Beitrag zur Physiologie des N. Vagus. I. Mitteilung : Uber die Funktion des N. vagus auf die Atmung

1) Der Gaswechsel und der Respirationsquotient des Einrecurrenskaninchens (Lungenvagusfreienkaninchens, vgl. II. Mitteilung) scheint fast keinen Unterschied im Vergleich mit den normalen und Kontrolltieren zu zeigen. Beim Versuch wurde der von Saeki (1931) am hiesigen Institut verbesserte Haldensche Apparat angewendet. Aus diesem Resultat gelangt der Verfasser zu dem Schluss, dass die Ausschaltung des Lungenvagus auf den Gaswechsel und den Respirationsquotienten fast keinen Einfluss ausubt. 2) Bei der Durchschneidungsmethode, die sicher den Lungenvagus auschaltet, tritt immer die typische Vagusatmung auf. 3) Was die Vagusatmung des Einrecurrenskaninchens nach der Operation betrifft, so kehrt wieder allmahlich im Verlauf der Zeit ersichtlich der normale Atmungszustand zuruck, aber durch eine leichte Storung, z. B. durch die Anlegung der Gasmaske, kann die typische Vagusatmung hervorgerufen werden. Um an wiedererholte Erhaltung des normalen Atmungszustandes nach der Ausschaltung des Lungenvagus zu denken, muss man voraussetzen, dass es anstatt des Lungenvagus noch einen anderen Regulations-mechanismus fur den normalen Atmungszustand geben; falls der Regulationsmechanismus durch eine leichte Storung gestort wird, tritt die Vagusatmung auf. Es ist sehr schwer zu denken, an welchem Ort und auf welche Weise die Regulation ausgeubt wird, aber der Verfasser vermutet, dass das Atemzentrum durch die centripetalen Impulse von uberangestrengtem Atemmuskel und Zwerchfell nach der Lungenvagusaus-schaltung wieder die regulatorische centrifugale Wirkung zum Atemmuskel und Zwerchfell sendet, wodurch der Regulationsmechanismus entsteht. 4) Die Abnahme der Atmungszahl nach der doppelseitigen Vagotomie am Halse kann nicht durch die Schafersche Glottislahmungstheorie oder Hymansche Aorten-Sinusnervenlahmungstheorie erklart werden, sondern durch die Ausschaltung des Lungen-vagus selbst. 5) Beim Einrecurrenskaninchen sieht man keine Apnoe beim Einblasen der Lunge. Es gilt als eine Bestatigung dafur, dass alle Vagusaste nach der Lunge

GASDS: A kinetic-based package for biomass and coal gasification

In this paper, a simulation package called GASDS is introduced. It is particularly suited to evaluate the pyrolysis, gasification and combustion of biomass and coal feedstocks. The aim of this work is to describe the package from a numerical point of view and its interface. Additionally, experimental results for a countercurrent fixed-bed biomass gasification reactor are reproduced. The influence of reactor and particle discretizations are investigated with respect to accuracy and computational time. Some differences are present between experimental and simulation results. In order to improve the agreement between simulation and experimental results it is suggested to improve the kinetic scheme of the solid phase and gas-solid reactions. The negligible differences in terms of predictions, instead, do not justify the adoption of finer discretizations for the particle and reactor, which imply longer computational times

Numerical Methodologies and Algorithms for Optimal Experiment Design in the (Bio)chemical Industry

This thesis investigates optimal experiment design for parameter estimation of nonlinear dynamic systems in the (bio)chemical industry. A globalized market situation and sustainability aspects are a driver to improve the (bio)chemical industry's performance. A useful tool for this goal is the use of model based optimization techniques. However, before a model can be used in daily practice, the process has to be modeled accurately. This involves the selection of an appropriate model structure and the determination of accurate model parameter values. In this thesis, it is assumed that a correct model structure has already been determined. So, the focus of the dissertation is in the optimal design of experiments for obtaining accurate parameter estimates. As optimal experiment design is an optimization problem, a first challenge relates to the fast and efficient computation of an approximation of the parameter variance-covariance matrix which is needed in the objective function. A second challenge results from the fact that the optimized design typically depends on the current estimate of the parameters which are not exactly known. Consequently, a design has to be robust both with respect to the information content and with respect to constraint satisfaction. The information content in optimal experiment design is expressed in matrix form. However, in the optimization formulation a scalar function of the variance-covariance matrix is required. Hence, a third challenge concerns the question of how to select the most appropriate optimal experiment design criterion. Moreover, some design criteria pose computational challenges (e.g., with respect to differentiability). As a result, a fourth challenge involves the search for proper reformulations.In the first part of the thesis, the mathematical formulation of optimal experiment design for parameter estimation is presented. Several parameter variance-covariance matrix approximation techniques are discussed and formulated in an optimal control setting. In the second part of the thesis the propagation of uncertainty is tackled. A first contribution is a computational method for the approximation of the parameter variance-covariance matrix using Riccati equations. It is shown that the resulting approach is more efficient than the corresponding Fisher information matrix approach. A second contribution is an efficient expected value formulation related to the information content in an experiment. The proposed formulation can also be extended to be robust with respect to constraint satisfaction. The third part discusses how to select an appropriate criterion for optimal experiment design. A third contribution is the investigation of different possible choices as objective function in a multi-objective optimization framework such that the experimenter can make a well-informed choice. The fourth contribution is the formulation of optimal experiment design such that an improvement on a matrix inequality level is ensured. This approach also allows to avoid problems with respect to, e.g., differentiability of the minimum eigenvalue function. Several case studies from different fields have been used throughout the dissertation. The case study of a fed-batch bioreactor using Monod kinetics has been investigated in every contribution of the presented dissertation. Besides this model, a kinetic growth model in function of temperature from the field of predictive microbiology has been studied. Also, a Lotka Volterra model has been examined. A slightly larger chemical case study has been the Williams-Otto reactor.nrpages: 211status: publishe

Multi-purpose economic optimal experiment design applied to model based optimal control

© 2016 In contrast to classical experiment design methods, often based on alphabetic criteria, economic optimal experiment design assumes that our ultimate goal is to solve an optimization or optimal control problem. As the system parameters of physical models are in practice always estimated from measurements, they cannot be assumed to be exact. Thus, if we solve the model based optimization problem using the estimated, non-exact parameters, an inevitable loss of optimality is faced. The aim of economic optimal experiment design is precisely to plan an experiment in such a way that the expected loss of optimality in the optimization is minimized. This paper analyzes the question how to design economic experiments under the assumption that we have more than one candidate objective function. Here, we want to take measurements and estimate the parameters before we actually decide which objective we want to minimize.publisher: Elsevier articletitle: Multi-purpose economic optimal experiment design applied to model based optimal control journaltitle: Computers & Chemical Engineering articlelink: http://dx.doi.org/10.1016/j.compchemeng.2016.07.004 content_type: article copyright: © 2016 Published by Elsevier Ltd.status: publishe

Towards Quality by Design in Pharmaceutical Manufacturing: Modeling and Control of Air Jet Mills

Milling is an important step in pharmaceutical manufacturing as it not only determines the final formulation of the drug product, but also influences the bioavailability and dissolution rate of the active pharmaceutical ingredient (API). In this respect, the air jet mill (AJM) is most commonly used in the pharmaceutical industry as it is a non-contaminating and non-degrading self-classifying process capable of delivering narrow particle size distributions (PSD). Keeping the principles of Quality by Design in mind, the Critical Process Parameters (CPPs) of the AJM have been identified to be the pressures at the grinding nozzles, and the feed rate which affect the PSD, surface charge and the morphology of the product (i.e. the Critical Material Attributes (CMAs)). For the purpose of this research, the PSD is considered to be the only relevant CMA. A population balance based model is proposed to simulate the dynamics milling operation by utilizing the concept of breakage functions. This model agrees qualitatively with experimental observations of the air jet mill unit present at Janssen Pharmaceutica but further steps for model validation need to be carried out.status: publishe

Towards quality by design in pharmaceutical manufacturing: modelling and control of air jet mills

Milling is an important step in pharmaceutical manufacturing as it not only determines the final formulation of the drug product, but also influences the bioavailability and dissolution rate of the active pharmaceutical ingredient (API). In this respect, the air jet mill (AJM) is most commonly used in the pharmaceutical industry as it is a non-contaminating and non-degrading self-classifying process capable of delivering narrow particle size distributions (PSD). Keeping the principles of Quality by Design in mind, the Critical Process Parameters (CPPs) of the AJM have been identified to be the pressures at the grinding nozzles, and the feed rate which affect the PSD, surface charge and the morphology of the product (i.e. the Critical Material Attributes (CMAs)). For the purpose of this research, the PSD is considered to be the only relevant CMA. A population balance based model is proposed to simulate the dynamics milling operation by utilizing the concept of breakage functions. This model agrees qualitatively with experimental observations of the air jet mill unit present at Janssen Pharmaceutica but further steps for model validation need to be carried out

Dynamic optimization of biological networks under parametric uncertainty

Background: Micro-organisms play an important role in various industrial sectors (including biochemical, food and pharmaceutical industries). A profound insight in the biochemical reactions inside micro-organisms enables an improved biochemical process control. Biological networks are an important tool in systems biology for incorporating microscopic level knowledge. Biochemical processes are typically dynamic and the cells have often more than one objective which are typically conflicting, e.g., minimising the energy consumption while maximizing the production of a specific metabolite. Therefore multi-objective optimization is needed to compute trade-offs between those conflicting objectives. In model-based optimization, one of the inherent problems is the presence of uncertainty. In biological processes, this uncertainty can be present due to, e.g., inherent biological variability. Not taking this uncertainty into account, possibly leads to the violation of constraints and erroneous estimates of the actual objective function(s). To account for the variance in model predictions and compute a prediction interval, this uncertainty should be taken into account during process optimization. This leads to a challenging optimization problem under uncertainty, which requires a robustified solution. Results: Three techniques for uncertainty propagation: linearization, sigma points and polynomial chaos expansion, are compared for the dynamic optimization of biological networks under parametric uncertainty. These approaches are compared in two case studies: (i) a three-step linear pathway model in which the accumulation of intermediate metabolites has to be minimized and (ii) a glycolysis inspired network model in which a multiobjective optimization problem is considered, being the minimization of the enzymatic cost and the minimization of the end time before reaching a minimum extracellular metabolite concentration. A Monte Carlo simulation procedure has been applied for the assessment of the constraint violations. For the multi-objective case study one Pareto point has been considered for the assessment of the constraint violations. However, this analysis can be performed for any Pareto point. Conclusions: The different uncertainty propagation strategies each offer a robustified solution under parametric uncertainty. When making the trade-off between computation time and the robustness of the obtained profiles, the sigma points and polynomial chaos expansion strategies score better in reducing the percentage of constraint violations. This has been investigated for a normal and a uniform parametric uncertainty distribution. The polynomial chaos expansion approach allows to directly take prior knowledge of the parametric uncertainty distribution into account.status: publishe

Identifiability Analysis of Population Balance Model for Spiral Air Jet Mill

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