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A reduced model for particle calcination for use in DEM/CFD simulations

Particuology 2024年第10期93卷 316-327页

作者： Lucas Mieg Torben Bergold Enric Illana Mahiques Viktor Scherer Martin Mönnigmann Automatic Control and System Theory Ruhr University BochumGermany Energy Plant Technology Ruhr University BochumGermany

We treat the accurate simulation of the calcination reaction in particles,where the particles are large and,thus,the inner-particle processes must be *** these processes need to be described with coupled partial differential equations(PDEs)that must be solved numerically,the computation times for a single particle are too high for use in simulations that involve many *** of this type arise when the Discrete Element Method(DEM)is combined with Computational Fluid Dynamics(CFD)to investigate industrial systems such as quicklime production in lime shaft *** show that,based on proper orthogonal decomposition and Galerkin projection,reduced models can be derived for single particles that provide the same spatial and temporal resolution as the original PDE models at a considerably reduced computational *** the finite volume particle models with the reduced models results in an overall reduction of the reactor simulation time by about 40%for the sample system treated here.

关键词： Calcination Reactive bulk Model reduction Discrete element method Galerkin projection

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Convective drying of wood chips:Accelerating coupled DEM-CFD simulations with parametrized reduced single particle models

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Particuology 2024年第1期84卷 158-167页

作者： Lucas Reineking Jonas Fischer Ali Mjalled Enric Illana Siegmar Wirtz Viktor Scherer Martin Monnigmann Automatic Control and System Theory Ruhr-Universität-BochumUniversitätstraße 150Bochum44801Germany Energy Plant Technology Ruhr-Universität-BochumUniversitätstraße 150Bochum44801Germany

The simulation of industry-scale reactive bulks is challenging due to the complex interaction between fluid and *** particles in the bulk and their interaction with the fluid flow can be described by combined Discrete Element Method-Computational Fluid Dynamics(DEM-CFD)***,the computational cost of the Finite Volume(FV)methods deployed in these models can become prohibi-tively expensive,especially for high inner-particle *** particle Reduced Models(RMs)can be used to achieve both fast and accurate descriptions of the processes in each *** an example of bulk systems comprising heat and mass transfer,we compared FV and RM simulations for the drying of wood chips in a bulk reactor.A manifold-based nonlinear interpolation was applied to resolve changing boundary conditions for the *** simulations showed that RMs provide accurate values for the thermodynamic state variables of the ***,the time required for the bulk simulation was reduced by 67%with the *** is evident that simulations with high inner-particle resolution can be accelerated by RMs if manifold-based nonlinear interpolation is used to address changing boundary conditions.

关键词： Reduced model Reactive bulk Discrete element method Manifold-based interpolation Drying of porous mediaDEM-CFD

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Computing the explicit MPC solution in the constrained zonotope case 63

Computing the explicit MPC solution in the constrained zonot...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Stoican, Florin Mihai, Stefan S. Monnigmann, Martin Ciubotaru, Bogdan D. University 'Politehnica' of Bucharest Department of Automatic Control and Systems Engineering 313 Splaiul Independenţei Sector 6 Bucharest060042 Romania Ruhr University Bochum Martin Mönnigmann Is with Automatic Control and System Theory Bochum44801 Germany

ISBN: (纸本)9798350316339

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem's constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution. © 2024 IEEE.

关键词： State space methods

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Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application*

Embedded Implementation of a Neural Network emulating Nonlin...

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European control Conference (ECC)

作者： Sebastian Leonow Raphael Dyrska Martin Mönnigmann Department of Mechanical Engineering Automatic Control and System Theory Ruhr-Universität Bochum Bochum Germany

We present the design, training, and implementation of a nonlinear autoregressive neural network for the control of a multi-input, multi-output hydraulic plant. The network mimics the optimal control signals of a nonlinear model predictive controller and is implemented on a low-level microcontroller. While trained with simulation data only, experiments on the real plant show that not only the setpoint tracking, but to some degree also the constraint satisfaction and unmeasured disturbance rejection are adapted by the neural network. In contrast to the optimization-based predictive controller, the neural network easily runs on an ESP32 microcontroller and Micropython with guaranteed evaluation time and still achieves similar control performance as the predictive controller.

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Computing the explicit MPC solution in the constrained zonotope case

Computing the explicit MPC solution in the constrained zonot...

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IEEE Conference on Decision and control

作者： Florin Stoican Ştefan S. Mihai Martin Mönnigmann Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest 313 Splaiul Independenţei 060042 Sector 6 Bucharest Romania Martin Mönnigmann is with Automatic Control and System Theory Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem’s constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution.

关键词： State-space methods Predictive control

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A reduced model for particle calcination for use in DEM/CFD simulations

arXiv

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arXiv 2023年

作者： Reineking, Lucas Bergold, Torben Illana, Enric Scherer, Viktor Mönnigmann, Martin Automatic Control and System Theory Ruhr University Bochum Germany Energy Plant Technology Ruhr University Bochum Germany

We treat the accurate simulation of the calcination reaction in particles, where the particles are large and, thus, the inner-particle processes must be resolved. Because these processes need to be described with coupled partial differential equations that must be solved numerically, the computation times for a single particle are too high for use in simulations that involve many particles. Simulations of this type arise when the Discrete Element Method (DEM) is combined with Computational Fluid Dynamics (CFD) to investigate industrial systems such as quick lime production in lime shaft kilns. We show that, based on proper orthogonal decomposition and Galerkin projection, reduced models can be derived for single particles that provide the same spatial and temporal resolution as the original PDE models at a considerably reduced computational cost. Replacing the finite-volume particle models with the reduced models results in an overall reduction of the reactor simulation time by about 60% for the simple example treated here. © 2023, CC BY.

关键词： Finite difference method

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Soft sensor for monitoring dynamic changes in cell composition 9

Soft sensor for monitoring dynamic changes in cell compositi...

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9th IFAC Conference on Foundations of systems Biology in Engineering, FOSBE 2022

作者： Espinel-Ríos, Sebastián Morabito, Bruno Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

ISBN: (纸本)9781713867890

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia coli's metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens. Copyright © 2022 The Authors.

关键词： Metabolism

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Towards Safe Exploration for Autonomous Vehicles using Dual Model Predictive control

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IFAC-PapersOnLine 2022年第27期55卷 387-392页

作者： Mohamed Soliman Bruno Morabito Rolf Findeisen Laboratory for System Theory and Automatic Control Otto-von-Guericke Universität Magdeburg Germany Control and Cyber-physical Systems Laboratory TU Darmstadt Darmstadt Germany

Collision-free control and planning for autonomous vehicles in an only partially known environment is challenging. Often this problem is tackled passively - without active exploration, i.e., planning and control are performed based on the information obtained during operation without explicitly considering that reducing the environment uncertainty by active exploration can improve the overall performance, e.g., reduce the time needed to achieve the goal. We propose a moving horizon planning approach combined with a dual-mode predictive control formulation that actively explores the environment to obtain improved obstacle information. Given a path by the moving horizon planner, the dual-mode predictive tracking controller balances two objectives: fast and safe goal achievement, taking into account potential improvements due to active exploration - learning - of the environment. Adding an excitation term to the objective function incentivizes the system to explore the environment to enhance the overall objective and reduce uncertainty. The exploration-exploitation formulation enhances the overall performance while ensuring obstacle avoidance in combination with the moving horizon planning strategy. An example of an autonomous ground vehicle operating in a cluttered, only partially known environment demonstrates the approach's efficacy.

关键词： Model predictive control Dual control Perception Aware Planning control Obstacle Avoidance Autonomous Vehicles

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Towards Risk-aware Machine Learning Supported Model Predictive control and Open-loop Optimization for Repetitive Processes

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IFAC-PapersOnLine 2021年第6期54卷 321-328页

作者： Bruno Morabito Johannes Pohlodek Janine Matschek Anton Savchenko Lisa Carius Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany

Many processes operate repetitively, for example batch processes in biotechnology or chemical engineering. We propose a method for risk-aware run-to-run optimization and model predictive control of repetitive processes with uncertain models. The goal is to increase the performance as the number of runs increases by improving the model despite limited measurements while considering model uncertainty and avoiding uncertain areas. The method uses a gray-box model, i.e. a model formed by a first principle and a machine learning component, in this case an artificial neural network. The model uncertainty might be large, particularly in the first runs, where only a few measurements are available. We propose to quantify this uncertainty using Bayesian inference. This is in turn reflected by a risk measure entering an open-loop optimal control problem and a shrinking-horizon Model Predictive controller as a constraint to limit control and exploitation in high risk areas. We show that using this risk measure we are able to efficiently reach high process performance. The proposed method is tested in simulations on two biotechnological fed-batch processes.

关键词： Optimal control machine learning Gaussian process uncertain process batch-to-batch run-to-run biotechnology predictive control neural networks

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Derivative-Free Data-Driven control of Continuous-Time Linear Time-Invariant systems

arXiv

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arXiv 2024年

作者： Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Department of Electrical Electronic and Information Engineering University of Bologna Italy Institute for System Theory and Automatic Control University of Stuttgart Germany Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. © 2024, CC BY.

关键词： Continuous time systems

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