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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11-20 订阅

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Simultaneously Identifying the System dynamics and Fault Isolation for Air Data Sensor Failures: A Convex Approach 12

Simultaneously Identifying the System Dynamics and Fault Iso...

引用

12th IFAC symposium on Fault Detection, Supervision and Safety for Technical processes (SAFEprocess)

作者： Noom, J. de Visser, C. C. Ramesh, N. S. Verhaegen, M. Delft Ctr Syst & Control Dept Mech Engn Delft Univ Technol Delft Netherlands Delft Univ Technol Control & Simulat Div Dept Aerosp Engn Delft Netherlands

This paper addresses the key question that when faults occur either the aircraft system dynamics changes due to the fault or these dynamics are unknown (precisely). This question is addressed for the important case of Air Data Sensor failures, due to e.g. icing, for fixed wing aircraft operating in a nominal flight condition. The solution to this question uses basic ideas from subspace identification to cast this problem in linear least squares problem with convex constraints (nuclear norm and 1-norm constraints). The latter are relaxations of a rank and cardinality *** presented solution is validated using real-life flight test data. Copyright (c) 2024 The Authors.

关键词： Signal and identification-based methods Flight control, fault detection FDI for linear systems

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TRANSMISSION LINE MODELS FOR FLUID POWER HOSES IDENTIFIED FROM MEASUREMENT DATA

TRANSMISSION LINE MODELS FOR FLUID POWER HOSES IDENTIFIED FR...

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BATH/ASME symposium on Fluid Power and Motion control (FPMC)

作者： Manhartsgruber, Bernhard Johannes Kepler Univ Linz Inst Machine Design & Fluid Power Linz Austria

ISBN: (纸本)9780791888193

Modeling and simulation have become essential tools in fluid power engineering. On the overall system simulation level, most components are described by a concentrated parameter approach resulting in a system of ordinary differential equations. The exception to this rule is found in pipelines and hoses, where wave propagation may be of importance and hence a distributed parameter approach results in some sort of spatial discretization in the modeling process. For long bores in hydraulic valve manifolds and also for rigidly supported steel pipes, the dynamic behaviour can be predicted very well with standard models available from literature like the classical frequency dependent friciton model of D'Souza and Oldenburger. With wire or fibre reinforced high pressure rubber hoses, this is no longer true and the transfer behaviour of such transmission lines can only be characterized based on measurement data. The present paper presents a novel approach towards the construction of low order models for the approximation of the measured input-output behaviour of fluid power hose lines. The method is based on the measurement of the linearized system dynamics at a number of pressure levels and is intended towards the local linear model tree (LOLIMOT) technique after identifying reduced order models for each pressure level. The results are applicable to fluid power system simulations where the dynamics of hose lines becomes important due to high operating frequencies or when noise attenuation in the hose lines is the primary focus of the simulation. The measurements provided in the paper are for a fluid power hose with a diameter of 20 mm and a length of 3 m.

关键词： Simulation Standard model (Physics) Wire Construction Pressure Differential equations Noise (Sound) High pressure (Physics) Transmission lines Modeling Energy engineering Approximation Steel dynamics (Mechanics) Manifolds Pipes Power system simulation Wave propagation Rubber Fibers System dynamics Valves Pipelines Fluids

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Modeling, control and Online Optimization of Microalgae-based Biomass Production in Raceway Reactors 12

Modeling, Control and Online Optimization of Microalgae-base...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Otalora, Pablo Skogestad, Sigurd Guzman, Jose L. Berenguel, Manuel Univ Almeria Dept Informat CIESOL ceiA3 Almeria 04120 Spain Norwegian Univ Sci & Technol Dept Chem Engn N-7491 Trondheim Norway

Microalgae production in raceway photobioreactors is a very attractive process for biomass production because of its high sustainability. This paper presents a complete methodology for optimizing production in raceway photobioreactors. A first principles model has been developed that describes the dynamics and steady state of the system and has been used as the core of a real-time optimization to maximize the economic profit of the process, which handles the references of different variables of the system. For that, a steady-state real-time optimization approach complemented with PID controllers is presented in this work. The results obtained demonstrate the potential of steady-state real-time optimization for such systems, as well as the benefits of employing optimization techniques during process operation. Copyright (C)2024 The Authors. This is an open access article under the CC BY-NC-ND license (htips://***/licenses/by-nc-nd/4.0/)

关键词： Real Time Optimization process control Microalgae Biomass Production

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control oriented modeling for particle size distributions in a spray drying process 20

Control oriented modeling for particle size distributions in...

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20th IFAC symposium on System Identification (SYSID)

作者： Lepsien, A. Jueptner, A. Scherliess, R. Schaum, A. Univ Hohenheim Dept Proc Analyt Stuttgart Germany Univ Kiel Dept Pharmaceut & Biopharmaceut Kiel Germany Univ Hohenheim Computat Sci Hub Stuttgart Germany

This paper addresses the problem of predicting the Particle Size Distrubution (PSD) in spray drying processes in function of the liquid phase volumetric flow rate. For this purpose, based on a Boltzmann-Gibbs distribution representation the stochastic droplet dynamics identified using experimental data and the Fokker-Planck Equation (FPE). The model is extended to account for piecewise constant flow rates in order to enable future studies on automatic control of the PSD. The results highlight the effectiveness of the stochastic modeling approach for predicting the PSD in spray drying processes, and its potentials for real-time control and process optimization. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： System identification spray drying particle size distributions stochastic systems

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Dynamic Multiscale Hybrid Modelling of a CHO cell system for Recombinant Protein Production 12

Dynamic Multiscale Hybrid Modelling of a CHO cell system for...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Pennington, Oliver Rios, Sebastian Espinel Sebastian, Mauro Torres Dickson, Alan Zhang, Dongda Univ Manchester Oxford Rd Manchester M1 3AL Lancs England Princeton Univ Princeton NJ 08544 USA

Multiscale hybrid modelling of biosystems utilises advantageous aspects of several modelling approaches, from the physical interpretations of kinetic modelling to the power of a data-driven Artificial Neural Network (ANN). This study implements multiscale modelling to gain insight into the production of Trastuzumab (Herceptin) from Chinese Hamster Ovary (CHO) cells under challenging dynamics A reduced metabolic network is subject to enzyme constraints with a Dynamic Metabolic Flux Analysis (ecDMFA) approach and integrated within a macro-scale hybrid kinetic model. The model can simulate fed-batch processes with optimized feed control, as well as providing insight into the control gained by alteration to the cell culture media. On the intracellular level, the influence from extracellular perturbations can be observed, in addition to giving an estimated production rate of unmeasured by-products. Overall, this model can be used as a reliable digital twin to estimate the underlying fed-batch process dynamics for future model predictive control and process optimisation. Copyright (C)2024 The Authors. This is an open access article under the CC BY-NC-ND license (htips://***/licenses/by-nc-nd/4.0/)

关键词： Machine Learning Assisted Modelling Optimal control Experiment Design

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Model Predictive control using Physics Informed Neural Networks for process systems 12

Model Predictive Control using Physics Informed Neural Netwo...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Patel, Rahul Bhartiya, Sharad Gudi, Ravindra Indian Inst Technol Dept Chem Engn Mumbai 400076 India

Model based control approaches require an accurate and computationally fast prediction model to solve the governing equations in real time. While numerical approaches based on first principles models are accurate, the high computational cost renders them unsuitable for online estimation and model predictive control (MPC). On the other hand, the reduced order models can provide real time solutions, but there is invariably a trade-off between the accuracy and computational time. Machine learning based approaches such as physics informed neural networks (PINN) that are based on incorporating physics based knowledge into NNs, can provide faster and accurate solutions in such scenarios. This work demonstrates the control-oriented modeling of process systems governed by ODEs and DAEs using residual PINN that is trained using neural tangent kernel update. Such PINN models can replace the conventional numerical time integration of the process dynamics and facilitate accurate and faster predictions. We present our results for setpoint tracking via MPC using the PINN model to demonstrate the capability of the proposed approach. Copyright (c) 2024 The Authors.

关键词： Model Predictive control Physics Informed Neural Networks Machine Learning Optimization State Estimation

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Population Balance Model-based Dynamic Multiobjective Optimization of Yeast Cell Manufacturing 12

Population Balance Model-based Dynamic Multiobjective Optimi...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Ganko, Krystian Berliner, Marc D. Rhyu, Jinwook Wu, Liang Braatz, Richard D. Leyffer, Sven MIT Dept Chem Engn Cambridge MA 02139 USA JuliaHub Cambridge MA 02139 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA

Biological systems play a key role in many advanced manufacturing processes, of which many have interesting nonlinear dynamics. We investigate a continuous yeast cell manufacturing process that produces sustained oscillations in outputs under nominal conditions. Using a population balance model to perform dynamic optimization with multiple objectives and observability constraints, we quantify tradeoffs on the Pareto surface for varying the extent of process oscillations that the decision-maker deems tolerable (or desirable). Numerical optimal control design for oscillatory distributed parameter systems is discussed within the context of both dynamic optimization and on-line nonlinear model predictive control strategies. Copyright (c) 2024 The Authors.

关键词： Optimal control distributed parameter system population balance model multiobjective optimization biosystems and bioprocesses chemical process control

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Enhancing Reinforcement Learning Robustness via Integrated Multiple-Model Adaptive control 12

Enhancing Reinforcement Learning Robustness via Integrated M...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Rastegarpour, Soroush Feyzmahdavian, Hamid Reza Isaksson, Alf J. ABB Corp Res Vasteras Sweden

Reinforcement learning (RL) has attracted considerable attention from both industry and academia for its success in solving complex problems. However, the performance of RL algorithms often decreases in environments characterized by uncertainties, unmodeled dynamics, and nonlinearities. This paper presents a novel robust RL algorithm designed to ensure closed-loop stability for industrial processes. The algorithm considers a wide range of potential scenarios across various operating conditions and different ranges of parameter uncertainties. Using the multiple-model adaptive control methodology, the algorithm evaluates all scenarios and ranks them based on their likelihood of accurately characterizing the actual process. The validity of the results is demonstrated using a benchmark continuous stirred tank reactor (CSTR) problem. Copyright (C) 2024 The Authors.

关键词： Reinforcement learning control Robust control process control applications

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Stabilizing extremum seeking control applied to model -free bioprocess productivity optimization 12

Stabilizing extremum seeking control applied to model -free ...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Dewasme, Laurent Wouwer, Alain Vande Univ Mons Syst Estimat Control & Optimizat SECO B-7000 Mons Belgium

This study investigates an application of stabilizing extremum seeking (STAB -ESC) to model free bioprocess productivity optimization. To this end, microbial growth in a chemostat is considered as a typical example. In contrast with the classical ESC formulation, STAB -ESC confines the measurable cost function in the argument of a periodic and bounded control function, which confers very interesting features to the control scheme in terms of sparse parameter tuning, fast convergence and robustness to measurement noise. These points are highlighted in the present study through an analysis of the Lie bracket average dynamics and a performance comparison with a Newton -based recursive ES.

关键词： Data-driven control stabilization optimization averaging process control

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Lateral Path Tracking control Based on Tube MPC 24

Lateral Path Tracking Control Based on Tube MPC

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3rd International symposium on Intelligent Unmanned systems and Artificial Intelligence (SIUSAI)

作者： Ren, Hongbin Wang, Lifen Li, Yan Yang, Lin Beijing Inst Technol Beijing 100081 Peoples R China

ISBN: (纸本)9798400710025

To solve the problem of external disturbances encountered in the lateral path tracking control process of autonomous vehicles, this paper proposes a TMPC algorithm based on the nonlinear vehicle dynamics model. The core of this method lies in tightening the constraint boundaries and increasing errors feedback compensation to ensure lateral tracking performance and driving stability. Additionally, the algorithm's real-time performance is further improved by using zonotopes instead of polytopes. Finally, the effectiveness of the method is verified through simulation experiments. The results demonstrate that compared to traditional MPC, TMPC reduces the maximum value and mean square value of lateral tracking errors by 80.1% and 86.6%, respectively. It also reduces the maximum value and mean square value of heading tracking errors by 37.2% and 33.8%, respectively.

关键词： Intelligent vehicles Lateral path tracking External disturbances Tube MPC

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