Comparison of Model-Based Flow Rate Estimation Methods in Frequency-Converter-Driven Pumps and Fans

Fluid handling systems such as pump and fan systems are found to have a significant potential for energy efficiency improvements. To deliver the energy saving potential, there is a need for easily implementable methods to monitor the system output. This is because information is needed to identify inefficient operation of the fluid handling system and to control the output of the pumping system according to process needs. Model-based pump or fan monitoring methods implemented in variable speed drives have proven to be able to give information on the system output without additional metering; however, the current model-based methods may not be usable or sufficiently accurate in the whole operation range of the fluid handling device. To apply model-based system monitoring in a wider selection of systems and to improve the accuracy of the monitoring, this paper proposes a new method for pump and fan output monitoring with variable-speed drives. The method uses a combination of already known operating point estimation methods. Laboratory measurements are used to verify the benefits and applicability of the improved estimation method, and the new method is compared with five previously introduced model-based estimation methods. According to the laboratory measurements, the new estimation method is the most accurate and reliable of the model-based estimation methods.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Doubly fed wind turbine performance in variable grid conditions

Wind turbines based on doubly fed induction generators (DFIG) become the most popular solution in high power wind generation industry. While this topology provides great performance with the reduced power rating of power converter, it has more complicated structure in comparison with full-rated topologies, and therefore leads to complexity of control algorithms and electromechanical processes in the system. The purpose of presented study is to present a proper vector control scheme for the DFIG and overall control for the WT to investigate its behavior at different wind speeds and in different grid voltage conditions: voltage sags, magnitude and frequency variations. The key principles of variable-speed wind turbine were implemented in simulation model and demonstrated during the study. Then, based on developed control scheme and mathematical model, the set of simulation is made to analyze reactive power capabilities of the DFIG wind turbine. Further, the rating of rotor-side converter is modified to not only generate active rated active power, but also to fulfill Grid Codes. Results of modelling and analyzing of the DFIG WT behavior under different speeds and different voltage conditions are presented in the work.

Review of investment strategy, portfolio management, and scorecard based portfolio selection tool for direct property investments in Finland

The lack of research of private real estate is a well-known problem. Earlier studies have mostly concentrated on the USA or the UK. Therefore, this master thesis offers more information about the performance and risk associated with private real estate investments in Nordic countries, but especially in Finland. The structure of this master thesis is divided into two independent sections based on the research questions. In first section, database analysis is performed to assess risk-return ratio of direct real estate investment for Nordic countries. Risk-return ratios are also assessed for different property sectors and economic regions. Finally, review of diversification strategies based on property sectors and economic regions is performed. However, standard deviation itself is not usually sufficient method to evaluate riskiness of private real estate. There is demand for more explicit assessment of property risk. One solution is property risk scoring. In second section risk scorecard based tool is built to make different real estate comparable in terms of risk. In order to do this, nine real estate professionals were interviewed to enhance the structure of theory-based risk scorecard and to assess weights for different risk factors.

Variable-speed-drive-based detection methods for problems in fluid handling systems

Fluid handling systems account for a significant share of the global consumption of electrical energy. They also suffer from problems, which reduce their energy efficiency and increase life-cycle costs. Detecting or predicting these problems in time can make fluid handling systems more environmentally and economically sustainable to operate. In this Master’s Thesis, significant problems in fluid systems were studied and possibilities to develop variable-speed-drive-based detection methods for them was discussed. A literature review was conducted to find significant problems occurring in fluid handling systems containing pumps, fans and compressors. To find case examples for evaluating the feasibility of variable-speed-drive-based methods, queries were sent to industrial companies. As a result of this, the possibility to detect heat exchanger fouling with a variable-speed drive was analysed with data from three industrial cases. It was found that a mass flow rate estimate, which can be generated with a variable speed drive, can be used together with temperature measurements to monitor a heat exchanger’s thermal performance. Secondly, it was found that the fouling-related increase in the pressure drop of a heat exchanger can be monitored with a variable speed drive. Lastly, for systems where the flow device is speed controlled with by a pressure measurement, it was concluded that increasing rotational speed can be interpreted as progressing fouling in the heat exchanger.

Healthcare Information System Selection Model for Medical Clinics

The issue of selecting an appropriate healthcare information system is a very essential one. If implemented healthcare information system doesn’t fit particular healthcare institution, for example there are unnecessary functions; healthcare institution wastes its resources and its efficiency decreases. The purpose of this research is to develop a healthcare information system selection model to assist the decision-making process of choosing healthcare information system. Appropriate healthcare information system helps healthcare institutions to become more effective and efficient and keep up with the times. The research is based on comparison analysis of 50 healthcare information systems and 6 interviews with experts from St-Petersburg healthcare institutions that already have experience in healthcare information system utilization. 13 characteristics of healthcare information systems: 5 key and 7 additional features are identified and considered in the selection model development. Variables are used in the selection model in order to narrow the decision algorithm and to avoid duplication of brunches. The questions in the healthcare information systems selection model are designed to be easy-to-understand for common a decision-maker in healthcare institution without permanent establishment.

Design and Material Selection of High-Speed Rotating Electrical Machines

The increasing emphasis on energy efficiency is starting to yield results in the reduction in greenhouse gas emissions; however, the effort is still far from sufficient. Therefore, new technical solutions that will enhance the efficiency of power generation systems are required to maintain the sustainable growth rate, without spoiling the environment. A reduction in greenhouse gas emissions is only possible with new low-carbon technologies, which enable high efficiencies. The role of the rotating electrical machine development is significant in the reduction of global emissions. A high proportion of the produced and consumed electrical energy is related to electrical machines. One of the technical solutions that enables high system efficiency on both the energy production and consumption sides is high-speed electrical machines. This type of electrical machines has a high system overall efficiency, a small footprint, and a high power density compared with conventional machines. Therefore, high-speed electrical machines are favoured by the manufacturers producing, for example, microturbines, compressors, gas compression applications, and air blowers. High-speed machine technology is challenging from the design point of view, and a lot of research is in progress both in academia and industry regarding the solution development. The solid technical basis is of importance in order to make an impact in the industry considering the climate change. This work describes the multidisciplinary design principles and material development in high-speed electrical machines. First, high-speed permanent magnet synchronous machines with six slots, two poles, and tooth-coil windings are discussed in this doctoral dissertation. These machines have unique features, which help in solving rotordynamic problems and reducing the manufacturing costs. Second, the materials for the high-speed machines are discussed in this work. The materials are among the key limiting factors in electrical machines, and to overcome this limit, an in-depth analysis of the material properties and behavior is required. Moreover, high-speed machines are sometimes operating in a harsh environment because they need to be as close as possible to the rotating tool and fully exploit their advantages. This sets extra requirements for the materials applied.

Macroalgal Defenses Against Herbivory: Causes and Consequences of Intraspecific Variation

In marine benthic communities, herbivores consume a considerable proportion of primary producer biomass and, thus, generate selection for the evolution of resistance traits. According to the theory of plant defenses, resistance traits are costly to produce and, consequently, inducible resistance traits are adaptive in conditions of variable herbivory, while in conditions of constant/strong herbivory constitutive resistance traits are selected for. The evolution of resistance plasticity may be constrained by the costs of resistance or lack of genetic variation in resistance. Furthermore, resource allocation to induced resistance may be affected by higher trophic levels preying on herbivores. I studied the resistance to herbivory of a foundation species, the brown alga Fucus vesiculosus. By using factorial field experiments, I explored the effects of herbivores and fish predators on growth and resistance of the alga in two seasons. I explored genetic variation in and allocation costs of resistance traits as well as their chemical basis and their effects on herbivore performance. Using a field experiment I tested if induced resistance spreads via water-borne cues from one individual to another in relevant ecological conditions. I found that in the northern Baltic Sea F. vesiculosus communities, strength of three trophic interactions strongly vary among seasons. The highly synchronized summer reproduction of herbivores promoted their escape from the top-down control of fish predators in autumn. This resulted into large grazing losses in algal stands. In spring, herbivore densities were low and regulated by fish, which, thus,enhanced algal growth. The resistance of algae to herbivory increased with an increase in constitutive phlorotannin content. Furthermore, individuals adopted induced resistance when grazed and when exposed to water-borne cues originating from grazing of conspecific algae both in the laboratory and in field conditions. Induced resistance was adopted to a lesser extent in the presence of fish predators. The results in this thesis indicate that inducible resistance in F. vesiculosus is an adaptation to varying herbivory in the northern Baltic Sea. The costs of resistance and strong seasonality of herbivory have likely contributed to the evolution of this defense strategy. My findings also show that fish predators have positive cascading effects on F. vesiculosus which arise via reduced herbivory but possibly also through reduced resource allocation to resistance. I further found evidence that the spread of resistance via water-borne cues also occurs in ecologically realistic conditions in natural marine sublittoral. Thus, water-borne induction may enable macroalgae to cope with the strong grazing pressure characteristic of marine benthic communities. The results presented here show that seasonality can have pronounced effects on the biotic interactions in marine benthic communities and thereafter influence the evolution of resistance traits in primary producers.