Konkurssin ennustaminen ja makrotaloudelliset muuttujat: rakennusalan pk-yritykset

Tämän tutkimuksen tavoitteena on selvittää kannattaako makrotaloudellisia muuttujia käyttää tunnuslukujen lisäksi yrityksen konkurssin ennustamisessa. Perinteiset konkurssinennustamismallit hyödyntävät pelkästään tilinpäätöksestä saatavia tunnuslukuja eivätkä huomioi yrityksen toimintaympäristöä ja sen muutoksia. Aiemmissa tutkimuksissa makrotaloudellisilla muuttujilla on pystytty parantamaan perinteisiä ennustamismalleja. Tutkimus toteutetaan luomalla kolme erilaista konkurssinennustamismallia logistista regressioanalyysiä hyödyntäen ja vertailemalla niiden paremmuutta. Tutkimustulokset osoittavat, että rakennusalan pk-yritysten konkursseja pystytään ennustamaan kolmen tunnusluvun mallilla. Taloudellisen ajanjakson, jolta yrityksen tiedot ovat peräisin, huomioiminen ei tuo malliin lisäarvoa eikä paranna ennustamiskykyä merkittävästi.

Konkurssin ennustaminen ja makrotaloudelliset muuttujat: rakennusalan pk-yritykset

Tämän tutkimuksen tavoitteena on selvittää kannattaako makrotaloudellisia muuttujia käyttää tunnuslukujen lisäksi yrityksen konkurssin ennustamisessa. Perinteiset konkurssinennustamismallit hyödyntävät pelkästään tilinpäätöksestä saatavia tunnuslukuja eivätkä huomioi yrityksen toimintaympäristöä ja sen muutoksia. Aiemmissa tutkimuksissa makrotaloudellisilla muuttujilla on pystytty parantamaan perinteisiä ennustamismalleja. Tutkimus toteutetaan luomalla kolme erilaista konkurssinennustamismallia logistista regressioanalyysiä hyödyntäen ja vertailemalla niiden paremmuutta. Tutkimustulokset osoittavat, että rakennusalan pk-yritysten konkursseja pystytään ennustamaan kolmen tunnusluvun mallilla. Taloudellisen ajanjakson, jolta yrityksen tiedot ovat peräisin, huomioiminen ei tuo malliin lisäarvoa eikä paranna ennustamiskykyä merkittävästi.

Predicting bankruptcy of Finnish limited liability companies from historical financial statements

This master’s thesis studies the probability of bankruptcy of Finnish limited liability companies as a part of credit risk assessment. The main idea of this thesis is to build and test bankruptcy prediction models for Finnish limited liability companies that can be utilized in credit decision making. The data used in this thesis consists of historical financial statements from 2112 Finnish limited liability companies, half of which have filed for bankruptcy. A total of four models are developed, two with logistic regression and two with multivariate discriminant analysis (MDA). The time horizon of the models varies from 1 to 2 years prior to the bankruptcy, and 14 different financial variables are used in the model formation. The results show that the prediction accuracy of the models ranges between 81.7% and 88.9%, and the best prediction accuracy is achieved with the one year prior the bankruptcy logistic regression model. However the difference between the best logistic model and the best MDA model is minimal. Overall based on the results of this thesis it can be concluded that predicting bankruptcy is possible to some extent, but naturally the results are not perfect.

Lightweight, liquid-cooled, direct-drive generator for highpower wind turbines: motivation, concept, and performance

Thesis: A liquid-cooled, direct-drive, permanent-magnet, synchronous generator with helical, double-layer, non-overlapping windings formed from a copper conductor with a coaxial internal coolant conduit offers an excellent combination of attributes to reliably provide economic wind power for the coming generation of wind turbines with power ratings between 5 and 20MW. A generator based on the liquid-cooled architecture proposed here will be reliable and cost effective. Its smaller size and mass will reduce build, transport, and installation costs. Summary: Converting wind energy into electricity and transmitting it to an electrical power grid to supply consumers is a relatively new and rapidly developing method of electricity generation. In the most recent decade, the increase in wind energy’s share of overall energy production has been remarkable. Thousands of land-based and offshore wind turbines have been commissioned around the globe, and thousands more are being planned. The technologies have evolved rapidly and are continuing to evolve, and wind turbine sizes and power ratings are continually increasing. Many of the newer wind turbine designs feature drivetrains based on Direct-Drive, Permanent-Magnet, Synchronous Generators (DD-PMSGs). Being low-speed high-torque machines, the diameters of air-cooled DD-PMSGs become very large to generate higher levels of power. The largest direct-drive wind turbine generator in operation today, rated just below 8MW, is 12m in diameter and approximately 220 tonne. To generate higher powers, traditional DD-PMSGs would need to become extraordinarily large. A 15MW air-cooled direct-drive generator would be of colossal size and tremendous mass and no longer economically viable. One alternative to increasing diameter is instead to increase torque density. In a permanent magnet machine, this is best done by increasing the linear current density of the stator windings. However, greater linear current density results in more Joule heating, and the additional heat cannot be removed practically using a traditional air-cooling approach. Direct liquid cooling is more effective, and when applied directly to the stator windings, higher linear current densities can be sustained leading to substantial increases in torque density. The higher torque density, in turn, makes possible significant reductions in DD-PMSG size. Over the past five years, a multidisciplinary team of researchers has applied a holistic approach to explore the application of liquid cooling to permanent-magnet wind turbine generator design. The approach has considered wind energy markets and the economics of wind power, system reliability, electromagnetic behaviors and design, thermal design and performance, mechanical architecture and behaviors, and the performance modeling of installed wind turbines. This dissertation is based on seven publications that chronicle the work. The primary outcomes are the proposal of a novel generator architecture, a multidisciplinary set of analyses to predict the behaviors, and experimentation to demonstrate some of the key principles and validate the analyses. The proposed generator concept is a direct-drive, surface-magnet, synchronous generator with fractional-slot, duplex-helical, double-layer, non-overlapping windings formed from a copper conductor with a coaxial internal coolant conduit to accommodate liquid coolant flow. The novel liquid-cooling architecture is referred to as LC DD-PMSG. The first of the seven publications summarized in this dissertation discusses the technological and economic benefits and limitations of DD-PMSGs as applied to wind energy. The second publication addresses the long-term reliability of the proposed LC DD-PMSG design. Publication 3 examines the machine’s electromagnetic design, and Publication 4 introduces an optimization tool developed to quickly define basic machine parameters. The static and harmonic behaviors of the stator and rotor wheel structures are the subject of Publication 5. And finally, Publications 6 and 7 examine steady-state and transient thermal behaviors. There have been a number of ancillary concrete outcomes associated with the work including the following. X Intellectual Property (IP) for direct liquid cooling of stator windings via an embedded coaxial coolant conduit, IP for a lightweight wheel structure for lowspeed, high-torque electrical machinery, and IP for numerous other details of the LC DD-PMSG design X Analytical demonstrations of the equivalent reliability of the LC DD-PMSG; validated electromagnetic, thermal, structural, and dynamic prediction models; and an analytical demonstration of the superior partial load efficiency and annual energy output of an LC DD-PMSG design X A set of LC DD-PMSG design guidelines and an analytical tool to establish optimal geometries quickly and early on X Proposed 8 MW LC DD-PMSG concepts for both inner and outer rotor configurations Furthermore, three technologies introduced could be relevant across a broader spectrum of applications. 1) The cost optimization methodology developed as part of this work could be further improved to produce a simple tool to establish base geometries for various electromagnetic machine types. 2) The layered sheet-steel element construction technology used for the LC DD-PMSG stator and rotor wheel structures has potential for a wide range of applications. And finally, 3) the direct liquid-cooling technology could be beneficial in higher speed electromotive applications such as vehicular electric drives.

Influence of multi-phase phenomena on semibatch crystallization processes of aqueous solutions

Crystal properties, product quality and particle size are determined by the operating conditions in the crystallization process. Thus, in order to obtain desired end-products, the crystallization process should be effectively controlled based on reliable kinetic information, which can be provided by powerful analytical tools such as Raman spectrometry and thermal analysis. The present research work studied various crystallization processes such as reactive crystallization, precipitation with anti-solvent and evaporation crystallization. The goal of the work was to understand more comprehensively the fundamentals, phenomena and utilizations of crystallization, and establish proper methods to control particle size distribution, especially for three phase gas-liquid-solid crystallization systems. As a part of the solid-liquid equilibrium studies in this work, prediction of KCl solubility in a MgCl2-KCl-H2O system was studied theoretically. Additionally, a solubility prediction model by Pitzer thermodynamic model was investigated based on solubility measurements of potassium dihydrogen phosphate with the presence of non-electronic organic substances in aqueous solutions. The prediction model helps to extend literature data and offers an easy and economical way to choose solvent for anti-solvent precipitation. Using experimental and modern analytical methods, precipitation kinetics and mass transfer in reactive crystallization of magnesium carbonate hydrates with magnesium hydroxide slurry and CO2 gas were systematically investigated. The obtained results gave deeper insight into gas-liquid-solid interactions and the mechanisms of this heterogeneous crystallization process. The research approach developed can provide theoretical guidance and act as a useful reference to promote development of gas-liquid reactive crystallization. Gas-liquid mass transfer of absorption in the presence of solid particles in a stirred tank was investigated in order to gain understanding of how different-sized particles interact with gas bubbles. Based on obtained volumetric mass transfer coefficient values, it was found that the influence of the presence of small particles on gas-liquid mass transfer cannot be ignored since there are interactions between bubbles and particles. Raman spectrometry was successfully applied for liquid and solids analysis in semi-batch anti-solvent precipitation and evaporation crystallization. Real-time information such as supersaturation, formation of precipitates and identification of crystal polymorphs could be obtained by Raman spectrometry. The solubility prediction models, monitoring methods for precipitation and empirical model for absorption developed in this study together with the methodologies used gives valuable information for aspects of industrial crystallization. Furthermore, Raman analysis was seen to be a potential controlling method for various crystallization processes.

Minimization of the experimental workload for the prediction of pollutants propagation in rivers. Mathematical modelling and knowledge re-use

The present thesis in focused on the minimization of experimental efforts for the prediction of pollutant propagation in rivers by mathematical modelling and knowledge re-use. Mathematical modelling is based on the well known advection-dispersion equation, while the knowledge re-use approach employs the methods of case based reasoning, graphical analysis and text mining. The thesis contribution to the pollutant transport research field consists of: (1) analytical and numerical models for pollutant transport prediction; (2) two novel techniques which enable the use of variable parameters along rivers in analytical models; (3) models for the estimation of pollutant transport characteristic parameters (velocity, dispersion coefficient and nutrient transformation rates) as functions of water flow, channel characteristics and/or seasonality; (4) the graphical analysis method to be used for the identification of pollution sources along rivers; (5) a case based reasoning tool for the identification of crucial information related to the pollutant transport modelling; (6) and the application of a software tool for the reuse of information during pollutants transport modelling research. These support tools are applicable in the water quality research field and in practice as well, as they can be involved in multiple activities. The models are capable of predicting pollutant propagation along rivers in case of both ordinary pollution and accidents. They can also be applied for other similar rivers in modelling of pollutant transport in rivers with low availability of experimental data concerning concentration. This is because models for parameter estimation developed in the present thesis enable the calculation of transport characteristic parameters as functions of river hydraulic parameters and/or seasonality. The similarity between rivers is assessed using case based reasoning tools, and additional necessary information can be identified by using the software for the information reuse. Such systems represent support for users and open up possibilities for new modelling methods, monitoring facilities and for better river water quality management tools. They are useful also for the estimation of environmental impact of possible technological changes and can be applied in the pre-design stage or/and in the practical use of processes as well.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.