Martin von Kurnatowski

dmaic software GmbH

The Kruskal-Segur approach to selection theory in diffusion-limited or Laplacian growth is extended via combination with the Zauderer decomposition scheme. This way nonlinear bulk equations become tractable. To demonstrate the method, we apply it to two-dimensional crystal growth in a potential flow. We omit the simplifying approximations used in a...

The oxygen reduction reaction (ORR) mechanism for electrochemical production of hydrogen peroxide is investigated for an Au-Pd catalyst with 25 at% palladium. The sample was prepared by chemical reduction of a precursor solution on a carbon-based substrate. Rotating ring-disk electrode (RRDE) measurements were performed for electrochemical characte...

Commercially available flow sheet simulators cannot perform optimization runs with more than one criterion. To overcome this problem, the flow sheet simulator CHEMCAD is combined with an external optimization solver, and the Excel VBA client is used to arrange inter-process communications. The resulting tool for multi-criteria optimization in chemi...

Process design based on physical models often faces computational problems with respect to convergence, especially if the underlying flowsheets are complex. The use of data-driven surrogate models promises to overcome these challenges. This contribution presents the development of surrogate models and their use for flowsheet simulation. A new sampl...

Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the availab...

Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. T...

Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process c...

Process simulation based on physical models often faces computational problems with respect to convergence, especially if the underlying flowsheets are complex. The use of data‐driven surrogate models connected to flowsheets promises to overcome these challenges. Using the steam methane reforming process, this paper presents the development of surr...

We present a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints such as monotonicity or convexity constraints. Incorporating such information is particularly beneficial when the available data sets are sparse. We set up the regression subject to the considered shape constr...

Dividing wall columns operated close to the energetic optimum can achieve their product specification within finite ranges of the liquid and vapor split. This split flexibility is important because it enables a stable operation in practice. The impact of non-optimal stage allocations on the split flexibility, which has not been treated satisfactori...

This article introduces a novel laboratory-scale process for the electrochemical synthesis of hydrogen peroxide (H2O2). The process aims at an energy-efficient, decentralized production, and a mathematical optimization of it is presented. A dynamic, zero-dimensional mathematical model of the reactor is set up in Aspen custom modeler®. The proposed...

We present a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available data sets are small or do not cover the entire input space, as is often the case in man...

Multi-objective optimization of distillation configurations is a difficult problem that can lead to significantly improved process designs. The identification of improvements depends on a reliable and precise calculation of the Pareto points, and the ability to visualize them in potentially high-dimensional spaces. This paper presents a methodology...

Many thermodynamic models used in practice are at least partially empirical and thus require the determination of certain parameters using experimental data. However, due to the complexity of the models involved as well as the inhomogeneity of available data, a straightforward application of basic methods often does not yield a satisfactory result....

Pareto-Optimization in Chemical Processes

This book represents an advanced theoretical approach to the famous phenomenon of dendritic solidification patterns. The corresponding physical basics are explained and the applied method is introduced in detail. Most of the calculations are carried out explicitly and the elaborate analytical and numerical appendices promote a deep understanding. I...

Dendritic patterns frequently arise when a crystal grows into its own undercooled melt. Latent heat released at the two-phase boundary is removed by some transport mechanism, and often the problem can be described by a simple diffusion model. Its analytic solution is based on a perturbation expansion about the case without capillary effects. The le...

We use the method presented in M von Kurnatowski et al (2013 Phys. Rev. E 87 042405) to solve the nonlinear problem of free dendritic growth in an Oseen flow. The growth process is assumed to be limited by thermal transport via diffusion and convection. A singular perturbation expansion is treated to lowest nontrivial order in the framework of asym...