Process Dynamics and Operations Group (DYN) - Video Portal
Our YouTube ChannelWelcome to our new video portal
A personal message of Prof. Dr.-Ing. Engell to co-workers, project partners, colleagues, friends, and former members of the dyn group:
The Covid-19 situation created new challenges for all of us. Besides having to change our teaching methods drastically (especially for an old-fashioned blackboard and chalk addicted professor), the modification of all conferences and symposia into virtual events was also a new challenge, as it involved prior video recording of the presentations. But challenges also create chances, and going virtual can mean larger (possibly selective) attendance, and the longer-term availability of the recorded material is another advantage. Therefore we are making the video presentations of the 2020 conference papers of the dyn group available via this channel. The presentations are all nicely done and easy to follow, so please have a look! I would like to thank all group members, project partners and colleagues for the smooth collaboration during the past months! Let us continue to make the best of the situation! I wish good health to all of you and your families and friends!
Sebastian Engell
Welcome to the video portal of the DYN research group
A personal message of Dipl.-Inf. Tim Janus (PhD Candidate) to people interested in the research fields of the DYN group,
I would like to welcome every person who is interested in research fields of chemical process dynamics and operations. Let me shortly introduce you to our multi-cutural and interdisciplinary research group. Our research is mainly focused on control but we're a broad group. We also work in the fields of scheduling, machine learning, process synthesis and design optimization. I hope you'll find our work inspiring. Feel free to get in touch. We're looking forward to discussions and are also interested in your your contributions to the scientific community. Enjoy the video!
Tim Janus
TOC: Contributions at Conferences
During the covid19 situation the DYN chair contributed a number of video presentations to different conferences. At this place you find all our contributions at one central location.
- ESCAPE - The DYN group added six contributions.
- Reliable Modelling of Twin-screw Extruders by Integrating the Backflow Cell Methodology into a Mechanistic Model
- Integrating superstructure optimization under uncertainty and optimal experimental design in early stage process development
- Online Process Monitoring in SMB Processes
- Nonlinear Prediction Model of Blast Furnace Operation Status
- SCHEDULING OF A LARGE-SCALE INDUSTRIAL MAKE-AND-PACK PROCESS WITH FINITE INTERMEDIATE BUFFER USING DISCRETE-TIME AND PRECEDENCE-BASED MODELS
- A novel multi-stage stochastic formulation with decision-dependent probabilities for condition-based maintenance optimization
- Market-like distributed coordination of individually constrained and coupled production plants with quadratic approximation
- World Congress Computational Intelligence - The DYN group added one contribution.
- Neural Networks for Surrogate-assisted Evolutionary Optimization of Chemical Processes
- IFAC World Congress - The DYN group added eleven contributions.
- Experimental Real Time Optimization of a Continuous Membrane Separation Plant
- An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment
- A Multi-stage Economic NMPC for the Tennessee Eastman Challenge Process
- Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform
- Optimization of the electric efficiency of the electric steel making process
- A Simplified Implementation of Tube-Enhanced Multi-Stage NMPC
- A Bi-level Approach to MPC for Switching Nonlinear Systems
- Dual multi-stage NMPC using sigma point principles
- An efficient model-error model update strategy for multi-stage NMPC with model-error model
- A Two-stage Simulated Annealing-based Scheduling Algorithm for a Make-and-Pack Production Plant
- ECC 2020 - The DYN group added two contributions.
ESCAPE30
The ESCAPE European Symposium on Computer Aided Process Engineering conference series is held every year. The series started with ESCAPE-1 held in Denmark (1992), while the latest in the series, ESCAPE30 has been held in Graz during 31.08 - 02.9 June 2020. The DYN group has six contributions at the conference.
Our contributions:- Reliable Modelling of Twin-screw Extruders by Integrating the Backflow Cell Methodology into a Mechanistic Model
- Integrating superstructure optimization under uncertainty and optimal experimental design in early stage process development
- Online Process Monitoring in SMB Processes
- Nonlinear Prediction Model of Blast Furnace Operation Status
- SCHEDULING OF A LARGE-SCALE INDUSTRIAL MAKE-AND-PACK PROCESS WITH FINITE INTERMEDIATE BUFFER USING DISCRETE-TIME AND PRECEDENCE-BASED MODELS
- A novel multi-stage stochastic formulation with decision-dependent probabilities for condition-based maintenance optimization
- Market-like distributed coordination of individually constrained and coupled production plants with quadratic approximation
Reliable Modelling of Twin-screw Extruders by Integrating the Backflow Cell Methodology into a Mechanistic Model
Keywords: Extrusion, Reactive Extrusion, Twin-Screw Extrusion, Backflow cell model, Residence Time Distribution
Process modelling for twin-screw extruders is important for the optimal design, control and understanding of these machines. Existing models are often describing the residence time distribution (RTD) of the melt based on experimental data without the usage of further process knowledge. These completely data driven methods are unreliable for more advanced extrusion processes as a strong coupling between many internal states exists, which may not be reflected in the measurements. Therefore the use of a mechanistic model is beneficial to be able to address all important effects simultaneously. The standard mechanistic model describes the RTD as a series of continuous stirred tank reactors. However, this approximation is not capable of describing tailing effects that can occur when elements that promote distributive mixing are present. These effects can be described by the backflow cell model (BCM). Within the BCM the unidirectional flow is divided into an upstream flow and downstream flow with a fixed flow ratio for a series of tanks. This model can be in cooperated into the CSTR model, exploiting the similarities of the structure of the two models. In this work, the combination of the two methods is presented and applied to different screw geometries.
Integrating superstructure optimization under uncertainty and optimal experimental design in early stage process development
Keywords: Early stage process design, superstructure optimization, model refinement, optimal experimental design
We present an iterative methodology that combines superstructure optimization, sensitivity analysis, and optimal design of experiments. In the early design phase, usually no accurate models for use in superstructure optimization are available, and the uncertainties of the models can influence the structure of the optimal design. The accuracy of the models is gradually increased by experimental investigations. In order to reduce the time and effort needed for process development, the experiments should focus on the most influencing parameters with respect to the design decisions. After one or few process structures have been fixed, further experiments will then lead to quantitatively accurate predictions. The methodology is applied to the case study of the hydroaminomethylation of decene.
Online Process Monitoring in SMB Processes
Keywords: SMB, Process Monitoring
Conventionally, preparative chromatographic separation processes are operated in batch mode. For more efficient separation, the simulated moving bed (SMB) process has been introduced. Due to its hybrid dynamics, optimal operation of the SMB process is challenging. For increased process efficiency, model-based optimizing control schemes can be applied. These schemes require online information about the states and the parameters of the plant. The online process monitoring strategy presented here is based on the transport dispersive model of the SMB process and simultaneously estimates the states and the parameters of the individual columns by exploiting the switching nature of the SMB process. The scheme can be activated before the process reaches its cyclic steady state (CSS). The strategy is demonstrated for the separation of two amino acids.
Nonlinear Prediction Model of Blast Furnace Operation Status
Keywords: Blast furnace operation status, NARX model, Multistep ahead prediction
The operation status of a process in the steel industry is mainly defined by three aspects, efficiency, productivity and safety. It provides guidance for the operators to make decisions on their future actions. The abrasive process environment inside a blast furnace (BF) makes it demanding to analyse the operation status by direct internal measurements. The blast furnace gas utilization factor (ETACO) is an essential indicator of the process efficiency. Besides efficiency, productivity and safety can, to some extent, be derived from the pressure drop (DP) and the top gas temperature (TG). This paper presents a nonlinear autoregressive network with exogenous inputs (NARX) model for the simultaneous multistep ahead prediction of ETACO, DP and TG, based upon a new set of fast and slow dynamic input attributes. Validation results using real industrial plant measurements show that this approach not only enables monitoring of the current operation status but also provides prediction capability by including the slow dynamics of the blast furnace into the model.
SCHEDULING OF A LARGE-SCALE INDUSTRIAL MAKE-AND-PACK PROCESS WITH FINITE INTERMEDIATE BUFFER USING DISCRETE-TIME AND PRECEDENCE-BASED MODELS
Keywords: MILP modelling, Industrial scheduling, Heuristic decomposition, Make-and-Pack processes, Shifting bottlenecks, Precedence-based models
We address the short-term scheduling of a two-stage continuous make-and-pack process with finite intermediate buffer and sequence-dependent changeovers from the consumer goods industry. In the current layout of the plant under consideration, the two stages, product formulation and packing, are directly coupled, i.e. the products of the formulation stage go directly to the packing stage. As for different products either one of both stages can be the bottleneck, a gain in productivity can be obtained if the two stages are decoupled by a buffer so that the formulation lines and the packing lines can both run at full capacity. The disadvantage of this setup is an increased complexity of the scheduling problem, so that support for the schedulers must to be provided. We employ a mixed-integer programming problem formulation for this purpose. The problem at hand is characterized by a large number of products in several product families, product specific order quantities and deadlines, product dependent production times, sequence-dependent changeover times, and a finite intermediate buffer. As the problem turned out to be intractable for the planning horizons of interest, a solution approach that employs a discrete-time scheduling model, a precedence-based presorting model and a decomposition strategy that is enhanced by several heuristics was developed.
A novel multi-stage stochastic formulation with decision-dependent probabilities for condition-based maintenance optimization
Keywords: Condition-based maintenance, Prognosis, Stochastic programming, Endogenous uncertainty, Cox model
The challenge addressed in this work is the integrated production planning and condition-based maintenance optimization for a process plant. We take into account uncertain information of the predicted equipment degradation adopting a stochastic programming formulation. To adjust the likelihood of the failure scenarios, we embed a prognosis model, the Cox model, into the optimization problem. We propose here a novel endogenous uncertainty formulation where the decisions at one point in time have an impact on the probability of the uncertainty. We provide computational results implementing a custom branching within the global solver BARON and decomposing the problem via the Benders algorithm.
Market-like distributed coordination of individually constrained and coupled production plants with quadratic approximation
Keywords: Follows soon...
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World Congress of Computational Intelligence
PhD candidate Tim Janus presented his work in the field of flowsheet optimization with surrogate models on the conference of evolutionary computation (CEC) which was part of the World Congress Computational Intelligence (WCCI) 2020.
Neural Networks for Surrogate-assisted Evolutionary Optimization of Chemical Processes
Keywords: evolutionary algorithms, surrogate models, optimization, neural networks, chemical processes
In the chemical industry commercial process simulators are widely used for process design due to their extensive library of models of plant equipment and thermodynamic properties and their ease of use. Most of these simulators compute the steady-states of complex flowsheets, but their models are inaccessible and derivatives with respect to their model parameters are not available. Evolutionary algorithms are a suitable approach for the global optimization of such black-box models, but they require the evaluation of many individuals. Applications to industrial-size case-studies suffer from high computational times where the numerical simulations consume the majority of the time. This contribution proposes the use of neural networks as surrogate models to guide the evolutionary search. These models are trained multiple times during the evolutionary search and are used to exclude nonpromising individuals and to generate candidate solutions. We demonstrate the performance improvement due to the use of the surrogate models for a medium-size case-study of a chemical plant consisting of a reactor, a liquid-liquid separation and a distillation column. The results show that the required number of simulations can be reduced by 50%.
IFAC World Congress
The IFAC World Congress is not merely a very big conference on systems and control (in fact it is the biggest of its kind), but it is the one event that all other IFAC meetings (the almost 100 IFAC conferences and the over 200 organizational meetings between World Congresses) are aligned and oriented towards.
Our contributions:- Experimental Real Time Optimization of a Continuous Membrane Separation Plant
- An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment
- A Multi-stage Economic NMPC for the Tennessee Eastman Challenge Process
- Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform
- Optimization of the electric efficiency of the electric steel making process
- A Simplified Implementation of Tube-Enhanced Multi-Stage NMPC
- A Bi-level Approach to MPC for Switching Nonlinear Systems
- Dual multi-stage NMPC using sigma point principles
- An efficient model-error model update strategy for multi-stage NMPC with model-error model
Experimental Real Time Optimization of a Continuous Membrane Separation Plant
Keywords: Iterative real-time optimization, Modifier adaptation, Plant-model mismatch
This paper deals with the optimal operation of a continuously operated laboratory membrane separation plant. The goal is to find an economically optimal regime of operation using the transmembrane pressure (TMP) and the operating temperature as adjustable setpoints for the low-level controllers. The main challenge is to identify the optimum in the absence of an accurate process model. We employ an iterative real-time optimization scheme, modifier adaptation with quadratic approximation (MAWQA), to identify the plant optimum in the presence of the plant-model mismatch and measurement noise. Two experiments are performed; one with and one without a productivity constraint. The experimental results show the capabilities of the MAWQA scheme to identify the process optimum in real-world scenarios. The optimum identified by the MAWQA scheme coincides with the optimum of a surrogate model that was built using a larger data set.
An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment
Keywords: Iterative real-time optimization, Modifier adaptation, Plant-model mismatch, Reactor control, PAT-sensor, NMR
Iterative real-time optimization methods like modifier adaptation based techniques are used to identify the real process optimum in the presence of structural and/or parametric plant-model mismatch. For modifier adaptation based techniques, as a prerequisite, the a priori process model has to satisfy the model adequacy conditions, i.e. positive definiteness of the Hessian as only the gradients are corrected based on the available measurement information. Modifier adaptation with quadratic approximation (MAWQA) is a powerful real-time optimization tool in which either the surrogate quadratic approximation model or the a priori known process model with gradient correction is chosen for optimization based on how well they represent the process measurements. This can lead to the selection of a quadratic model which does not satisfy the model adequacy conditions and can cause undesired oscillations. It is therefore necessary to make sure that the chosen model is adequate. In this paper, we propose to use convex quadratic approximation and thereby to ensure satisfaction of the model adequacy conditions and the convergence to the real process optimum. We demonstrate the performance of the proposed MAWQA with guaranteed model adequacy (GMA) scheme using a chemical engineering case study.
A Multi-stage Economic NMPC for the Tennessee Eastman Challenge Process
Keywords: robust NMPC, economics optimizing control, multi-stage optimization, plant-wide control, Tennessee Eastman Challenge Problem.
This paper addresses the design and implementation of a robust nonlinear model predictive control (NMPC) scheme for a benchmark plant-wide control problem. The focus of our research is on the performance of direct optimizing control for a complex large-scale process which is subject to plant-model mismatch and external disturbances. As a benchmark case for control and monitoring applications, the Tennessee Eastman Challenge (TEC) process has been widely employed in many publications. We present a first NMPC implementation for this where only economics criteria are used for the control of the process. The results obtained demonstrate the viability of plant-wide economics optimizing NMPC. We also address the issue of robustness against model uncertainties and employ multi-stage NMPC to tackle these. Different possible multi-stage NMPC implementations are discussed and the trade-offs between economic performance and robustness are highlighted.
Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform
Keywords: NMPC, artificial neural networks, learned control, F1/10, AV
This paper addresses the challenges of developing an embedded non-linear model predictive control (NMPC) solution for the optimal driving of miniature scale autonomous vehicles (AVs). The NMPC approach lends itself perfectly to driving applications, provided that a system for localization and tracking of the vehicle is available. An important challenge in the implementation results from the need to accurately steer the vehicle at high speeds, which requires fast actuation. In this paper we present a solution to this problem, which employs an artificial neural network (ANN) controller trained with rigorous NMPC input-output data. We discuss the development process, from modelling until the realization of the ANN controller within the operating system of the AV. The procedure is demonstrated within the virtual environment of the popular F1/10 race car, an AV platform widely used in AI and autonomous driving challenges. The results contain both NMPC and ANN-based simulations for different race tracks and for different driving strategies. The main focus of this work lies in the formulation of the optimal driving control problem and the training method of the ANN. Our approach uses a standardization of the driving problem, which enables us to abstractize optimal driving and to simplify it for the learning process. We show how driving patterns can be learned accurately on a reduced set of training data and that they can subsequently be extended to new and more challenging driving situations.
Optimization of the electric efficiency of the electric steel making process
Keywords: Dynamic optimization, advanced process control, process control applications, steel production, electric arc furnace, efficiency enhancement
This work reports numerical and practical results of an open-loop optimal control formulation that reduces the power consumption of the electric arc furnace (EAF) steel production process. A control vector parametrization technique is used to optimize the batch trajectory with the goal to minimize the energy losses of the process. First principles models are utilized to describe the dynamics, as well as the influence of the voltage and impedance set-points on the process. The results of the dynamic optimization provided a sequence of set-points (called a melting profile) that aligns well with intuition: the profile utilizes high power levels during the high efficiency stages of the process, and low power levels as the batch moves towards a more energy inefficient state. The benefits of the proposed optimized mode of operation are demonstrated by an experimental study case. An optimal melting profile was calculated and implemented in a fully operative ultra-high power EAF. For a series of 19 test batches, the energy consumption and the batch time of the process were reduced by 4.5% and 4.6% for one type of steel.
A Simplified Implementation of Tube-Enhanced Multi-Stage NMPC
Keywords: Nonlinear Model-predictive control, Robust NMPC, Multi-stage NMPC, Process control
In a previous work, multi-stage NMPC and tube-based NMPC schemes were combined into a single framework called tube-enhanced multi-stage NMPC with the goal of achieving an improved trade-off between simplicity and performance. In tube-enhanced multistage NMPC, the large uncertainties are handled using a multi-stage primary controller and the small uncertainties are handled using a multi-stage ancillary controller that tracks the predictions of the primary controller. In this work, we propose the replacement of the multistage ancillary controller by a single scenario NMPC that tracks the predicted trajectories of one of the scenarios of the multi-stage primary controller. The scenario that will be tracked by the ancillary controller as well as the ancillary controller model are time varying and are adapted to the current plant dynamics. The benefits of the new formulation are demonstrated on the benchmark Williams-Otto Continuous Stirred Tank Reactor (CSTR) example.
A Bi-level Approach to MPC for Switching Nonlinear Systems
Keywords: Switching dynamical systems, Model predictive control, Supermarket refrigeration, control
In this paper, a nonlinear model predictive control approach for switching dynamical systems is presented. The controller comprises of two layers of optimization. The upper layer is based on the embedding transformation technique, hence it does not require prior knowledge of the switching sequence. In particular it provides the optimal relaxed switching sequence and the corresponding trajectories of the states and regulating inputs. Whereas the lower layer restores the integrality constraints by computing a switching solution which minimizes the error with respect to the reference trajectories given from the upper optimization. The formulation of the underlying optimization problems is discussed and bounds of the errors are evaluated. In addition the algorithm is validated via a simulation of a tracking and an economics optimizing nonlinear MPC for supermarket refrigerator system with $3$ display cases and $3$ compressors. The simulation results show the applicability and efficiency of the presented approach.
Dual multi-stage NMPC using sigma point principles
Keywords: adaptive control, dual control, parameter uncertainty, robust model predictive control, multi-stage NMPC, parametric uncertainty, unscented transformation
Dual control is a technique that addresses the trade-off between probing (excitation signal) and control actions, which results in a better estimation of the unknown parameters and therefore in a better (tracking or economic) performance. The standard multi-stage NMPC scheme represents the uncertainty using a scenario tree that is often built by assuming parametric uncertainty and taking into account the minimum, nominal and maximum values of the uncertain parameters. If the uncertainty set is not a box, this augments the uncertainty set and results in a performance loss. In this paper, we mitigate this problem by tightly approximating the uncertainty set using the so-called sigma points and computing an ellipsoidal over-approximation of the reachable set of the system using the unscented transformation. In addition to this, we extend the proposed approach achieve implicit dual control actions by considering the future reduction of the ranges of the uncertainties due to control actions and measurements. The advantages of the proposed approach to the standard multi-stage NMPC scheme are demonstrated for two linear and nonlinear (semi-batch reactor) simulation case studies.
An efficient model-error model update strategy for multi-stage NMPC with model-error model
Keywords: adaptive control, model-error model, multi-stage NMPC, nonlinear model predictive control, process control, robust control
Multi-stage NMPC with model-error model (MS-MEM) handles structural plant-model mismatch present in the nominal model of the plant in a non-conservative fashion. A model-error model (MEM) that consists of a stable linear time-invariant dynamics and a static time-variant nonlinear mapping is built using the past data such that it captures the unmodeled dynamics of the plant. The scenario tree is built for the nominal and for the extreme realizations of the plant obtained using the nominal model and the model-error model, and a multi-stage decision problem is formulated. In this paper, we propose an efficient strategy to update the model-error model present in the MS-MEM approach if new measurements invalidate the model-error model. The advantages of the proposed scheme over the previous approach where only the gain of the linear model is updated are demonstrated for a continuous stirred tank reactor (CSTR) benchmark example.
A Two-stage Simulated Annealing-based Scheduling Algorithm for a Make-and-Pack Production Plant
Keywords: TBA
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ECC 2020
The European Control Conference ECC is one of the IFAC conferences that is alligend to to the well-known IFAC World Congress.
Our contributions:- Guaranteed Model Adequacy for Modifier Adaptation With Quadratic Approximation
- Adaptive multi-stage NMPC using sigma point principles
Guaranteed Model Adequacy for Modifier Adaptation With Quadratic Approximation
Keywords: Adaptation models, Optimization, Mathematical model, Linear programming, Real-time systems, Oscillators, Convergence
Iterative real-time optimization methods like modifier adaptation based techniques are used to identify the real process optimum in the presence of structural and/or parametric plant-model mismatch. For modifier adaptation based techniques, as a prerequisite, the a priori process model has to satisfy the model adequacy conditions, i.e. positive definiteness of the Hessian as only the gradients are corrected based on the available measurement information. Modifier adaptation with quadratic approximation (MAWQA) is a powerful real-time optimization tool in which either the surrogate quadratic approximation model or the a priori known process model with gradient correction is chosen for optimization based on how well they represent the process measurements. This can lead to the selection of a quadratic model which does not satisfy the model adequacy conditions and can cause undesired oscillations. It is therefore necessary to make sure that the chosen model is adequate. In this paper, we propose to use convex quadratic approximation and thereby to ensure satisfaction of the model adequacy conditions and the convergence to the real process optimum. We demonstrate the performance of the proposed MAWQA with guaranteed model adequacy (GMA) scheme using a chemical engineering case study.
Adaptive multi-stage NMPC using sigma point principles
Keywords: adaptive control, parameter uncertainty, robust model predictive control, multi-stage NMPC, parametric uncertainty, unscented transformation
A novel non-conservative robust nonlinear model predictive control scheme (NMPC) based on the multi-stage formulation is introduced. Multi-stage NMPC models uncertainty by a tree of discrete scenarios. The scenario tree is often built for the box over-approximation of the uncertainty set. This augments the uncertainty set and results in a performance loss while using the robust NMPC approaches. We propose to mitigate this problem by tightly over-approximating the uncertainty set using the so-called sigma points. An ellipsoidal over-approximation of the reachable set of the system is predicted along the prediction horizon using the unscented transformation. In addition, the plant measurements are used to reduce the size of the uncertainty set and the scenario tree of the multi-stage NMPC is updated. The advantages of the proposed scheme over the traditional multi-stage NMPC are demonstrated for a benchmark semi-batch reactor case study.