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DYN at the IFAC 2017 World Congress in Toulouse, France

From July 9th until July 14th, 2017, seven members of the Process Dynamics and Operations Group (dyn) and the former PostDoc Radoslav Paulen attended the 20th IFAC World Congress in Toulouse, France.

IFAC_WC

The IFAC World Congress is largest conference organized by the International Federation of Automatic Control and it is held every three years. It is attended by scientists and engineers from both academia and industry coming from all over the world, who present and discuss current trends and emerging technologies as well as theoretical foundations in the field of control. The IFAC World Congress covers all domains and fields of application of control and aims at helping to find solutions to the future challenges in technical, social, economic, and ecological systems using automatic control.

This year, the IFAC celebrated its 60th anniversary in Toulouse, France, and the (past and present) dyn group members presented several research papers from the broad field of control and optimization. Prof. Sebastian Engell took part in a panel session about “How to Enhance Industry/University Collaboration on Advanced Control”.

The technical talks and tutorials were embraced by various technical visits and social events, among which the gala dinner on July 13th, a garden party with 3000 participants, was clearly the highlight of the week with tasty drinks and French cuisine. Some of the dyn members used the occasion to enjoy the French national holiday on July 14th with its famous fireworks in the city together with former group members, new friends and colleagues.

More pictures of the IFAC 2017 WC can be found here.

DYN papers at IFAC 2017

  • J. Cadavid, R. Hernandez, S. Engell, Speed-Up of Iterative Real-Time Optimization by Estimating the Steady States in the Transient Phase Using Nonlinear System Identification [Presentation Slides | Abstract]
  • W. Gao, R. Hernandez, S. Engell, Real-Time Optimization of a Novel Hydroformylation Process by Using Transient Measurements in Modifier Adaptation [Presentation slides | Abstract]
  • A.R. Gottu Mukkula, R. Paulen, Model-Based Optimal Experiment Design for Nonlinear Parameter Estimation Using Exact Confidence Regions [Presentation slides | Abstract]
  • L. S. Maxeiner, S. Engell, Hierarchical MPC of Batch Reactors with Shared Resources [Abstract]
  • S. Nazari, S. Wenzel, L. S. Maxeiner, C. Sonntag, S. Engell, A Framework for the Simulation and Validation of Distributed Control Architectures for Technical Systems of Systems [Presentation slides | Abstract]
  • S. Subramanian, S. Lucia, S. Engell, An Improved Output Feedback MPC Scheme for Constrained Linear Systems [Abstract]
  • S. Wenzel, R. Paulen, B. Beisheim, S. Krämer, S. Engell, Adaptive Pricing for Optimal Resource Allocation in Industrial Production Sites [Abstract]


Abstracts

J. Cadavid, R. Hernandez, S. Engell, Speed-Up of Iterative Real-Time Optimization by Estimating the Steady States in the Transient Phase Using Nonlinear System Identification

Keywords: Real time optimization and control, Process optimisation, Process modeling and identification

Abstract: Iterative Real-Time Optimization (RTO) has gained increasing attention in the context of model-based optimization of the operating points of chemical plants in the presence of plant-model mismatch. In all iterative RTO schemes, it is necessary to wait until the plant has reached a steady-state to obtain the required information on plant performance and constraint satisfaction which leads to slow convergence in the case of processes with slow dynamics. It has recently been proposed to use a linear black-box model that is identified online to predict the steady-state values of the plant during the transient between different stationary operating points; these values are then employed in the modifier adaptation with quadratic approximation to drive the process to its optimum. In this contribution, this idea is extended by integrating nonlinear system identification into iterative RTO. Specifically, a Nonlinear Output Error (NOE) model is proposed to describe the dynamics of the process, thus providing a faster prediction of the steady-state of the plant. A robust scheme for the estimation of the model parameters is proposed. The performance of the strategy is illustrated by simulation studies of a continuous stirred-tank reactor. By means of the proposed methodology a fast convergence to the plant optimum can be achieved despite plant-model mismatches

 

W. Gao, R. Hernandez, S. Engell, Real-Time Optimization of a Novel Hydroformylation Process by Using Transient Measurements in Modifier Adaptation

Keywords: Real time optimization and control

Abstract: This paper deals with the use of transient measurements in the steady-state optimization of a hydroformylation process in the presence of plant-model mismatch. The key idea is to predict steady states from the measurements obtained during the transients of the process. The predicted steady states are then employed in the modifier adaptation with quadratic approximation algorithm to drive the operation of the process to its economic optimum. Simulation results show the promising performance of the proposed scheme.

 

A.R. Gottu Mukkula, R. Paulen, Model-Based Optimal Experiment Design for Nonlinear Parameter Estimation Using Exact Confidence Regions

Keywords: Process modeling and identification, Model predictive and optimization-based control, Estimation and fault detection

Abstract: Optimal experiment design is usually performed as a search over a finitely-parameterized shape that (over-)approximates the confidence region of parameters of a model. In general, there exists no such shape to exactly enclose the confidence region of a nonlinear parameter estimation problem. Due to this fact, the design-of-experiment techniques are not well established for this problem and approximate designs are conducted. In this contribution, assuming Gaussian (normally distributed) noise, we propose and study (a) two schemes to over-approximate the confidence region of parameters using an ellipsoid and an orthotope and (b) a framework for optimal experiment design. We formulate the over-approximation of the confidence region as an optimization problem. The optimal experiment design is then proposed as a bi-level optimization problem. In line with the existing optimal experiment design methodology for a linear parameter estimation problem, we also propose several design criteria that optimize some measure of the over-approximated confidence region for the nonlinear case. The proposed bi-level optimization problem is solved (i) as a nonlinear programming problem using the necessary conditions for optimality or (ii) as a nested problem with globally optimized inner-level problem. We illustrate the proposed schemes on a benchmark test case.

 

L. S. Maxeiner, S. Engell, Hierarchical MPC of Batch Reactors with Shared Resources

Keywords: Distributed nonlinear control

Abstract: Multi-reactor semi-batch plants are widespread in the chemical and pharmaceutical industry, since they can produce more flexibly and depending on the product also more profitably than continuous plants. Such reactors usually share resources that are constrained, as for instance raw materials or cooling and heating media. In order to maximize the productivity while respecting product quality constraints, equipment limitations, as well as constraints on the utilization of the shared resources, the feeding policies and temperature profiles in such semi-batch reactors are increasingly optimized online. This can be done by a plant-wide optimizing controller, however, due to robustness, flexibility, and reduced computational effort, distributed schemes that solve the combined trajectory optimization and resource assignment problem are of high interest. In this contribution, we present a hierarchical model predictive control scheme that computes cost and resource optimal trajectories online for a set of semi-batch reactors. Each reactor maximizes its profit function locally by maximizing the amount of product while incurring a cost for the use of the shared resources. On the coordinator level, the future availability of the shared resources is taken into account and the prices are adjusted iteratively, such that the feasibility of the joint operation of all reactors is guaranteed. We show that the problem can be solved using the alternating direction method of multipliers (ADMM), modified for inequality constraints, and compare the performance of this scheme to a decentralized one. Furthermore, the need to coordinate over the whole prediction horizon is discussed and a reduced coordination horizon and its selection are investigated.

 

S. Nazari, S. Wenzel, L. S. Maxeiner, C. Sonntag, S. Engell, A Framework for the Simulation and Validation of Distributed Control Architectures for Technical Systems of Systems

Keywords: Process control applications, Control of large-scale systems, Control of distributed systems

Abstract: There are many examples of modern technical systems that consist of many closely integrated physical and cyber subsystems. Centralized management and control strategies may not always be the best option, or may even be infeasible, for these so-called cyber-physical systems of systems (CPSoS) due to their large complexity or due to confidentiality restrictions between the subsystems. These challenges can be overcome by distributed management and coordination strategies that do not require a central controller. Instead, each subsystem solves its own local optimization problem and only reports limited information to a higher-level controller to ensure the satisfaction of the global constraints which are often defined as limitations on the utilization or production on some shared resources or raw materials. This paper presents a novel Modelica-based software framework that supports the development of such distributed management and control systems by providing a structured, plug-and-play approach for the validation of distributed management architectures on simulation models of the controlled CPSoS, and for the subsequent deployment into operational environments. The capabilities of the framework are demonstrated on two complex, industrial use cases, the distributed management of an integrated chemical production site and the distributed optimization of a network of multi-product semi-batch reactors.

 

S. Subramanian, S. Lucia, S. Engell, An Improved Output Feedback MPC Scheme for Constrained Linear Systems

Keywords: Output feedback control (linear case), Robust control (linear case), Linear multivariable systems

Abstract: We propose an output feedback model predictive control (MPC) scheme that is independent of the estimation method employed. The proposed approach is non-conservative by design as the feedback information subject to estimation errors is explicitly considered in the predictions. The proposed scheme is robust to both the plant-model mismatch and the estimation error and avoids the unnecessary conservatism introduced by other existing output feedback MPC approaches in the literature. We achieve this by making use of all the available information at every time-step and formulate a control policy with the assumption that bounds for the estimation error are available at all times. We show that the proposed scheme is recursively feasible and demonstrate its advantages using a double-integrator example.S. Wenzel, R. Paulen, B. Beisheim, S. Krämer, S. Engell, Adaptive Pricing for Optimal Resource Allocation in Industrial Production Sites

 

S. Wenzel, R. Paulen, B. Beisheim, S. Krämer, S. Engell, Adaptive Pricing for Optimal Resource Allocation in Industrial Production Sites

Keywords: Control of distributed systems, Industrial applications of process control, Control of large-scale systems

Abstract: In large integrated production sites, an optimal allocation of the shared resources among different possibly competing production plants is key to a resource and energy efficient operation of the overall site. Typically, a large integrated production site can be regarded as a physically coupled system of systems (SoS), since it comprises many different physically linked production plants with a certain degree of autonomy in respect to the individual operating conditions where the plants tend to pursue their own economic goals and interests. In order to improve the overall operation of the production site, a centralized optimization for the shared resource allocation within the site is favored. However, a centralized solution cannot always be realized due to various technical or managerial reasons. One of the reasons is the limited amount of information about the individual subsystems that a central site management can access, because the subsystems want to preserve a high level of confidentiality. In this contribution, we present the application of price-based coordination subgradient-based price updates and the Alternating Direction Method of Multipliers, ADMM) to the case study of the integrated petrochemical production site of INEOS in Köln. We discuss the requirements of the price-based coordination for industrial applicability in the case of limited sharing of information. In a simulation study, we show how the central site management uses price incentives to steer the individual productions plants towards a site-optimal operation and thus is able to react to changing conditions such as capacity changes.