Monivaiheisen höyryturbiinin optimointi

Tässä diplomityössä optimoitiin nelivaiheinen 1 MWe höyryturbiinin prototyyppimalli evoluutioalgoritmien avulla sekä tutkittiin optimoinnista saatuja kustannushyötyjä. Optimoinnissa käytettiin DE – algoritmia. Optimointi saatiin toimimaan, mutta optimoinnissa käytetyn laskentasovelluksen (semiempiirisiin yhtälöihin perustuvat mallit) luonteesta johtuen optimoinnin tarkkuus CFD – laskennalla suoritettuun tarkastusmallinnukseen verrattuna oli jonkin verran toivottua pienempi. Tulosten em. epätarkkuus olisi tuskin ollut vältettävissä, sillä ongelma johtui puoliempiirisiin laskentamalleihin liittyvistä lähtöoletusongelmista sekä epävarmuudesta sovitteiden absoluuttisista pätevyysalueista. Optimoinnin onnistumisen kannalta tällainen algebrallinen mallinnus oli kuitenkin välttämätöntä, koska esim. CFD-laskentaa ei olisi mitenkään voitu tehdä jokaisella optimointiaskeleella. Optimoinnin aikana ongelmia esiintyi silti konetehojen riittävyydessä sekä sellaisen sopivan rankaisumallin löytämisessä, joka pitäisi algoritmin matemaattisesti sallitulla alueella, muttei rajoittaisi liikaa optimoinnin edistymistä. Loput ongelmat johtuivat sovelluksen uutuudesta sekä täsmällisyysongelmista sovitteiden pätevyysalueiden käsittelyssä. Vaikka optimoinnista saatujen tulosten tarkkuus ei ollut aivan tavoitteen mukainen, oli niillä kuitenkin koneensuunnittelua edullisesti ohjaava vaikutus. DE – algoritmin avulla suoritetulla optimoinnilla saatiin turbiinista noin 2,2 % enemmän tehoja, joka tarkoittaa noin 15 000 € konekohtaista kustannushyötyä. Tämä on yritykselle erittäin merkittävä konekohtainen kustannushyöty. Loppujen lopuksi voitaneen sanoa, etteivät evoluutioalgoritmit olleet parhaimmillaan prototyyppituotteen optimoinnissa. Evoluutioalgoritmeilla teknisten laitteiden optimoinnissa piilee valtavasti mahdollisuuksia, mutta se vaatii kypsän sovelluskohteen, joka tunnetaan jo entuudestaan erinomaisesti tai on yksinkertainen ja aukottomasti laskettavissa.

Analyzing Carbon Trade Permits in Optimization Framework

The threats caused by global warming motivate different stake holders to deal with and control them. This Master's thesis focuses on analyzing carbon trade permits in optimization framework. The studied model determines optimal emission and uncertainty levels which minimize the total cost. Research questions are formulated and answered by using different optimization tools. The model is developed and calibrated by using available consistent data in the area of carbon emission technology and control. Data and some basic modeling assumptions were extracted from reports and existing literatures. The data collected from the countries in the Kyoto treaty are used to estimate the cost functions. Theory and methods of constrained optimization are briefly presented. A two-level optimization problem (individual and between the parties) is analyzed by using several optimization methods. The combined cost optimization between the parties leads into multivariate model and calls for advanced techniques. Lagrangian, Sequential Quadratic Programming and Differential Evolution (DE) algorithm are referred to. The role of inherent measurement uncertainty in the monitoring of emissions is discussed. We briefly investigate an approach where emission uncertainty would be described in stochastic framework. MATLAB software has been used to provide visualizations including the relationship between decision variables and objective function values. Interpretations in the context of carbon trading were briefly presented. Suggestions for future work are given in stochastic modeling, emission trading and coupled analysis of energy prices and carbon permits.

Optimization of a heatset dryer

The objective of the thesis was to examine the possibilities in designing better performing nozzles for the heatset drying oven in Forest Pilot Center. To achieve the objective, two predesigned nozzle types along with the replicas of the current nozzles in the heatset drying oven were tested on a pilot-scale dryer. During the runnability trials, the pilot dryer was installed between the last printing unit and the drying oven. The two sets of predesigned nozzles were consecutively installed in the dryer. Four web tension values and four different impingement air velocities were used and the web behavior during the trial points was evaluated and recorded. The runnability in all trial conditions was adequate or even good. During the heat transfer trials, each nozzle type was tested on at least two different nozzle-to-surface distances and four different impingement air velocities. In a test situation, an aluminum plate fitted with thermocouples was set below a nozzle and the temperature measurement of each block was logged. From the measurements, a heat transfer coefficient profile for the nozzle was calculated. The performance of each nozzle type in tested conditions could now be rated and compared. The results verified that the predesigned simpler nozzles were better than the replicas. For runnability reasons, there were rows of inclined orifices on the leading and trailing edges of the current nozzles. They were believed to deteriorate the overall performance of the nozzle, and trials were conducted to test this hypothesis. The perpendicular orifices and inclined orifices of a replica nozzle were consecutively taped shut and the performance of the modified nozzles was measured as before, and then compared to the performance of the whole nozzle. It was found out, that after a certain nozzle-to-surface distance the jets from the two nozzles would collide, which deteriorates the heat transfer.

Optimization of energy in a pulp mill

This study presents examination of ways to increase power generation in pulp mills. The main purpose was to identify and verify the best ways of power generation growth. The literature part of this study presented operation of energy pulp mill departments, energy consumption and generation by the recovery and power boilers. The second chapter of this part described the main directions for increase of electricity generation rise of black liquor dry solid content, increase of main steam parameters, flue gas heat recovery technologies, feed water and combustion air preheating. The third chapter of the literature part presented possible technical, environment and corrosion risks appeared from described alternatives. In the experimental part of this study, calculations and results of possible models with alternatives was presented. The possible combinations of alternatives were generated in 44 `models of energy pulp mill. The target of this part was define extra electricity generation after alternatives using and estimate profitability of generated models. The calculations were made by computer programme PROSIM. In the conclusions, the results were estimated on the basis of extra electricity generation and equipment design data of models. The profitability of cases was verified by their payback periods and additional incomes.

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

Production Cycle Optimization: Defining the Market Potential for a New Planning Tool

An optimization tool has been developed to help companies to optimize their production cycles and thus improve their overall supply chain management processes. The application combines the functionality that traditional APS (Advanced Planning System) and ARP (Automatic Replenishment Program) systems provide into one optimization run. A qualitative study was organized to investigate opportunities to expand the product’s market base. Twelve personal interviews were conducted and the results were collected in industry specific production planning analyses. Five process industries were analyzed to identify the product’s suitability to each industry sector and the most important product development areas. Based on the research the paper and the plastic film industries remain the most potential industry sectors at this point. To be successful in other industry sectors some product enhancements would be required, including capabilities to optimize multiple sequential and parallel production cycles, handle sequencing of complex finishing operations and to include master planning capabilities to support overall supply chain optimization. In product sales and marketing processes the key to success is to find and reach the people who are involved directly with the problems that the optimization tool can help to solve.

Line filter optimization in elevator use

The purpose of this thesis was to create design a guideline for an LCL-filter. This thesis reviews briefly the relevant harmonics standards, old filter designs and problems faced with the previous filters. This thesis proposes a modified design method based on the “Liserre’s method” presented in the literature. This modified method will take into account network parameters better. As input parameters, the method uses the nominal power, allowed ripple current in converter and network side and desired resonant frequency of the filter. Essential component selection issues for LCL-filter, such as heating, voltage strength, current rating etc. are also discussed. Furthermore, a simulation model used to verify the operation of the designed filter in nominal power use and in transient situations is included in this thesis.

Optimization of wind blade design including its energetic characteristics

The goal of the Master’s thesis is to develop and to analyze the optimization method for finding a geometry shape of classical horizontal wind turbine blades based on set of criteria. The thesis develops a technique that allows the designer to determine the weight of such factors as power coefficient, sound pressure level and the cost function in the overall process of blade shape optimization. The optimization technique applies the Desirability function. It was never used before in that kind of technical problems, and in this sense it can claim to originality of research. To do the analysis and the optimization processes more convenient the software application was developed.

Optimization of the power cable for low voltage direct current system

The demand for electricity is constantly growing in contemporary world and, in the same time, quality and reliability requirements are becoming more rigid. In addition, renewable sources of energy have been widely introduced for power generation, and they create specific challenges for the network. Consequently, new solution for distribution system is required, and Low Voltage Direct Current (LVDC) system is the proposed one. This thesis focuses on the investigation of specific cable features for low voltage direct current (LVDC) distribution system. The LVDC system is public ±750 VDC distribution system, which is currently being developed at Lappeen-ranta University of Technology. The aspects, considered in the thesis, are reliable and economic power transmission in distribution networks and possible power line communication in the LVDC cable.

Complexity Optimization in Product Development with Modularization

A company’s competence to manage its product portfolio complexity is becoming critically important in the rapidly changing business environment. The continuous evolvement of customer needs, the competitive market environment and internal product development lead to increasing complexity in product portfolios. The companies that manage the complexity in product development are more profitable in the long run. The complexity derives from product development and management processes where the new product variant development is not managed efficiently. Complexity is managed with modularization which is a method that divides the product structure into modules. In modularization, it is essential to take into account the trade-off between the perceived customer value and the module or component commonality across the products. Another goal is to enable the product configuration to be more flexible. The benefits are achieved through optimizing complexity in module offering and deriving the new product variants more flexibly and accurately. The developed modularization process includes the process steps for preparation, mapping the current situation, the creation of a modular strategy and implementing the strategy. Also the organization and support systems have to be adapted to follow-up targets and to execute modularization in practice.

Algorithmic Solutions for Combinatorial Problems in Resource Management of Manufacturing Environments

This thesis studies the use of heuristic algorithms in a number of combinatorial problems that occur in various resource constrained environments. Such problems occur, for example, in manufacturing, where a restricted number of resources (tools, machines, feeder slots) are needed to perform some operations. Many of these problems turn out to be computationally intractable, and heuristic algorithms are used to provide efficient, yet sub-optimal solutions. The main goal of the present study is to build upon existing methods to create new heuristics that provide improved solutions for some of these problems. All of these problems occur in practice, and one of the motivations of our study was the request for improvements from industrial sources. We approach three different resource constrained problems. The first is the tool switching and loading problem, and occurs especially in the assembly of printed circuit boards. This problem has to be solved when an efficient, yet small primary storage is used to access resources (tools) from a less efficient (but unlimited) secondary storage area. We study various forms of the problem and provide improved heuristics for its solution. Second, the nozzle assignment problem is concerned with selecting a suitable set of vacuum nozzles for the arms of a robotic assembly machine. It turns out that this is a specialized formulation of the MINMAX resource allocation formulation of the apportionment problem and it can be solved efficiently and optimally. We construct an exact algorithm specialized for the nozzle selection and provide a proof of its optimality. Third, the problem of feeder assignment and component tape construction occurs when electronic components are inserted and certain component types cause tape movement delays that can significantly impact the efficiency of printed circuit board assembly. Here, careful selection of component slots in the feeder improves the tape movement speed. We provide a formal proof that this problem is of the same complexity as the turnpike problem (a well studied geometric optimization problem), and provide a heuristic algorithm for this problem.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Optimization of the refining and shoe press in board production

Optimointi on tavallinen toimenpide esimerkiksi prosessin muuttamisen tai uusimisen jälkeen. Optimoinnilla pyritään etsimään vaikkapa tiettyjen laatuominaisuuksien kannalta paras tapa ajaa prosessia tai erinäisiä prosessin osia. Tämän työn tarkoituksena oli investoinnin jälkeen optimoida neljä muuttujaa, erään runkoon menevän massan jauhatus ja määrä, märkäpuristus sekä spray –tärkin määrä, kolmen laatuominaisuuden, palstautumislujuuden, geometrisen taivutusjäykkyyden ja sileyden, suhteen. Työtä varten tehtiin viisi tehdasmittakaavaista koeajoa. Ensimmäisessä koeajossa oli tarkoitus lisätä vettä tai spray –tärkkiä kolmikerroskartongin toiseen kerrosten rajapintaan, toisessa koeajossa muutettiin, jo aiemmin mainitun runkoon menevän massan jauhatusta ja jauhinkombinaatioita. Ensimmäisessä koeajossa tutkittiin palstautumislujuuden, toisessa koeajossa muiden lujuusominaisuuksien kehittymistä. Kolmannessa koeajossa tutkittiin erään runkoon menevän massan jauhatuksen ja määrän sekä kenkäpuristimen viivapaineen muutoksen vaikutusta palstautumislujuuteen, geometriseen taivutusjäykkyyteen sekä sileyteen. Neljännessä koeajossa yritettiin toistaa edellisen koeajon paras piste ja parametreja hieman muuttamalla saada aikaan vieläkin paremmat laatuominaisuudet. Myös tässä kokeessa tutkittiin muuttujien vaikutusta palstautumislujuuteen, geometriseen taivutusjäykkyyteen ja sileyteen. Viimeisen kokeen tarkoituksena oli tutkia samaisen runkoon menevän massan vähentämisen vaikutusta palstautumislujuuteen. Erinäisistä vastoinkäymisistä johtuen, koeajoista saadut tulokset jäivät melko laihoiksi. Kokeista kävi kuitenkin ilmi, että lujuusominaisuudet eivät parantuneet, vaikka jauhatusta jatkettiin. Lujuusominaisuuksien kehittymisen kannalta turha jauhatus pystyttiin siis jättämään pois ja näin säästämään energiaa sekä säästymään pitkälle viedyn jauhatuksen mahdollisesti aiheuttamilta muilta ongelmilta. Vähemmällä jauhatuksella ominaissärmäkuorma saatiin myös pidettyä alle tehtaalla halutun tason. Puuttuvat lujuusominaisuudet täytyy saavuttaa muilla keinoin.

Search Engine Optimization Methods & Search Engine Indexing for CMS Applications

Search engine optimization & marketing is a set of processes widely used on websites to improve search engine rankings which generate quality web traffic and increase ROI. Content is the most important part of any website. CMS web development is now become very essential for most of organizations and online businesses to develop their online system and websites. Every online business using a CMS wants to get users (customers) to make profit and ROI. This thesis comprises a brief study of existing SEO methods, tools and techniques and how they can be implemented to optimize a content base website. In results, the study provides recommendations about how to use SEO methods; tools and techniques to optimize CMS based websites on major search engines. This study compares popular CMS systems like Drupal, WordPress and Joomla SEO features and how implementing SEO can be improved on these CMS systems. Having knowledge of search engine indexing and search engine working is essential for a successful SEO campaign. This work is a complete guideline for web developers or SEO experts who want to optimize a CMS based website on all major search engines.