Sebastian Engell

Sebastian Engell
Technische Universität Dortmund | TUD · Faculty of Biochemical and Chemical Engineering

Prof. Dr.-Ing.

About

876
Publications
66,932
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
9,185
Citations
Citations since 2017
212 Research Items
3892 Citations
20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
Introduction
The research of my group covers various elements of Process Systems Engineering: Process Control with an emphasis on Robust Model Predictive Control, Plant-wide Optimization and Distribute Coordination, Contol of Chemical and Biochemical Processes, Production Scheduling, and Process Design under Uncertainty.
Additional affiliations
August 1990 - present
Technische Universität Dortmund
Position
  • Professor of Process Dynamics and Operations
August 1986 - July 1990
Fraunhofer Institut IITB
Position
  • Group Leader
January 1979 - July 1986
University of Duisburg-Essen
Position
  • Research Assistant
Education
October 1973 - January 1978
Ruhr-Universität Bochum
Field of study
  • Electrical Engineering

Publications

Publications (876)
Article
During the early-stage design of chemical production processes many decisions have to be made on the basis of incomplete knowledge about the underlying chemical and physical phenomena. Therefore, optimization-based approaches are often applied only in a later stage when more knowledge has been generated. In this work, an integrated approach to fast...
Conference Paper
This contribution addresses the problem of optimizing the operation of a batch process where energy is supplied electrically from mixed traditional and renewable sources. The case study considered here is that of an evaporator for the synthesis of titanium dioxide nanoparticles, where an energy-demanding distillation step is optimized and scheduled...
Article
Industrial scheduling problems are usually characterized by a high complexity and in many, if not all cases, a detailed representation of many specific features of the production process and of the constraints is necessary to ensure that the resulting schedules are feasible when they are executed. Such detailed representations are provided by comme...
Article
The stable operation and the optimal thermal control of industrial blast furnaces are challenging due to the complexity of the multi-phase and multi-scale physical and chemical phenomena, the presence of fast and extremely slow dynamics with latency periods of more than 8 hours, the absence of direct measurements of key inner variables, and the occ...
Article
Full-text available
This paper presents an optimal control problem (OCP) that finds the optimal voltage and impedance setpoints that improve the electrical efficiency of an EAF for operations at a fixed power level. In the optimization framework, an electric arc model and an EAF process model are embedded into a dynamic optimization framework that aims at minimizing t...
Article
Full-text available
This paper presents a comprehensive model of an industrial electric arc furnace (EAF) that is based upon several rigorous first-principles submodels of the heat exchange in the EAF and practical experience from an industrial melt shop. The model is suited for process simulation, optimization, and control applications. It assumes that the energy dem...
Article
Full-text available
The assessment of the advancement of technological innovations at their development stage is a difficult task, but important to judge on the performance of innovation projects. Assessments have so far been made by assessing technical characteristics, subjectively, or by counting patents. This paper proposes an approach to assess the advancement of...
Article
Full-text available
Publicly funded multi-actor research, development and innovation projects are a setting where a network of multiple organizational actors form a temporary consortium to jointly create new knowledge and market-upstream innovations. The couplings between the organizational actors and sub-groups of these actors represent joint work that leads to flows...
Preprint
Full-text available
Tube-enhanced multi-stage nonlinear model predictive control is a robust control scheme that can handle a wide range of uncertainties with reduced conservatism and manageable computational complexity. In this paper, we elaborate on the flexibility of the approach from an application point of view. We discuss the path to making design decisions to i...
Article
Im Profil: Prof. Dr.‐Ing. Sebastian Engell, Lehrstuhl für Systemdynamik und Prozessführung / Fakultät Bio‐ und Chemieingenieurwesen, TU Dortmund; 2021 mit der Arnold‐Eucken‐Medaille der GVT ausgezeichnet.
Chapter
This chapter presents an integrated modeling and optimization framework that is tailored to the optimization of the energy demand and the environmental impact of the steelmaking process in electric arc furnaces (EAF). A control vector parametrization technique is used to optimize the batch trajectories of the EAF with the goal to minimize the energ...
Chapter
Commercial process simulators are widely used in process design, due to their extensive library of models and ease of use. The results obtained from these simulators can be used for global flowsheet optimization but often gradient information is not provided so that derivative-free optimization methods must be used. The process simulator is called...
Chapter
Optimization-based process design can be an efficient tool for finding synergies between process units, but it strongly relies on accurate process models. Hence, experiments for model refinement may be necessary. We present an optimization-based methodology to enhance the process development by integrating superstructure optimization under uncertai...
Chapter
Surrogate models can be used to reduce the computational load when a simulation model is computationally costly to evaluate. This is the case if sophisticated thermodynamic models are integrated as e.g. the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state. When constructing surrogate models, the question of how to ch...
Chapter
This contribution deals with the development of an integer linear programming (ILP) model and a solution strategy for a two-stage industrial formulation plant with parallel production units for crop protection chemicals. Optimal scheduling of this plant is difficult, due to the number of units and operations that must be scheduled while at the same...
Chapter
In this work, a simulation-optimization strategy is applied to a benchmark scheduling problem from the pharmaceutical industry, as published by Kopanos, et al. (2010). The optimization is performed by a meta-heuristic using a commercial Discrete Event Simulation software as the schedule builder (simulation-optimization approach). Our work is motiva...
Chapter
For the application of advanced process control and optimization methods, a dynamic process model is necessary. Developing a purely mechanistic white-box process model is time consuming and challenging when the underlying physical, chemical or biochemical phenomena are not fully understood. Black-box models can be used in such cases when sufficient...
Chapter
This work presents the application of modifier adaptation with quadratic approximation (MAWQA) to the production process of hydrophobically modified ethoxylated urethanes (HEUR) by reactive extrusion in a simulation study. A plant-model mismatch in the conveying rate in the twin-screw extruder model as well as a mismatch in the reaction kinetics ar...
Chapter
This paper investigates the flexible operation of a Continuous Oscillatory Baffled Reactor for the hydrothermal synthesis of zeolites to find the economically optimal operating trajectory in the presence of varying electric energy prices. The process and the rigorous dynamic model are introduced. The performance of the dynamic optimization scheme i...
Chapter
Titania nanoparticles are an important building block for materials with photocatalytic activity or specific optical properties. The production via the hydrolysis of an alcoxide precursor involves a distillation step which is needed to control the final particle size but is time consuming and energy-intensive. The concern of this paper is the optim...
Article
Dynamic process models are a key requirement for advanced process control and the application of process optimization techniques. The derivation of these models is time consuming and error-prone in cases where a lack of physico-chemical understanding is present. Machine learning (ML) methods can be employed in these cases to extract models or model...
Article
Tube-enhanced multi-stage nonlinear model predictive control is a robust control scheme that can handle a wide range of uncertainties with reduced conservatism and manageable computational complexity. In this paper, we elaborate on the flexibility of the approach from an application point of view. We discuss the path to making design decisions to i...
Article
We propose a novel method for the synthesis of computationally efficient multi-stage model predictive controllers based on scenario trees for uncertain linear systems. As for several uncertainties and long prediction horizons the scenario tree can become very large, we consider a situation where a robust horizon is used, i.e. the scenario tree bran...
Article
Full-text available
The operation of on-site power plants in the chemical industry is typically determined by the steam demand of the production plants. This demand is uncertain due to deviations from the production plan and fluctuations in the operation of the plants. The steam demand uncertainty can result in an inefficient operation of the power plant due to a surp...
Article
Full-text available
Flowsheet optimization is an important part of process design where commercial process simulators are widely used, due to their extensive library of models and ease of use. However, the application of a framework for global flowsheet optimization upon them is computationally expensive. Based on machine learning methods, we added mechanisms for reje...
Article
Full-text available
This paper is concerned with the real-time optimization (RTO) of chemical plants, i.e., the optimization of the steady-state operating points during operation, based on inaccurate models. Specifically, modifier adaptation is employed to cope with the plant-model mismatch, which corrects the plant model and the constraint functions by bias and gradi...
Article
The stable, economically optimal, and environmental-friendly operation of blast furnaces is still a challenge. Blast furnaces consume huge amounts of energy and are among the biggest sources of CO2 in the metal industry. The operation of industrial blast furnaces is challenging because of their sheer size, multi-phase and multi-scale physics and ch...
Article
In this work we address the challenge of integrating production planning and maintenance optimization for a process plant. We consider uncertain predictions of the equipment degradation by adopting a stochastic programming formulation with decision-dependent uncertainty. The probability of the uncertain parameters, in this work the remaining useful...
Article
Full-text available
Models based on first principles are an effective way to model chemical processes. The quality of these depends critically on the accurate description of thermodynamic equilibria. This is provided by modern thermodynamic models, e.g., PC‐SAFT, but they come with a high computational cost, which makes process optimization challenging. This can be ad...
Conference Paper
This paper discusses a new approach to model predictive control (MPC) of switched nonlinear dynamic systems. Optimal control schemes that are based on relaxation followed by integrality restoration, have been proven to be computationally efficient in handling switched systems and therefore, are promising candidates for use in MPC algorithms. The ma...
Article
This work deals with 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 present coupled layout of the plant under consideration, the two stages, product formulation and packaging, are directly coupled, i.e. the products...
Conference Paper
Model Predictive Control (MPC) is an advanced control strategy for the control of multi-variable and constrained dynamical systems. Tube-based MPC is a robust control strategy used to handle uncertainties that are present in the model. Since the full-state information is rarely available in practical applications, the estimation error must also be...
Chapter
This contribution deals with the development of a Constraint Programming (CP) model and solution strategy for a two-stage industrial formulation plant with parallel production units for crop protection chemicals. Optimal scheduling of this plant is difficult: a high number of units and operations have to be scheduled while at the same time a high d...
Article
We propose a robust Nonlinear Model Predictive Control (NMPC) scheme that provides an improved trade-off between optimality and complexity when compared to other available strategies. Two controllers are employed in the proposed framework: A multi-stage primary controller that optimizes a given objective in the presence of uncertainties with tighte...
Conference Paper
This paper proposes a model-free extremum seeking control (ESC) approach to optimize the productivity of continuous cultures of microalgae, considering the dilution rate and the light intensity as manipulated variables, and the biomass concentration as single measurement. The resulting two-input single-output optimization problem is first solved us...
Conference Paper
The daily operation of blast furnaces in the steel industry is only partly automated. The thermal control of the process is yet carried out manually by the operators. Their decisions may lead to an oversupply of carbon-based fuels, causing surplus production of carbon monoxide. The unexploited excess of carbon monoxide in the iron oxide reduction r...
Article
Full-text available
The trade‐off between optimality and complexity has been one of the most important challenges in the field of robust model predictive control (MPC). To address the challenge, we propose a flexible robust MPC scheme by synergizing the multi‐stage and tube‐based MPC approaches. The key idea is to exploit the nonconservatism of the multi‐stage MPC and...
Chapter
The performance of the reconstruction of the states of the controlled system is a key factor for the performance of nonlinear model-based control. In this work, the design and experimental evaluation of a Constrained Extended Kalman Filter (CEKF) for a continuous copolymerization process is presented. The experimental set-up and the model are intro...
Chapter
Accurate process models which are the key to a reliable model-based process design usually need to be identified on the basis of expensive laboratory experiments. In this work, we present an integrated methodology which enables to focus these experiments on the most relevant model parameters by combining a global sensitivity analysis and optimal de...
Chapter
Chemical process simulations rely on the accurate representation of thermodynamic phenomena. Complex models like the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) provide such accurate descriptions but due to their implicit formulation, process optimization based on such models is computationally very demanding. This issue can be a...
Article
The simulated moving bed (SMB) process is a highly efficient continuous chromatographic separation process. Due to its hybrid process dynamics that lead to discontinuities and sharp fronts on the state trajectories, optimal SMB process operation is challenging. Process performance can be improved by applying model-based optimizing control methods....
Article
This paper proposes a model-free extremum seeking control (ESC) approach to optimize the productivity of continuous cultures of microalgae, considering the dilution rate and the light intensity as manipulated variables, and the biomass concentration as single measurement. The resulting two-input single-output optimization problem is first solved us...
Article
A robust adaptive controller for nonlinear plants with parametric uncertainties, additive disturbances, and state estimation errors based on the tube-enhanced multi-stage (TEMS) nonlinear model predictive (NMPC) framework is proposed. In TEMS NMPC, primary multi-stage NMPC is used to achieve robustness against the uncertainties which have a large e...
Article
In this work the control of the reactive extrusion of e-Caprolactone in a twin-screw extruder using nonlinear model predictive control with a tracking objective is investigated. For this, the modeling of the extrusion process using a one-dimensional mechanistic model is presented and extended to reactive extrusion systems. A novel modeling approach...
Preprint
Full-text available
The trade-off between optimality and complexity has been one of the most important challenges in the field of robust Model Predictive Control (MPC). To address the challenge, we propose a flexible robust MPC scheme by synergizing the multi-stage and tube-based MPC approaches. The key idea is to exploit the non-conservatism of the multi-stage MPC an...
Conference Paper
In this paper, we propose an asymptotically stabilizing formulation of multi-stage nonlinear model predictive control (NMPC) for plants with state and input dependent uncertainties. We derive time-varying Lyapunov-type sufficient conditions for asymptotic stability. We then propose a novel multi-stage NMPC formulation with time-varying terminal con...
Article
Full-text available
This paper addresses the problem faced by large electricity consumers to simultaneously determine the optimal day-ahead electricity procurement and the optimal energy-aware production schedule. The inherent uncertainty of the problem, due to the bidding process in the day-ahead market, is dealt with by means of the stochastic programming modeling f...
Book
Full-text available
Front Matter. Wiley announcement: "ICT Policy, Research, and Innovation: Perspectives and Prospects for EU-US Collaboration provides a clearly readable overview of selected information and communication technology (ICT) and policy topics. Rather than deluge the reader with technical details, the distinguished authors provide just enough technical b...
Chapter
This chapter focuses on a few linked Information and Communication Technologies: 5th Generation, Internet of Things, cyber‐physical systems, Next Generation Internet, Big Data, and cybersecurity which are key enablers for tomorrow's smart society, for tackling the societal challenges, and for higher productivity and better services. It cites some e...
Chapter
The connectivity aspect of the Internet of Things (IoT) has received most of the attention over the last years and has led to mature solutions for IoT‐connected devices. This chapter focuses on the opportunities that the provision of streams of real‐time data from a large number of IoT‐connected devices with sensing capabilities provides for monito...
Conference Paper
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 measurem...
Conference Paper
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...
Article
Full-text available
A channel arc model (CAM) that predicts the temperature and the geometry of an electric arc from its voltage and impedance set-points is presented. The core of the model is a nonlinear programming (NLP) formulation that minimizes the entropy production of a plasma column, the physical and electrical properties of which satisfy the Elenbaas-Heller e...
Conference Paper
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 mo...
Article
Full-text available
The physical and virtual connectivity of systems via flows of energy, material, information, etc., steadily increases. This paper deals with systems of sub-systems that are connected by networks of shared resources that have to be balanced. For the optimal operation of the overall system, the couplings between the sub-systems must be taken into acc...
Article
This paper discusses the stability properties of a robust nonlinear model predictive control (NMPC) scheme that is based on a multi-stage optimization formulation. The use of a scenario tree to represent the uncertainty makes it possible to formulate a closed-loop robust approach with recourse which improves the open-loop approach in terms of perfo...
Conference Paper
In this paper, a nonlinear model predictive control scheme 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 switc...
Article
Full-text available
We address the question of how to reduce the inevitable loss of performance that is incurred by robust multi-stage NMPC due to the lack of knowledge compared to the case where the exact plant model (no uncertainty) is available. Multi-stage NMPC in the usual setting over-approximates a continuous parametric uncertainty set by a box and includes the...
Presentation
We present a direct optimizing control approach for the Tennessee Eastman Benchmark Process based on the economics NMPC algorithms implemented in the do-mpc platform
Presentation
A method for implementing optimal driving of an F1/10 autonomous vehicle as both an NMPC approach and subsequently as a machine learning approach
Article
As a major energy consumer, steel plants can help to stabilize the power grid by shifting production from periods with high demand. Electric arc furnaces can be operated at different power levels, affecting energy efficiency, duration of melting tasks and the rate of electrode degradation, which has previously been neglected. We thus propose a new...
Article
Model-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or c...
Article
Full-text available
The architects of inter-organizational R&D projects organize collaboration by structuring the activities and the knowledge base of the project. How do these two dimensions interplay and what are the implications on the project execution? The paper aims at developing new perspectives on inter-organizational multi-actor R&D projects using an explorat...
Conference Paper
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...
Conference Paper
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 chall...
Conference Paper
Full-text available
A novel robust nonlinear model predictive control scheme (NMPC) based on the multi-stage formulation is introduced in this paper. The scenario tree of Multi-stage NMPC is often built by assuming parametric uncertainty and by considering the minimum and maximum values of the parameters. This can augment the uncertainty set and can result in a perfor...
Chapter
For the reproducible and efficient operation of chromatographic separations, automatic control is indispensable. In this chapter, we first present the standard process control as it is usually implemented in industrial separations. Then we discuss concepts for advanced control. These concepts not only keep the process at a predefined set point but...
Article
Full-text available
Chemical production sites usually consist of plants that are owned by different companies or business units but are tightly connected by streams of materials and carriers of energy. Distributed optimization, where each entity optimizes its objective and the transfer prices of energy and materials are adapted by a coordinator, is a promising approac...
Article
Full-text available
In macroscopic dynamic models of fermentation processes, Elementary Modes (EM) derived from metabolic networks are often used to describe the reaction stoichiometry in a simplified manner and to build predictive models by parameterizing kinetic rate equations for the EM. In this procedure, the selection of a set of EM is a key step which is followe...
Article
Full-text available
The mitigation of the climate change requires a significant reduction of the fossil energy consumption in all industrial sectors. The implementation of formalized management systems supports the industry to continuously improve the energy performance which is measured using so called "Energy Performance Indicators". One essential requirement for th...
Article
The performance of most bioprocesses can be improved significantly by the application of model-based methods fromadvanced process control (APC). However, due to the complexity of the processes and the limited knowledge about them, plant-model mismatch is unavoidable. A variety of different modeling strategies (each with individual advantages and de...
Article
A novel non-conservative robust nonlinear model predictive control scheme (NMPC) based on the multi-stage formulation is introduced for the case of an ellipsoidal uncertainty set. Multi-stage NMPC models uncertainty by a tree of discrete scenarios. In the case of a continuous-valued uncertainty, the scenario tree is usually built for all combinatio...
Chapter
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 ad...
Chapter
The surge of computational power and the increasing availability of data in the process industry result in a growing interest in data based methods for process modelling and control. In this contribution a concept is described that uses statistical methods to analyse the root-causes for deviations from baselines that are used for the monitoring of...
Chapter
The optimal operation of large chemical or petrochemical production sites is challenging, because streams of shared resources such as steam or intermediates physically couple the individual production plants. For a feasible operation, it is essential to coordinate the production of the plants to balance the networks for the shared resources. Often,...
Chapter
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 appli...