Publicaties

© 2015 Elsevier B.V. The manufacturing industry is faced with the challenge to constantly improve its processes under more and more stringent conditions, e.g., due to more strict environmental policies, lower profit margins and increased societal awareness. These three aspects are considered as the pillars of sustainable development and typically give rise to multiple and conflicting objectives. Hence, any decision taken will require trade-offs ...

© 2014 Elsevier Ltd. Dynamic experiments that yield as much information as possible are highly valuable for estimating parameters in nonlinear dynamic processes. Techniques for model-based optimal experiment design enable to systematically design such experiments. However, these experiments depend on the current best estimate of the parameters, which are not necessarily the true values. Consequently, in real experiments (i) the information ...

© 2014 IEEE. Economic Model Predictive Control (EMPC) is an advanced receding horizon based control technique which optimizes an economic objective subject to potentially nonlinear dynamic equations as well as control and state constraints. The main contribution of this paper is an algorithmic differentiation (AD) based real-time EMPC algorithm including a software implementation in ACADO Code Generation. The scheme is based on a novel memory ...

Traditionally, high-grade poly silicon is produced in the so-called "Siemens" reactor. The process is based on the Chemical Vapor Deposition (CVD) of silicon from a gaseous mixture of silanes and hydrogen on silicon rods. To obtain the crystal growth on the rod surface high temperatures are needed. The rods are heated internally by the Joule effect, i.e., an electrical current induces heating due to the electrical resistance of the silicon ...

Nonlinear Model Predictive Control (NMPC) is a powerful technique that can be used to control many industrial processes. Different and often conflicting control objectives, e.g., reference tracking, disturbance rejection and minimum control effort, are typically present. Most often these objectives are translated into a single weighted sum (WS) objective function. This approach is widespread because it is easy to use and understand. However, ...

© 2014 IEEE. Experiments that yield as much information as possible are highly valuable for estimating parameters in nonlinear dynamic processes. Techniques for model based optimal experiment design enable the design of such experiments. However, these experiments depend on the current best estimate of the parameters, which are not necessarily the true values. Consequently, in real experiments (i) the information content can be lower than ...

This paper is about optimal experiment design for uncertain nonlinear dynamic processes. We are interested in designing experiments which allow to identify the unknown states and parameters of a differential equation from noisy measurements. Here, unpredictable process noise or structural model-plant mismatches can be an additional complication. In this case, robustness aspects have to be taken into account as the experiment has to be planned ...

This study deals with the optimization of photochemical reactors for advanced oxidationprocesses (AOPs). The optimization consists of minimizing the average reactor outlet concentrationof an organic pollutant in a water flow by optimizing the reactor geometry and is based onmathematical reactor models. Two tubular reactor configurations are studied: a single- and a multilampreactor. For given values of the reactor volume, the flow of the water ...

The dynamic optimisation and optimal control of innovative cyclic chemical processes are challenging because these problems involve apart from temporal periodicity constraints often also Partial Differential Equation (PDE) models and multiple objectives. To allow a fast and efficient generation of the Pareto set, this paper presents a deterministic approach that integrates accurate scalarisation methods for multi-objective optimisation as Normal ...

The current study investigates the optimal operation of an air-to-water heat pump system. To this end, the control problem is formulated as a classic optimal control or dynamic optimization problem. As conflicting objectives arise, namely minimizing energy cost while maximizing thermal comfort, the optimization problem is tackled from a multi-objective optimization perspective. The adopted system model incorporates the building dynamics and the ...