Fac Ingn Inst Invest Ingn San Jose 115012060 Costa Rica Univ Autonoma Barcelona Dept Telecomunicaci & Engn Sistemes Barcelona 08193 Spain

ISBN: (纸本)9781665492782

This paper presents an interactive software tool developed to be used in an entry level course of control system. This software tool is open-source and it was programmed using Python3 language. It was created with the purpose of offering students an alternative software tool with a low computational cost. It was made thinking in providing a great user experience to the students when they are learning about process dynamics and closed-loop system behavior based on proportional-integral and derivative controllers. In particular, the real-time simulation mode, provides to the user an interactive way to understand the meaning of each parameter in classical second order process models or in PID controllers and, hence it could become in a useful tool in careers as electrical engineering.

关键词： Closed-loop system control Education Feedback control Interactive tool Open-source tool PID controller

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Applying Tsallis Divergence to Proteins Organization Prediction Problems 13th

Applying Tsallis Divergence to Proteins Organization Predict...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Chernyshov, Kirill R. Russian Acad Sci VA Trapeznikov Inst Control Sci 65 Profsoyuznaya Moscow 117997 Russia

The paper considers the application of consistent measures of dependence of random values (in other words, those measures that vanish only under the conditions of stochastic independence of such values) to solve the problems of selecting representative indicators characterizing the predictability (stability) of the behavior of biological objects, such as protein units. Copyright (c) 2022 The Authors. This is an open access article under the CC BY- NC- ND license (https:// creativecommons. org/ licenses/ by- nc- nd/ 4.0/)

关键词： biomedical systems measures of dependence mutual information prediction proteins stochastic signal analysis Tsallis entropy

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