The master thesis deals with optimization of business processes in a telecommunication company views of knowledge management. For this purpose, attention is paid to the following areas, namely knowledge management, implementation methodologies of knowledge management, process analysis and process optimization. The theoretical part deals mainly with the link between knowledge management and ways of managing organizations, it presents a theoretical framework necessary for the practical part of the work, which is based on implementation of monitoring system using selected methodology in an environment of selected organization.

The master thesis deals with optimization of business processes in a food company. For this purpose the attention is paid to the three areas, namely process analysis, implementation of activity based costing model and business process optimization. The theoretical part describes the linkage between process analysis and activity based costing and acts as theoretical framework for practical part of the thesis. Presented cost model is implemented in existing company and information provided by model is used for business process optimization.

This diploma thesis focuses on the definition of process management, optimization of processes and methodologies of modeling processes. The practical part includes the optimization of system of management projects in selected IT companies due to compliance with standards to ISO 10006 and another standards of process.

Multi-User Multi-Input Multi-Output (MU-MIMO) systems are considered to be the sustainable technologies of the current and future of the upcoming wireless and mobile networks generations. The perspectives of these technologies under several scenarios is the focus of the present thesis. The initial system model covers the MU-MIMO, especially in the massive form that is considered to be the promising ideas and pillars of the 5G network. It is observed that the optimal number of users should be served in the time-frequency resource even though the maximum limitation of the MU-MIMO is governed by the total receiving antennas (K) is less than or equal to the base station antennas (M). The system capacity of the massive MIMO (mMIMO) under perfect channel state information (CSI) of uncorrelated channel is investigated and studied. Two types of precoders were applied, one is directly based on channel inversion, and the other uses the Eigen decomposition that is derived subject to the signal to a leakage maximization problem. The two precoders show a degree of equivalency under certain assumptions for the number of antennas at the user end. The convex optimization of multi-antenna networks to achieve the design model of optimum beamformer (BF) based on the uniform linear array (ULA) is studied. The ULA is selected for its simplicity to analyse many scenarios and its importance to match the future network applied millimetre wave (mmWave) spectrum. The maximum beams generated by the ULA are explored in terms of several physical system parameters. The duality between the MU-MIMO and ULA and how they are related based on beamformer operation are detailed and discussed. Finally, two approaches for overloaded systems are presented when the availability of massive array that is not guaranteed due to physical restrictions since the existence of a large number of devices will result in breaking the dimension rule (i.e., K ≤ M). As a solution, a low complexity users selection algorithm is proposed. The channel considered is uncorrelated with full and perfect knowledge at the BS. In particular, these two channel conditions may not be available in all scenarios. The CSI may be imperfect, and even the instantaneous form does not exist. A hybrid precoder between the mixed CSI (includes imperfect and statistical) and rate splitting approach is proposed to deal with an overloaded system under a low number of BS antennas. / Ministry of Higher Education and Scientific Research of Iraq

A busca por eficiência e qualidade esta presente nas usinas de produção de etanol. A demanda por novas tecnologias e soluções para problemas conhecidos é dinâmica e contínua. No processo de produção do etanol há a necessidade de se quantificar a qualidade do etanol produzido. Esta análise demanda procedimentos laboratoriais que impõe um tempo entre a coleta da amostra e o resultado das análises laboratoriais. Este tempo afeta a velocidade de reação do sistema de controle de produção a uma variação na qualidade do etanol. Quanto antes se atue na produção, menor é o retrabalho sobre o que foi produzido fora da especificação desejada e maior o rendimento e a produtividade do processo. Um dos indicadores de rendimento do processo de produção de etanol é a porcentagem de massa de etanol em relação à massa de água contida na solução. Este indicador é representado por \"m/m\", e significa grau alcoólico do etanol. Tabelas alcoométricas são utilizadas para determinar o valor do grau alcoólico, tendo como variáveis de entrada a temperatura e a densidade. O intervalo de temperatura onde as tabelas alcoométricas são válidas não é o mesmo que o processo industrial necessita. O processo industrial trabalha com temperaturas próximas a 90°C. Este fato impõe que as análises do grau alcoólico sejam feitas em laboratório, prejudicando o tempo de resposta ao processo industrial. Objetivando resolver este problema, este trabalho busca por meio da utilização de redes neurais determinar uma função matemática que possa ser capaz de indicar o valor do grau alcoólico do etanol de maneira precisa e sem que seja necessário o uso de consultas e aproximações por tabelas de referência. / The search for efficiency and quality is present in ethanol plants. The demand for new technologies and solutions to known problems is dynamic and continuous. In the process of producing ethanol is necessary to quantify the quality of the produced ethanol. This analysis requires laboratory procedures it takes a time between sample collection and results of laboratory analysis. This time affects the reaction speed of the system production control to a variation in the quality of ethanol. The sooner they are acting in the production, lower rework on what was produced outside the desired specification and higher efficiency and productivity of the process. One of the indicators of process performance ethanol is the percentage mass of ethanol relative to water diluted solution. This indicator is represented by \"m/m\" and means a degree alcoholic of ethanol. Alcohol tables are used to determine the amount of degree alcohol having as input variables the temperature and density. The temperature range where the alcohol tables are valid is not the same as the industrial process needs. The industrial process works at temperatures around 90°C. This fact requires the analysis of the degree of alcoholic of the ethanol are made in the laboratory, hampering the response time to the industrial process. Aiming to solve this fact, this paper aims through the use of neural networks for universal approximation theorem, find a mathematical function that can be implemented in a virtual sensor capable of indicating the value of the alcoholic content of ethanol.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Rice Husks (RH) can be regarded as an abundant lignocellulosic raw material in the Brazilian southern(se for somente regiões do RS, SC e PR fica South region, senão, tá ok) region, renewable and practically without economic added value able to be bioconverted into industrial and pharmochemical products of great interest. The main objective of this dissertation was to search the use of RH for the production of itaconic acid (IA) and, simultaneously, to contribute to the mitigation of a serious environmental problem arising from the improper disposal of the processing residues. For this, RHs were submitted to pressurized acid hydrolysis with diluted HNO3 and H3PO4in accordance with a statistical multivariate planning using Central Composite Rotatable Design (DCCR). The experiments showed that, at 145 °C and reaction time of 28 min, with 3,4% (v v-1) H3PO4, it was produced 44,4 g sugar L-1, by yield of 266,4 mg sugar g-1 RH. By using 3,76% (v v-1) HNO3, the best experimental was 135 ºC and reaction time of 35 min, resulting on a 42,0 g sugar L-1, by yield of 252,0 mg sugar g-1RH. Compared to the results obtained before in our group for chloridric RH hydrolysis, the outcomes of this work were better, considering the concomitant liberation of glucose, xylose and arabinose. However, no additional benefit was found by applying a RH pretreatment with NaOH and NH4OH. Previous fermentative tests were made to find the best conditions for the grown of the Aspergillus terreus (ATCC 7860) fungus in RH hydrolysate, submitting the fermentative process to a central compound experimental design in blocks, evaluating the variables initial pH, temperature, use of yeast extract (YE) and RH origin. 20 mL flasks were used for the batch experiments. The best experiment produced 1,9 g IA L-1, by yield of 132 mg AI g-1 sugars (or 11,4 mg AI g-1 RH), using chloridric hydrolysate (0,8% HCl v v-1), 145 ºC and reation time of 46 min; detoxification with CaO, fermentation medium 50:50 hydrolysate : potato dextrose solution, with 10,0 g YE L-1, initial pH 6,0, 152 h fermentation). The used analytical techniques were HPLC-DAD and -RID, and UV-visible spectrophotometry, including the figures-of-merit and validation of the developed methodologies. It was possible to demonstrate the viability of the production processes of IA from RH, in laboratory scale, contributing to the efforts to the development of the biotechnological research and scientific innovation. / A casca de arroz (CA) pode ser considerada matéria-prima lignocelulósica abundante na região sul-brasileira, renovável e quase sem valor comercial - passível de bioconversão em produtos de grande interesse industrial e farmoquímico. O principal objetivo deste trabalho foi investigar o aproveitamento da CA na produção de ácido itacônico (AI) e, simultaneamente, contribuir para a mitigação de grave problema ambiental oriundo da disposição imprópria deste resíduo de beneficiamento. Para tanto, a CA foi submetida à hidrólise ácida à pressão segundo planejamento estatístico multivariado, usando-se delineamento composto central rotacional (DCCR) para as hidrólises com HNO3 e H3PO4 diluídos. Os experimentos revelaram que, a 145 °C e 28 min de reação, com 3,4% (v v-1) H3PO4, foram produzidos 44,4 g de açúcar L-1, com rendimento de 266,4 mg de açúcar g-1 CA. Empregando-se 3,76% (v v-1) HNO3, a melhor condição experimental foi 135 ºC e 35 min de reação, produzindo-se 42,0 g açúcar L-1, com rendimento de 252,0 mg de açúcar g-1 CA. Comparados a resultados de hidrólises com HCl de trabalhos anteriores desenvolvidos no grupo, os resultados foram superiores, considerando-se a liberação concomitante de glicose, xilose e arabinose. Nenhum benefício adicional foi obtido, porém, com o pré-tratamento da CA com NaOH e NH4OH. Ensaios fermentativos prévios foram feitos com o intuito de encontrar-se as condições mais adequadas ao crescimento do fungo Aspergillus terreus (ATCC 7860) no hidrolisado de CA, submetendo-se o processo fermentativo a planejamento experimental composto central, em blocos, avaliando-se as variáveis pH inicial do meio, temperatura, uso de extrato de levedura (EL) e origem da CA. Utilizaram-se frascos de 20 mL para os experimentos em batelada. O melhor experimento foi aquele que produziu 1,9 g AI L-1, com rendimento de 132 mg AI g-1 açúcares (ou 11,4 mg AI g-1 CA), usando-se hidrolisado clorídrico (0,8% HCl v v-1), temperatura de 145 ºC e 46 min de reação; destoxificação com CaO, meio de fermentação 50:50 hidrolisado : solução de dextrose de batata, com 10,0 g EL-1, pH inicial 6,0, 152 h de fermentação). As técnicas analíticas utilizadas foram HPLC-DAD e -RID, e Espectrofotometria UV-vis, apresentando-se figuras-de-mérito e validação das metodologias desenvolvidas. Foi possível demonstrar a viabilidade do processo de produção de AI a partir da CA, em escala de laboratório, contribuindo-se para o esforço do desenvolvimento da pesquisa biotecnológica e da inovação científica.

A busca por eficiência e qualidade esta presente nas usinas de produção de etanol. A demanda por novas tecnologias e soluções para problemas conhecidos é dinâmica e contínua. No processo de produção do etanol há a necessidade de se quantificar a qualidade do etanol produzido. Esta análise demanda procedimentos laboratoriais que impõe um tempo entre a coleta da amostra e o resultado das análises laboratoriais. Este tempo afeta a velocidade de reação do sistema de controle de produção a uma variação na qualidade do etanol. Quanto antes se atue na produção, menor é o retrabalho sobre o que foi produzido fora da especificação desejada e maior o rendimento e a produtividade do processo. Um dos indicadores de rendimento do processo de produção de etanol é a porcentagem de massa de etanol em relação à massa de água contida na solução. Este indicador é representado por \"m/m\", e significa grau alcoólico do etanol. Tabelas alcoométricas são utilizadas para determinar o valor do grau alcoólico, tendo como variáveis de entrada a temperatura e a densidade. O intervalo de temperatura onde as tabelas alcoométricas são válidas não é o mesmo que o processo industrial necessita. O processo industrial trabalha com temperaturas próximas a 90°C. Este fato impõe que as análises do grau alcoólico sejam feitas em laboratório, prejudicando o tempo de resposta ao processo industrial. Objetivando resolver este problema, este trabalho busca por meio da utilização de redes neurais determinar uma função matemática que possa ser capaz de indicar o valor do grau alcoólico do etanol de maneira precisa e sem que seja necessário o uso de consultas e aproximações por tabelas de referência. / The search for efficiency and quality is present in ethanol plants. The demand for new technologies and solutions to known problems is dynamic and continuous. In the process of producing ethanol is necessary to quantify the quality of the produced ethanol. This analysis requires laboratory procedures it takes a time between sample collection and results of laboratory analysis. This time affects the reaction speed of the system production control to a variation in the quality of ethanol. The sooner they are acting in the production, lower rework on what was produced outside the desired specification and higher efficiency and productivity of the process. One of the indicators of process performance ethanol is the percentage mass of ethanol relative to water diluted solution. This indicator is represented by \"m/m\" and means a degree alcoholic of ethanol. Alcohol tables are used to determine the amount of degree alcohol having as input variables the temperature and density. The temperature range where the alcohol tables are valid is not the same as the industrial process needs. The industrial process works at temperatures around 90°C. This fact requires the analysis of the degree of alcoholic of the ethanol are made in the laboratory, hampering the response time to the industrial process. Aiming to solve this fact, this paper aims through the use of neural networks for universal approximation theorem, find a mathematical function that can be implemented in a virtual sensor capable of indicating the value of the alcoholic content of ethanol.

<div> <div> <div> <p>First-order methods such as Stochastic Gradient Descent are methods of choice for solving non-convex optimization problems in machine learning. These methods primarily rely on the gradient of the loss function to estimate descent direction. However, they have a number of drawbacks, including converging to saddle points (as opposed to minima), slow convergence, and sensitivity to parameter tuning. In contrast, second order methods that use curvature information in addition to the gradient, have been shown to achieve faster convergence rates, theoretically. When used in the context of machine learning applications, they offer faster (quadratic) convergence, stability to parameter tuning, and robustness to problem conditioning. In spite of these advantages, first order methods are commonly used because of their simplicity of implementation and low per-iteration cost. The need to generate and use curvature information in the form of a dense Hessian matrix makes each iteration of second order methods more expensive. </p><p><br></p> <p>In this work, we address three key problems associated with second order methods – (i) what is the best way to incorporate curvature information into the optimization procedure; (ii) how do we reduce the operation count of each iteration in a second order method, while maintaining its superior convergence property; and (iii) how do we leverage high-performance computing platforms to significant accelerate second order methods. To answer the first question, we propose and validate the use of Fisher information matrices in second order methods to significantly accelerate convergence. The second question is answered through the use of statistical sampling techniques that suitably sample matrices to reduce per-iteration cost without impacting convergence. The third question is addressed through the use of graphics processing units (GPUs) in distributed platforms to deliver state of the art solvers.</p></div></div></div><div><div><div> <p>Through our work, we show that our solvers are capable of significant improvement over state of the art optimization techniques for training machine learning models. We demonstrate improvements in terms of training time (over an order of magnitude in wall-clock time), generalization properties of learned models, and robustness to problem conditioning. </p> </div> </div> </div>

[ES] Reducir las emisiones de gases contaminantes de los motores de combustión interna alternativos (MCIA) es uno de los mayores retos para combatir el calentamiento global. Dado que los motores seguirán siendo utilizados por la industria durante décadas, es necesario desarrollar nuevas tecnologías. En este contexto, la presente tesis doctoral viene motivada por la necesidad de seguir mejorando los motores, tanto desde el punto de vista de la ingeniería técnica como desde el punto de vista social, debido a los efectos de los gases de efecto invernadero. El objetivo principal de esta tesis es desarrollar una metodología de optimización para sistemas de combustión de motores de encendido por compresión (MEC) mediante el acoplamiento de algoritmos de optimización con simulación por ordenador. Con la optimización de los sistemas de combustión es posible aumentar la eficiencia de los motores, reduciendo así el consumo de combustible junto con la reducción de emisiones contaminantes, en particular óxidos de nitrógeno (NOx) y hollín. En el primer paso, se abordan diferentes algoritmos de optimización con el fin de elegir el mejor candidato para esta metodología. A partir de aquí, la primera optimización se centra en un motor de encendido por compresión que funciona con combustible convencional para validar la metodología y también para evaluar el estado actual de evolución de estos motores. Con el objetivo de reducir el consumo de combustible manteniendo los niveles de NOx y hollín por debajo de los valores de un motor real, se inicia el proceso de optimización. Los resultados obtenidos confirman que un nuevo sistema de combustión específico para este motor podría generar una reducción del consumo de combustible manteniendo las emisiones de gases por debajo del valor estipulado. Además, se concluye que los motores MEC que utilizan combustible convencional se encuentran ya en un nivel de eficiencia muy elevado, y es difícil mejorarlos sin utilizar un sistema de postratamiento. Así pues, el segundo bloque de optimización se basa en el uso de motores MEC que funcionan con un combustible alternativo, que en este caso es el OME. El objetivo de este estudio es diseñar un sistema de combustión específico para un motor que utilice este combustible y que ofrezca un rendimiento del mismo orden de magnitud que un motor diésel. En la búsqueda de una mayor eficiencia, las emisiones de NOx son una restricción del sistema de optimización para que el sistema de combustión no emita más gases que un motor real. En este caso, el hollín no se tiene en cuenta debido a que las características del combustible no producen este tipo de contaminante. Los resultados mostraron que un sistema de combustión diseñado específicamente para esta operación podía ofrecer altas eficiencias, incluso la eficiencia obtenida fue alrededor de 2,2 % mayor en comparación con el motor diesel real. Además, fue posible reducir a la mitad las emisiones de NOx cuando el motor funciona con OME. El último bloque de optimización se refiere a una nueva arquitectura de motor que permite eliminar las emisiones de NOx. El modelo de oxicombustión resulta apasionante, ya que se elimina el nitrógeno de la mezcla de admisión y, por tanto, no se generan emisiones que contengan N2. Además, con el uso de este modo de combustión, es posible capturar CO$_{2}$ de los gases de escape, que luego puede venderse en el mercado. Dado que se trata de un tema nuevo y poco investigado, los resultados son prometedores. Demuestran que fue posible obtener un sistema de combustión específico capaz de ofrecer niveles de eficiencia cercanos a los de los motores convencionales. Además, se eliminaron las emisiones de NOx, así como las de hollín. Adicionalmente, este sistema fue capaz de reducir las emisiones de CO y HC a niveles similares a los motores convencionales. Por otra parte, los resultados presentados en esta tesis doctoral proporcionan una base de datos ampliada para explorar el funcionamiento del motor CI. / [CAT] Reduir les emissions de gasos contaminants dels motors de combustió interna alternatius (MCIA) és un dels majors reptes per a combatre el camvi climàtic. Atés que els motors continuaran sent utilitzats per la indústria durant dècades, és necessari desenvolupar noves tecnologies. En aquest context, la present tesi doctoral ve motivada per la necessitat de continuar millorant els motors, tant des del punt de vista de l'enginyeria tècnica com des del punt de vista social, degut a l'efecte dels gasos d'efecte d'hivernacle. L'objectiu principal d'aquesta tesi és desenvolupar una metodologia d'optimització per a sistemes de combustió de motors d'encesa provocada mitjançant l'acoblament d'algorismes d'optimització amb simulació per ordinador. Amb l'optimització dels sistemes de combustió és possible augmentar l'eficiència dels motors, reduint així el consum de combustible, concomitantment amb la reducció d'emissions de gasos, en particular òxids de nitrogen (NOx) i sutge. En el primer pas, s'aborden diferents algorismes d'optimització amb la finalitat d'elegir el millor candidat per a aquesta metodologia. A partir d'ací, la primera optimització se centra en un motor d'encesa per compressió que funciona amb combustible convencional per a validar la metodologia i també per a avaluar l'estat actual d'evolució d'aquests motors. Amb l'objectiu de reduir el consum de combustible mantenint els nivells de NOx i sutge per davall dels valors d'un motor real, s'inicia el procés d'optimització. Els resultats obtinguts confirmen que un nou sistema de combustió específic per a aquest motor podria generar una reducció del consum de combustible mantenint les emissions de gasos per davall del valor estipulat. A més, es conclou que els motors d'encesa per compressió que utilitzen combustible convencional es troben ja en un nivell d'eficiència molt elevat, i és difícil millorar-los sense utilitzar un sistema de posttractament. Així doncs, el segon bloc d'optimització es basa en l'ús de motors d'encesa per compressió que funcionen amb un combustible alternatiu, que en aquest cas és el OME. L'objectiu d'aquest estudi és dissenyar un sistema de combustió específic per a un motor que utilitze aquest combustible i que oferisca un rendiment del mateix ordre de magnitud que un motor dièsel. En la cerca d'una major eficiència, les emissions de NOx són una restricció del sistema d'optimització perquè el sistema de combustió no emeta més gasos que un motor real. En aquest cas, el sutge no es té en compte pel fet que les característiques del combustible no produeixen aquest tipus de contaminant. Els resultats van mostrar que un sistema de combustió dissenyat específicament per a aquesta operació podia oferir altes eficiències, fins i tot l'eficiència obtinguda va ser al voltant de 2,2 % major en comparació amb el motor dièsel real. A més, va ser possible reduir a la meitat les emissions de NOx quan el motor funciona amb OME. L'últim bloc d'optimització es refereix a una nova arquitectura del motor que permet eliminar les emissions de NOx. El model de oxicombustió resulta apassionant, ja que s'elimina el nitrogen de la mescla d'admissió i, per tant, no es generen emissions que continguen N2. A més, amb l'ús d'aquesta manera de combustió, és possible capturar CO$_{2}$ dels gasos de fuita, que després pot vendre's en el mercat. Atés que es tracta d'un tema nou i poc investigat, els resultats són prometedors. Demostren que va ser possible obtindre un sistema de combustió específic capaç d'oferir nivells d'eficiència pròxims als dels motors convencionals. A més, es van eliminar les emissions de NOx, així com les de sutge. Addicionalment, aquest sistema va ser capaç de reduir les emissions de CO i HC a nivells similars als motors convencionals. D'altra banda, els resultats presentats en aquesta tesi doctoral proporcionen una base de dades ampliada per a explorar el funcionament del motor CI. / [EN] Reducing emissions of pollutant gases from internal combustion engines (ICE) is one of the biggest challenges to combat global warming. As the engines will continue to be used by industry for decades, it is necessary to develop new technologies. In this context, the present doctoral thesis was motivated by the need to further improve engines, both from a technical engineering and social point of view, due to the effects of greenhouse gases. The main objective of this thesis is to develop an optimization methodology for compression ignition (CI) engine combustion systems by coupling optimization algorithms with computer simulation. With the optimization of the combustion systems, it is possible to increase the efficiency of the engines, thus reducing fuel consumption, concomitantly with the reduction of gas emissions, in particular nitrogen oxides (NOx) and soot. In the first step, different optimization algorithms are addressed in order to elect the best candidate for this methodology. From this point on, the first optimization is focused on a CI engine operating with conventional fuel in order to validate the methodology and also to evaluate the current state of evolution of these engines. With the goal of reducing fuel consumption while keeping NOx and soot levels below the values of a real engine, the optimization process begins. The results obtained confirm that a new combustion system specifically for this engine could generate a reduction in fuel consumption while keeping gas emissions below the stipulated value. Furthermore, it is concluded that CI engines using conventional fuel are already at a very high-efficiency level, and it is difficult to improve them without the use of an after-treatment system. Thus, the second optimization block is based on the use of CI engines operating on an alternative fuel, which in this case is OME. This study aimed to design a specific combustion system for an engine using this fuel that delivers efficiency on the same order of magnitude as a diesel engine. While searching for better efficiency, the NOx emissions are a restriction of the optimization system so that the combustion system does not emit more gases than a real engine. In this case, soot is not considered due to the characteristics of the fuel not producing this kind of pollutant. The results showed that a combustion system designed specifically for this operation could deliver high efficiencies, including the efficiency obtained was around 2.2 \% higher compared to the real diesel engine. In addition, it was possible to halve the NOx emissions when the engine operates with OME. The last optimization block concerns a new engine architecture that makes it possible to eliminate NOx emissions. The oxy-fuel combustion model is exciting since nitrogen is eliminated from the intake mixture, and thus no emissions containing N2 are generated. Furthermore, with the use of this combustion mode, it is possible to capture CO$_{2}$ from the exhaust gas, which can then be sold to the market. Since this is a new and little-researched topic, the results are promising. They show that it was possible to obtain a specific combustion system capable of delivering efficiency levels close to conventional engines. Furthermore, NOx emissions were eliminated, as well as soot emissions. Additionally, this system was able to reduce CO and HC emissions to levels similar to conventional engines. Moreover, the results presented in this doctoral thesis provide an extended database to explore the CI engine operation. Additionally, this work showed the potential of computational simulation allied with mathematical methods in order to design combustion systems for different applications. / I want to thanks the Universitat Politecnica de Valencia for his predoctoral contract (FPI-2019-S2-20-555), which is included within the framework of Programa de Apoyo para la Investigacion y Desarrollo (PAID). / Spohr Fernandes, C. (2023). Analysis and CFD-Guided optimization of advanced combustion systems in compression-ignited engines [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/193292

The task of the diploma thesis named VBA and Maple Application on Process Engineering Problems is to show the possibilities of using these programming languages for various engineering tasks. Particularly the programming language Visual Basic for Application (VBA), which is a part of MS Office package, is very little used in practise. That´s why this thesis solves the complex task of a furnaces design process; the topic was reccomended by the supervisor prof. Ing. Josef Kohoutek, CSc. Specifically, the thesis deals with calculations of heat transfer and optimization of the height of extended surfaces of tubes in the convection section of process furnaces. The entire task is elaborated in VBA and runs in Excel. After entering the input information, the created program first calculates the size and heat output of the convection section, but it can also optimize the height of extended surfaces in different parts of the convection section and thus minimize investment costs.