Želimir Kurtanjek

Gas permeability and DSC characteristics of polymers used in food packaging

The work is an experimental study of gas permeability and DSC characterisation of polymers used in food packaging. For this purpose polyethylene (0.03 and 0.1 mm), polypropylene (0.03 mm), and coextruded polypropylene (0.1 mm) were studied. The permeabilities of polymers to oxygen and nitrogen were measured at 20 oC, 40 oC and 60 oC using the manometric method. From the obtained permeability data, permeation, diffusion and solubility constants were calculated while temperatures and enthalpies of the phase change were determined by the DSC method. Permeability of oxygen and nitrogen increase with temperature for all investigated samples. Permeation data for the polypropylene (0.03 mm) and polyethylene (0.03 mm) showed good agreement with the Arrhenius equation. Upon temperature increase starting from 40 0C all samples undergo phase change in the temperature range from 107 0C to 173 0C with enthalpies in the range of 40 to 85 J g^-1. Experiments, where simulated conditions of food sterilisation were used as well as exposition to radiation in a microwave oven, showed negligible changes in DSC indicating structural stability of the polymers.

Modeling and kinetic parameter estimation of alcohol dehydrogenase-catalyzed hexanol oxidation in a microreactor

A mathematical model for hexanol oxidation catalyzed by NAD+‐dependent alcohol dehydrogenase from bakers yeast in a microreactor was developed and compared with the model when the reaction takes place in a macroscopic reactor. The enzyme kinetics was modeled as a pseudo‐homogeneous process with the double substrate Michaelis–Menten rate expression. In comparison with the kinetic parameters estimated in the cuvette, a 30‐fold higher maximum reaction rate and a relatively small change in the saturation constants are observed for the kinetic parameters estimated in the continuously operated tubular microreactor (Vm1=197.275 U/mg, Kmhexanol=9.420 mmol/L, and Km1NAD+=0.187 mmol/L). Kinetic measurements performed in the microreactor, estimated from the initial reaction rate experiments at the residence time of 36 s, showed no product inhibition, which could be explained by hydrodynamic effects and the continuous removal of inhibiting products. The Fourier amplitude sensitivity test method was applied for global kinetic parameter analysis, which shows a significant increase in the sensitivity of Km1NAD+ in the microreactor. Independent experiments performed in the microreactor were used to validate and to verify the developed mathematical model.

Enhancement of phenolic compounds oxidation using laccase from Trametes versicolor in a microreactor

Laccases catalyse the oxidation of a wide range of substrates by a radical-catalyzed reaction mechanism, with a corresponding reduction of oxygen to water in a four-electron transfer process. Due to that, laccases are considered environmentally friendly enzymes, and lately there has been great interest in their use for the transformation and degradation of phenolic compounds. In this work, enzymatic oxidation of catechol and L-DOPA using commercial laccase from Trametes versicolor was performed, in continuously operated microreactors. The main focus of this investigation was to develop a new process for phenolic compounds oxidation, by application of microreactors. For a residence time of 72 s and an inlet oxygen concentration of 0.271 mmol/dm3, catechol conversion of 41.3% was achieved, while approximately the same conversion of L-DOPA (45.0%) was achieved for an inlet oxygen concentration of 0.544 mmol/dm3. The efficiency of microreactor usage for phenolic compounds oxidation was confirmed by calculating the oxidation rates; in the case of catechol oxidation, oxidation rates were in the range from 76.101 to 703.935 g/dm3/d (18–167 fold higher, compared to the case in a macroreactor). To better describe the proposed process, kinetic parameters of catechol oxidation were estimated, using data collected from experiments performed in a microreactor. The maximum reaction rate estimated in microreactor experiments was two times higher than one estimated using the initial reaction rate method from experiments performed in a cuvette. A mathematical model of the process was developed, and validated, using data from independent experiments.

Principal component ANN for modelling and control of baker’s yeast production

Modelling of bakers yeast production by the principal component based artificial neural networks (ANN) is presented. The models are derived for their application in adaptive control of fermentation by the internal model control (IMC) method. Modelling data are from industrial production in 40 m3 deep jet bioreactor and from computer simulations. The modelling effort is focused on selection of ANN structure and model verification. Principal component analysis of process variables results in projection of patterns to a space of low dimension, which enables determination of ANN structure, removes data colinearity and random components of measurement signals, and model degradation by over-training is eliminated. In view of IMC application, the models for prediction of the controlled variable (ethanol partial pressure) and the inverse model for manipulative variable (molasses feed rate) are determined. The models are tested for their predictability in the time horizon from 1 to 20 min. ANN models are derived with average relative errors for untrained patterns in the range from 1 to 10%.

Application of Fuzzy Logic in Diet Therapy - Advantages of Application

Computing, in its usual sense, is centred on manipulation of numbers and symbols. In contrasts, computing with linguistic variables is a methodology in which the object of computation are words and propositions drawn from a natural language, e.g., significant increase in price, small, large, far from recommendations, etc. Computing with words is inspired by the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computation. A basic difference between perception and measurements is that, in general, measurements are crisp whereas perceptions are fuzzy. Instead of Boolean logic, fuzzy logic uses a collection of fuzzy variables defined by membership functions and inference rules. Most of traditional tools for formal modelling, reasoning, and computing are crisp, deterministic, and precise in character. This methodology is a part of mathematical theories of artificial intelligence. Human nutrition, notably the diet evaluation, considering the daily intake of energy and nutrients, is often explained by computing with words, for instance the final conclusion regarding an analysed diet plan can result with phrases as: “the intake of Na should be considerably reduced” or “the consumption of fruits and vegetables must be increased”. The theory of fuzzy logic was used in the planning and management of expenses in social nourishment concerning also the nutritive structure of meals. Modelling and planning of nourishment includes a number of unspecified characteristics, which are depended on nutrient offer and also on age, gender and profession of a person (concerning the physical activity level) or population group. Some recommended nutrient and energy intakes are given as single numbers (crisp values). But for most nutrients are also given the average requirements (AR), the lowest threshold intake (LTI) and the calculated population reference intake (PRI). These intervals and the values of LTI, AR and PRI do not represent the full reality, which is a continuous transition from critical low intake to adequate intake to excess or even toxic amounts. In this work the daily recommendations as crisp numbers are modelled as fuzzy sets. The daily recommended intake (DRI) for each observed nutrient and energy intake is “softened” by introduction of membership function of fuzzy sets defined for each individual nutrient

Modeling of chemical reactor dynamics by nonlinear principal components

Abstract The modelling of nonisothermal continuous stirred chemical reactor dynamics by linear and nonlinear principal components methods is investigated. The derived models are analysed with respect of their ability to predict the existence of the reactor multiple steady states and their use for adaptive on-line process control. The time evolution of the state variables is approximated by a single-step finite difference prediction equation. Nonlinear principal components are determined by a feedforward neural network with a single hidden layer. Input and output patterns are jointly projected to a two dimensional surface yielding an implicit process model. The ability of implicit models to predict controlled and manipulative variables without the need for separate model development for the direct and inverse models makes them ideally applicable in adaptive internal model control loops. The model correctly predicts the existence of three steady states and provides an excellent fit to untrained samples of patterns under various dynamic conditions. The linear models based on a partial least squares algorithm can correctly model behaviour under unsteady conditions, but they fail to predict multiple steady states in chemical reacting systems. Since accurate model of steady-state properties is essential for process control, linear principal component models are inadequate when multiple steady states exist.

Mass transfer coefficient of slug flow for organic solvent-aqueous system in a microreactor

Application of microreactor systems could be the next break-through in the intensification of chemical and biochemical processes. The common flow regime for organic solvent-aqueous phase two-phase systems is a segmented flow. Internal circulations in segments cause high mass transfer and conversion. We analyzed slug flow in seven systems of organic solvents and aqueous phase. To analyze how slug lengths in tested systems depend on linear velocity and physical and chemical properties of used organic solvents, regression models were proposed. It was shown that models based on linearization of approximation by potentials give low correlation for slug length prediction; however, application of an essential nonlinear model of multiple layer perceptron (MLP) neural network gives high correlation with R2=0.9. General sensitivity analysis was applied for the MLP neural network model, which showed that 80% of variance in slug length for the both phases is accounted for the viscosity and density of the organic phases; 10% is accounted by surface tension of the organic phase, while molecular masses and flow rates each account for 5%. For defined geometry of microreactor, mass transfer has been determined by carrying out the neutralization experiment with NaOH where acetic acid diffuses from organic phase (hexane) into aqueous phase. Estimated mass transfer coefficients were in the range kLa=4,652-1,9807 h−1.

Chemometric versus Random Forest Predictors of Ionic Liquid Toxicity

Interest in ionic liquids ILs stems from their unique solvent properties and potential process “self-containment”. Their application in chemical processes and biotransformations provides the possibility for clean manufacturing (“green technology”). Besides their solvent and extraction functions, ILs also exhibit synergy effects with catalysts (enzymes) yielding higher production productivity. Theoretically, there is a limitless number of possible ILs with a very broad range of physical and chemical properties. Research on ILs has become one of the most interesting application research areas in novel catalytic synthesis, biofuel production from agricultural wastes, integration of chemical and enzyme reactors with separation processes, polymerization, nanotechnology, enzyme-catalysis, composite preparation and renewable resource utilization1–3. Especially interesting is the use of microreactors for ionic liquid synthesis and possibly as production systems for integrated biotransformations and product separation4. However, the recent questions of ILs’ eco-toxicity and their degradability have also been raised. Analysis of their versatile structure is formally viewed as a combinatorial problem which can be effectively accounted by computers. The object of this work is to apply computer modeling by chemometric methodology and decision tree algorithm for predicting continuous variables, such as toxicity level concentration EC50 and level classification, based on the choice of cation and anion structure and their chemical compositions. Predictions of ILs physical properties are based on literature published data and internet available NIST and MERCK databases of physical properties and cytotoxicity5–7. The main objective of this work is in inferring the rules and patterns implicitly contained in a set of chemical structures and molecular descriptors. Applied is a supervised learning algorithm with target sets for continuous and classification properties revealing relationships between molecular descriptors.