Production and characterization of lipid microparticles produced by spray cooling encapsulating a low molar mass hydrophilic compound

The objective of this research was to produce and characterize lipid particles (MpLs) that may be used as carriers of high amounts of hydrophilic core and evaluate the influence of the core amount on the performance of lipid microparticles. The MpLs were produced by spray cooling from solid and liquid lipid mixtures (stearic and oleic fatty acids and partly hydrogenated vegetable fat) containing glucose solution as core and soy lecithin as surfactant. The performance of MpLs was evaluated by means of the effective amount of encapsulated core, the core amount present on the surface of MpLs (superficial glucose) and the core release profile in aqueous solution. Morphological observations showed that MpLs presented spherical shape and a rugged and continuous surface, and an average diameter between 25 and 32 µm. The effective amount of encapsulated core was greater than 78% for all formulations evaluated. Larger amounts of superficial glucose were found in formulations in which more concentrated glucose solutions were used, regardless of the glucose lipid-solution ratio. The release results showed that core retention was significantly influenced by the glucose solution concentration, whereas release modulation was influenced by the glucose lipid-solution ratio.

Experimental results and modeling of poultry carcass cooling by water immersion

Poultry carcasses have to be chilled to reduce the central breast temperatures from approximately 40 to 4 °C, which is crucial to ensure safe products. This work investigated the cooling of poultry carcasses by water immersion. Poultry carcasses were taken directly from an industrial processing plant and cooled in a pilot chiller, which was built to investigate the influence of the method and the water stirring intensity on the carcasses cooling. A simplified empiric mathematical model was used to represent the experimental results. These results indicated clearly that the understanding and quantification of heat transfer between the carcass and the cooling water is crucial to improve processes and equipment. The proposed mathematical model is a useful tool to represent the dynamics of carcasses cooling, and it can be used to compare different chiller operational conditions in industrial plants. Therefore, this study reports data and a simple mathematical tool to handle an industrial problem with little information available in the literature.

Modeling the water uptake by chicken carcasses during cooling by immersion

In this study, water uptake by poultry carcasses during cooling by water immersion was modeled using artificial neural networks. Data from twenty-five independent variables and the final mass of the carcass were collected in an industrial plant to train and validate the model. Different network structures with one hidden layer were tested, and the Downhill Simplex method was used to optimize the synaptic weights. In order to accelerate the optimization calculus, Principal Component Analysis (PCA) was used to preprocess the input data. The obtained results were: i) PCA reduced the number of input variables from twenty-five to ten; ii) the neural network structure 4-6-1 was the one with the best result; iii) PCA gave the following order of importance: parameters of mass transfer, heat transfer, and initial characteristics of the carcass. The main contributions of this work were to provide an accurate model for predicting the final content of water in the carcasses and a better understanding of the variables involved.

Integration of cooking and vacuum cooling of carrots in a same vessel

Cooked vegetables are commonly used in the preparation of ready-to-eat foods. The integration of cooking and cooling of carrots and vacuum cooling in a single vessel is described in this paper. The combination of different methods of cooking and vacuum cooling was investigated. Integrated processes of cooking and vacuum cooling in a same vessel enabled obtaining cooked and cooled carrots at the final temperature of 10 ºC, which is adequate for preparing ready-to-eat foods safely. When cooking and cooling steps were performed with the samples immersed in boiling water, the effective weight loss was approximately 3.6%. When the cooking step was performed with the samples in boiling water or steamed, and the vacuum cooling was applied after draining the boiling water, water loss ranged between 15 and 20%, which caused changes in the product texture. This problem can be solved with rehydration using a small amount of sterile cold water. The instrumental textural properties of carrots samples rehydrated at both vacuum and atmospheric conditions were very similar. Therefore, the integrated process of cooking and vacuum cooling of carrots in a single vessel is a feasible alternative for processing such kind of foods.

Evaluation of a new mathematical model to describe Clostridium perfringens growth during the cooling of cooked ground beef

A mathematical model previously developed to study microbial growth in food products under an isothermal environment was adapted to a time-varying temperature regime. The resulting model was applied to study the growth of Clostridium perfringens in meat products. This micro-organism is of particular relevance to public health and economy due to the loss of productivity caused by it. Results showed a similar performance of the model used compared to the Baranyi model under an isothermal situation and a slightly better performance under a non-isothermal temperature profile.

Energy evaluation of an evaporative cooling system using water driven ejector

The search for efficient and accessible cooling systems has increased worldwide. This study aims to build and evaluate an evaporative cooling system using a water driven ejector, allowing it to be installed in places with plenty of water. The system was investigated varying the flow rate and temperature of the circulating water, temperature of the replacement water, and coefficient of performance. The best vacuum obtained was 8.5 kPa at nominal operating conditions of 4.1 ± 0.1 m³/h and 5 ± 0.5 ºC for the circulating water reaching the temperature of 9.7 ± 0.5 ºC. The pulse-like disturbance generated by replacing the cooling water at different periods of times did not result in significant affect vacuum destabilization and the temperature rise in the cooling tank. The coefficient of performance of the system at the highest thermal power of 92.27 W was 0.077, which was underestimated due to possible problems related to pump efficiency. The system evaluated under the conditions proposed can be very efficient for cooling fluids at higher temperatures, and it can be complementary to main refrigeration systems.

Adhesion and production of degrading enzymes by bacteria isolated from biofilms in raw milk cooling tanks

Biofilms in milk cooling tanks compromise product quality even on farms. Due to the lack of studies of this topic, this study evaluated the microbiological conditions of raw milk cooling tanks on farms and characterized the microorganisms isolated from these tanks. Samples were wiped off with sterile swabs from seven milk cooling tanks in three different points in each tank. Mesophiles and psychrotrophic counts were performed in all samples. The isolation of Pseudomonas spp., Bacillus cereus and atypical colonies formed on selective media were also performed, totalizing 297 isolates. All isolates were tested for protease and lipase production and biofilm formation. Of the total isolates, 62.9% produced protease, 55.9% produced lipase, and 50.2% produced biofilm. The most widespread genus inside the milk cooling tank was Pseudomonas since it was not possible to associate this contamination with a single sampling point in the equipment. High counts of microorganisms were found in some cooling tanks, indicating poor cleaning of the equipment and providing strong evidences of microbial biofilm presence. Moreover, it is worth mentioning the milk potential contamination with both microbial cells and their degrading enzymes, which compromises milk quality.

Can future systemic financial risks be quantified?: ergodic vs nonergodic stochastic processes

Different axioms underlie efficient market theory and Keynes's liquidity preference theory. Efficient market theory assumes the ergodic axiom. Consequently, today's decision makers can calculate with actuarial precision the future value of all possible outcomes resulting from today's decisions. Since in an efficient market world decision makers "know" their intertemporal budget constraints, decision makers never default on a loan, i.e., systemic defaults, insolvencies, and bankruptcies are impossible. Keynes liquidity preference theory rejects the ergodic axiom. The future is ontologically uncertain. Accordingly systemic defaults and insolvencies can occur but can never be predicted in advance.

Developing asset management in district heating and cooling business in a large energy company

The purpose of this Master´s Thesis is to develop asset management and its practices in case company. District heating and cooling systems operated by case company around Finland, Sweden, Poland and the Baltics form an enormous-sized asset base where some parts are starting to reach their end of life-cycles. Large-sized asset renewal actions are under discussion and maintenance spending is increasing. Financially justified decisions in changing business environment are needed. Asset management is one of the most important concepts for production organization which operates with capital-intensive production assets. Organizations profitability is highly dependent on assets´ performance. Such assets, like district heating and cooling systems, should be utilized as efficiently as possible within their life-cycles but also maintained and renewed optimally. In this qualitative thesis, empirical interview study was conducted to describe the current situation on how the assets are managed in the case company and to examine the readiness to implement a new, risk-based solution. Asset management revealed to be a very well-known concept. From proposed risk-based asset management point of view, several key observations were made. It was seen as a suitable solution, but further development will be needed. Based on the need and findings, several key processes and frameworks were created and also tested with a case study. Assets` condition monitoring should be improved, which would have a positive impact on event probability assessment. Risk acceptance is also a thing to be discussed further. When the evaluation becomes fluent in single investment cases, portfolio-level expansion should be considered and started. As a result, thesis proposes a solution how risk-based asset management could be performed practically in a capital-intensive case company in order to optimize the maintenance spending in a long run. Created practical framework is made universal: similar principles can be applied into multiple cases in case company but also in other energy companies. Risk-based asset management`s benefits could be utilized best in portfolio-level optimization where the capital would be invested to the most important objects from total risk point of view. Eventually, such approach would allow case company to optimize capital spending in a situation where funds are not adequate to cover all the mandatory needs and prioritization between the investment alternatives will truly be needed.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.

Stochastic particle models: mean reversion and burgers dynamics. An application to commodity markets

The aim of this study is to propose a stochastic model for commodity markets linked with the Burgers equation from fluid dynamics. We construct a stochastic particles method for commodity markets, in which particles represent market participants. A discontinuity in the model is included through an interacting kernel equal to the Heaviside function and its link with the Burgers equation is given. The Burgers equation and the connection of this model with stochastic differential equations are also studied. Further, based on the law of large numbers, we prove the convergence, for large N, of a system of stochastic differential equations describing the evolution of the prices of N traders to a deterministic partial differential equation of Burgers type. Numerical experiments highlight the success of the new proposal in modeling some commodity markets, and this is confirmed by the ability of the model to reproduce price spikes when their effects occur in a sufficiently long period of time.

Modelling the exothermic heat and the demand of cooling in paper and pulp industry biological wastewater treatment

This master thesis presents a study on the requisite cooling of an activated sludge process in paper and pulp industry. The energy consumption of paper and pulp industry and it’s wastewater treatment plant in particular is relatively high. It is therefore useful to understand the wastewater treatment process of such industries. The activated sludge process is a biological mechanism which degrades carbonaceous compounds that are present in waste. The modified activated sludge model constructed here aims to imitate the bio-kinetics of an activated sludge process. However, due to the complicated non-linear behavior of the biological process, modelling this system is laborious and intriguing. We attempt to find a system solution first using steady-state modelling of Activated Sludge Model number 1 (ASM1), approached by Euler’s method and an ordinary differential equation solver. Furthermore, an enthalpy study of paper and pulp industry’s vital pollutants was carried out and applied to revise the temperature shift over a period of time to formulate the operation of cooling water. This finding will lead to a forecast of the plant process execution in a cost-effective manner and management of effluent efficiency. The final stage of the thesis was achieved by optimizing the steady state of ASM1.

Exact property estimation from diffusion Monte Carlo with minimal stochastic reconfiguration /

Our objective is to develop a diffusion Monte Carlo (DMC) algorithm to estimate the exact expectation values, ($o|^|^o), of multiplicative operators, such as polarizabilities and high-order hyperpolarizabilities, for isolated atoms and molecules. The existing forward-walking pure diffusion Monte Carlo (FW-PDMC) algorithm which attempts this has a serious bias. On the other hand, the DMC algorithm with minimal stochastic reconfiguration provides unbiased estimates of the energies, but the expectation values ($o|^|^) are contaminated by ^, an user specified, approximate wave function, when A does not commute with the Hamiltonian. We modified the latter algorithm to obtain the exact expectation values for these operators, while at the same time eliminating the bias. To compare the efficiency of FW-PDMC and the modified DMC algorithms we calculated simple properties of the H atom, such as various functions of coordinates and polarizabilities. Using three non-exact wave functions, one of moderate quality and the others very crude, in each case the results are within statistical error of the exact values.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.