René Schenkendorf

Hochschule Harz · Department of Automation and Computer Sciences

In this paper, we present a systematic robust dynamic optimization framework applied to the benzaldehyde lyase-catalyzed carboligation of propanal and benzaldehyde to produce (R)-2-hydroxy-1-phenylbutan-1-one (BA). First, the elementary process functions approach was used to screen between different dosing concepts, and it was found that simultaneo...

Robust model-based process design in continuous pharmaceutical manufacturing aims to implement quality by design principles under uncertainty. Notably, various studies have discussed the back-off concept to solve the underlying robust optimization problem; however, for the concept to have practical value, its efficiency and convergence must be impr...

Chemical process engineering and machine learning are merging rapidly, and hybrid process models have shown promising results in process analysis and process design. However, uncertainties in first-principles process models have an adverse effect on extrapolations and inferences based on hybrid process models. Parameter sensitivities are an essenti...

In (bio)chemical process engineering, first-principles process models have played a central role for some time in better understanding, monitoring, and controlling these complex processes. Dynamic process models have become even more critical in the context of Industry 4.0 and the use of digital twins in the last decade. However, the quality and th...

Industry 4.0 has embraced process models in recent years, and the use of model-based digital twins has become even more critical in process systems engineering, monitoring, and control. However, the reliability of these models depends on the model parameters available. The accuracy of the estimated parameters is, in turn, determined by the amount a...

The analysis of process and equipment operational data in chemical engineering regularly requires a high level of expert knowledge. This work presents a Machine Learning-based approach to evaluate and interpret process data to support robust operation of a thermosiphon reboiler. By applying an outlier detection, potentially interesting and unstable...

The price of the currently best available antimalarial treatment is driven in large part by the limited availability of its base drug compound artemisinin. One approach to reduce the artemisinin cost is to efficiently integrate the partial synthesis of artemisinin starting from its biological precursor dihydroartemisinic acid (DHAA) into the produc...

The Process Analytical Technology initiative and Quality by Design paradigm have led to changes in the guidelines and views of how to develop drug manufacturing processes. On this occasion the concept of the design space, which describes the impact of process parameters and material attributes on the attributes of the product, was introduced in the...

Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty...

Considering the competitive and strongly regulated pharmaceutical industry, mathematical modeling and process systems engineering might be useful tools for implementing quality by design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However, a crucial task in modeling (bio)pharmaceutical manufacturing processes...

A model‐based uncertainty quantification (UQ) approach is applied to the manufacturing process of Lithium‐ion batteries (LIB). Cell‐to‐cell deviations and the influence of sub‐cell level variations of material and electrode properties on the cell performance are investigated experimentally and via modeling. Therefore, the electrochemical battery mo...

Enzyme catalyzed reactions are complex reactions due to the interplay of the enzyme, the reactants, and the operating conditions. To handle this complexity systematically and make use of a design space without technical restrictions, we apply the model based approach of elementary process functions (EPF) for selecting the best process design for en...

Active pharmaceutical ingredients (APIs) are highly valuable...

Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty...

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various metho...

In this contribution, we propose estimating the means and variances required for calculating back-off terms by using the point estimate method (PEM) as a highly efficient sampling strategy in robust process design. As case studies, we consider an upstream pharmaceutical process which involves the synthesis of 2-hydroxy-ketones via enzyme-catalyzed...

Model-based design of pharmaceutical manufacturing processes has received much interest in academia and industry. Model parameter uncertainties, however, might deteriorate the predicted process performance. Probability-based robust process design concepts as a countermeasure against uncertainties might be implemented. Here, parameter uncertainties...

The global transition to a clean and sustainable energy infrastructure does not stop at aviation. The European Commission defined a set of environmental goals for the “Flight Path 2050”: 75% CO2 reduction, 90% NOx reduction, and 65% perceived noise reduction. Hydrogen as an energy carrier fulfills these needs, while it would also offer a tenable an...

Micro‐bioreactors (MBRs) have become an indispensable part for modern bioprocess development enabling automated experiments in parallel while reducing material cost. Novel developments aim to further intensify the advantages as dimensions are being reduced. However, one factor hindering the scale‐down of cultivation systems is to provide adequate m...

Exploiting the information provided by (bio)chemical reaction networks has proved beneficial for process analysis and design. To this end, parameter uncertainties have to be included in the analysis and design of (bio)chemical processes to ensure reliable model‐based results. The goal is to investigate the impact of parameter correlations on (bio)c...

Predicting current and future states of rail infrastructure based on existing data and measurements is essential for optimal maintenance and operation of railway systems. Mathematical models are helpful tools for detecting failures and extrapolating current states into the future. This, however, inherently gives rise to uncertainties in the model r...

Currently applied methods for characterization and optimization of bio-pharmaceutical processes are still strongly empirical. This often involves Design of Experiment (DoE) methods that require a large number of time-consuming experiments and can hardly fulfill the requirements of ‘Quality by Design’ (QbD). Linking mathematical models with experime...

Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robus...

Model-based design has received considerable attention in biological and chemical industries over the last two decades. However, the parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies were introduced to solve the robust optimization pro...

This paper is concerned with a highly efficient active fault detection and isolation (FDI) framework. An auxiliary, fault-revealing input is derived by solving an optimization problem. As we implement a model-based approach, the active FDI framework is robustified against model parameter uncertainties, including parameter correlations which are com...

High product standards and cost-efficient robust process designs are key issues in the pharmaceutical industry and require thorough system understanding of the underlying physical phenomena. Model-based approaches have been proven favorable to gain valuable system insights. However, qualitative inferences can be drawn only if the model is adequatel...

Biologic drugs are promising therapeutics, and their efficient production is essential for a competitive pharma industry. Dynamic flux balance analysis (dFBA) enables the dynamic simulation of the extracellular bioreactor environment and intracellular fluxes in microorganisms, but it is rarely used for model-based optimization of biopharmaceutical...

Parameter uncertainties affect model-based system reliability analysis and may lead to safety issues in model-based process design. Global sensitivity analysis (GSA) is a valuable tool to quantify the influence of parameter uncertainties in the variation of the model output. However, GSA has not been widely employed in the field of chemical enginee...

In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 • C i...

High product standards and cost-efficient robust process designs are key issues in the pharmaceutical industry and require thorough system understanding of the underlying physical phenomena. Model-based approaches have been proven favorable to gain valuable system insights. However, qualitative inferences can be drawn only if the model is adequatel...

Since its development in the early 1990s [1], the Doyle-Newman model has been applied to a significant number of different Lithium-ion batteries. Whenever a model was validated with electrochemical measurements, certain model parameters were adjusted to fit the simulation to the experiment. In addition, simulation-based parameter estimation was use...

Robust optimization of dynamical systems requires the proper uncertainty quantification. Monte Carlo simulations and polynomial chaos expansion are frequently used methods for uncertainty quantification and have been applied to a number of problems in process design and optimization. Both methods, however, are either computationally prohibitive for...

The dynamic models used for biological and chemical process analysis and design usually include a significant number of uncertain model parameters. Sensitivity analysis is frequently applied to provide quantitative information regarding the influence of the parameters, as well as their uncertainties, on the model output. Various techniques are avai...

In the field of chemical engineering, mathematical models have been proven to be an indispensable tool for process analysis, process design, and condition monitoring. To gain the most benefit from model-based approaches, the implemented mathematical models have to be based on sound principles, and they need to be calibrated to the process under stu...

Parameter sensitivity analysis of mechanistic battery models has the power to quantify the individual and joint effects of parameters on the performance of lithium-ion cells. This information can be beneficial for industrial cell designs, cell testing, and battery management system (BMS) configurations. The numerical quantification of these paramet...

The railway and mass transportation system is composed of industrial goods with substantial capital investments and long life cycles. This applies to rolling stock like trains, locomotives, wagons, and even more to the infrastructure like signaling, catenary, tracks, bridges, and tunnels. The lifespan of rolling stock is 30 to 40 years while the in...

Pichia pastoris is an important host cell for the heterologous expression of recombinant proteins. In order to understand and design optimal biopharmaceutical processes with P. pastoris, unstructured and flux balance analysis (FBA) models have been developed which consider either extracellular fluxes or intracellular fluxes, respectively. In an att...

The application of robust model-based design concepts for complex chemical processes is limited due to the repeated cpu-intensive uncertainty quantification step for any new tested process design configuration. Therefore, an efficient One-Shot Sparse Polynomial Chaos Expansion (OS2-PCE) based process design framework is introduced in this work. The...

To gain profit from complex chemical processes, it is essential to ensure its proper operation, i.e. to avoid costly unexpected downtimes of underlying processing units. This paper explores a highly efficient active fault detection and isolation (FDI) framework, which facilitates the discriminability of a set of analysed model candidates including...

The efficient manufacturing of high quality and affordable biopharmaceuticals in Pichia pastoris requires the best bio-engineered configuration of the P. pastoris expression system in combination with a tailor-made bioreactor set-up. To this end, unstructured and structured models have been developed to facilitate the optimal production of biopharm...

Profit margins in the highly competitive pharmaceutical industry have been constantly declining over the last decades because of increasing research and development costs [1]. In the development and production of active pharmaceutical ingredients (APIs) biochemical pathways play an important role. Mathematical models have been proven beneficial to...

Today’s highly competitive pharmaceutical industry is in dire need of an accelerated transition from the drug development phase to the drug production phase. At the heart of this transition are chemical reactors that facilitate the synthesis of active pharmaceutical ingredients (APIs) and whose design can affect subsequent processing steps. Inspire...

The thermal behaviour of large-format cells is crucial for commercial cell designs and thermal management systems (TMS) in terms of cell performance and safety [1]. Individual and joint effects of these parameters on the cell performance are typically non-intuitive and difficult to quantify empirically. Here, an extensive model-based parameter sens...

Over the last decade there has been an increased interest in the pharmaceutical industry to shift the manufac- turing process of drugs from batch to continuous operation. The continuous manufacturing of pharmaceuticals provides significant benefits, e.g. savings in cost, time and materials - to name but a few. The implementation of a continuous man...

Over time railway networks have become complex systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures are made up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenanc...

A highly available infrastructure is a premise for capable railway operation of high quality. Therefore maintenance is necessary to keep railway infrastructure elements available. Especially switches are critical because they connect different tracks and allow a train to change its moving direction without stopping. Their inspection, maintenance an...

In recent years, the demands on railroad infrastructure operators have been rising by means of profitability, availability, safety, and punctuality. Here, the condition based preventive maintenance aims at strengthening the rail mode of transport through an optimized scheduling of maintenance actions taking account of the actual infrastructure cond...

Der vorliegende Bericht beschreibt Teile der Forschungsaktivitäten des Deutschen Zentrums für Luft- und Raumfahrt e.V. (DLR) im Rahmen des Projektes Next Generation Railway Systems II (NGRS2). Ziel der Arbeiten ist eine erhöhte Verfügbarkeit der Eisenbahninfrastruktur durch zielgerichtete, zustandsorientierte Instandhaltung der Feldelemente. Das DL...

In recent years, the demands on railroad infrastructure operators have been rising by means of profitability, availability, safety, and punctuality. Here, the condition based preventive maintenance aims at strengthening the rail mode of transport through an optimized scheduling of maintenance actions taking account of the actual infrastructure cond...

Rising demands on railroad infrastructure operator by means of profitability and punctuality call for advanced concepts of Prognostics and Health Management. Condition based preventive maintenance aims at strengthening the rail mode of transport through an optimized scheduling of maintenance actions based on the actual and prognosticated infrastruc...

Building on the two previous contributions to the subject of data driven, condition based maintenance in EI 11/14 and 03/15, the present article fulfills the last steps to a continuous supervision of infrastructure elements by diagnosis and forecasts. Causes of failures are detected in this way and it becomes easier to plan maintenance measures, wh...

Several informative characteristic features from sensor data must be evaluated to achieve a reliable, data-driven condition assessment of infrastructure elements. For the purpose of an efficient analysis, the information on these features may be reduced to a few significant ­ representative parameters. The present article focuses on how this can be...

Die gestiegenen und zum Teil konfliktären Anforderungen an die Eisenbahninfrastrukturbetreiber bezüglich Wirtschaftlichkeit und Verfügbarkeit haben unter anderem zu einem Umdenken im Instandhaltungsmanagement geführt. Es wurde erkannt, dass die zustandsabhängige präventive Instandhaltung einen wichtigen Beitrag leisten kann, den Verkehrsträger Schi...

A general framework to approach the challenge of uncertainty propagation in model based prognostics is presented in this work. It is shown how the so-called Point Estimate Meth- ods (PEMs) are ideally suited for this purpose because of the following reasons: 1) A credible propagation and represen- tation of Gaussian (normally distributed) uncertain...

The concept of differential flatness has been widely used for nonlinear controller design. In this contribution, it is shown that flatness may also be a very useful property for parameter identification. An identification method based on flat inputs is introduced. The treatment of noisy measurements and the extension of the method to delay differen...

Within the scope of the EU-sponsored AutoMain project and in cooperation with DB, the German Aerospace Centre (DLR) has developed methods to support decision making in network maintenance planning. With these tools, spontaneously appearing single failures will no longer require highly expensive emergency measures. The extended data-base achieved by...

Highly predictive mathematical models are of inestimable value in systems biology. Their application ranges from investigations of basic processes in living organisms up to model based drug design in the field of pharmacology. For the development of reliable models suitable model candidates and related model parameters have to be identified by mini...

The use of mathematical models is widely established in various fields of application. To name but a few of their major applications, mathematical models can improve the controller design of complex technical systems or are able to facilitate the understanding of highly complex biochemical systems. No matter what mathematical models are used for, h...

Mathematical models ensuring a highly predictive power are of inestimable value in systems biology. Their application ranges from investigations of basic processes in living organisms up to model based drug design in the field of pharmacology. For this purpose simulation results have to be consistent with the real process, i.e, suitable model param...

A precise estimation of parameters is essential to generate mathematical models with a highly predictive power. A framework that attempts to reduce parameter uncertainties caused by measurement errors is known as Optimal Experimental Design (OED). The Fisher Information Matrix (FIM), which is commonly used to define a cost function for OED, provide...

Using mathematical models for a quantitative description of dynamical systems requires the identification of uncertain parameters by minimising the difference between simulation