The use and misuse of the "impact factor" as a parameter for evaluation of scientific publication quality: a proposal to rationalize its application

We present a critical analysis of the generalized use of the "impact factor". By means of the Kruskal-Wallis test, it was shown that it is not possible to compare distinct disciplines using the impact factor without adjustments. After assigning the median journal the value of one (1.000), the impact factor value for each journal was calculated by the rule of three. The adjusted values were homogeneous, thus permitting comparison among distinct disciplines.

Framework for Regional Heating Energy Efficiency Assessment

The aim of this project was to develop general framework for systematic assessment of energy efficiency of heating on regional level in Russia. The framework created during this project includes two main instruments, namely: general regional heating energy efficiency assessment model (REEMod) and general regional heating energy efficiency assessment criteria for housing areas (REECrit). Framework pays extreme attention to realization of energy saving, overall cost efficiency and comfortable indoor climate. Life-cycle ideology was applied during creation of the framework. Application of the framework can provide decision-making process with systematically collected and processed information on current state of areas energy efficiency. Such information will help decision makers to evaluate current situation of the whole energy chain, to compare different development scenarios and to identify the most efficient improvement methods, thus supporting realization of regions efficient energy management. Simultaneous pursuit of energy savings, cost efficiency and indoor air quality can contribute to development of sustainable community. Presented instruments should be continuously developed further as an iterative process based on knew experience, development of technology and overall understanding of energy efficiency issues.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Intensification of hemicellulose hot-water extraction from spruce wood by parameter tuning

The growing population on earth along with diminishing fossil deposits and the climate change debate calls out for a better utilization of renewable, bio-based materials. In a biorefinery perspective, the renewable biomass is converted into many different products such as fuels, chemicals, and materials, quite similar to the petroleum refinery industry. Since forests cover about one third of the land surface on earth, ligno-cellulosic biomass is the most abundant renewable resource available. The natural first step in a biorefinery is separation and isolation of the different compounds the biomass is comprised of. The major components in wood are cellulose, hemicellulose, and lignin, all of which can be made into various end-products. Today, focus normally lies on utilizing only one component, e.g., the cellulose in the Kraft pulping process. It would be highly desirable to utilize all the different compounds, both from an economical and environmental point of view. The separation process should therefore be optimized. Hemicelluloses can partly be extracted with hot-water prior to pulping. Depending in the severity of the extraction, the hemicelluloses are degraded to various degrees. In order to be able to choose from a variety of different end-products, the hemicelluloses should be as intact as possible after the extraction. The main focus of this work has been on preserving the hemicellulose molar mass throughout the extraction at a high yield by actively controlling the extraction pH at the high temperatures used. Since it has not been possible to measure pH during an extraction due to the high temperatures, the extraction pH has remained a “black box”. Therefore, a high-temperature in-line pH measuring system was developed, validated, and tested for hot-water wood extractions. One crucial step in the measurements is calibration, therefore extensive efforts was put on developing a reliable calibration procedure. Initial extractions with wood showed that the actual extraction pH was ~0.35 pH units higher than previously believed. The measuring system was also equipped with a controller connected to a pump. With this addition it was possible to control the extraction to any desired pH set point. When the pH dropped below the set point, the controller started pumping in alkali and by that the desired set point was maintained very accurately. Analyses of the extracted hemicelluloses showed that less hemicelluloses were extracted at higher pH but with a higher molar-mass. Monomer formation could, at a certain pH level, be completely inhibited. Increasing the temperature, but maintaining a specific pH set point, would speed up the extraction without degrading the molar-mass of the hemicelluloses and thereby intensifying the extraction. The diffusion of the dissolved hemicelluloses from the wood particle is a major part of the extraction process. Therefore, a particle size study ranging from 0.5 mm wood particles to industrial size wood chips was conducted to investigate the internal mass transfer of the hemicelluloses. Unsurprisingly, it showed that hemicelluloses were extracted faster from smaller wood particles than larger although it did not seem to have a substantial effect on the average molar mass of the extracted hemicelluloses. However, smaller particle sizes require more energy to manufacture and thus increases the economic cost. Since bark comprises 10 – 15 % of a tree, it is important to also consider it in a biorefinery concept. Spruce inner and outer bark was hot-water extracted separately to investigate the possibility to isolate the bark hemicelluloses. It was showed that the bark hemicelluloses comprised mostly of pectic material and differed considerably from the wood hemicelluloses. The bark hemicelluloses, or pectins, could be extracted at lower temperatures than the wood hemicelluloses. A chemical characterization, done separately on inner and outer bark, showed that inner bark contained over 10 % stilbene glucosides that could be extracted already at 100 °C with aqueous acetone.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Optimization of a dynamic district heating power plant

The purpose of this Thesis is to find the most optimal heat recovery solution for Wärtsilä’s dynamic district heating power plant considering Germany energy markets as in Germany government pays subsidies for CHP plants in order to increase its share of domestic power production to 25 % by 2020. Different heat recovery connections have been simulated dozens to be able to determine the most efficient heat recovery connections. The purpose is also to study feasibility of different heat recovery connections in the dynamic district heating power plant in the Germany markets thus taking into consideration the day ahead electricity prices, district heating network temperatures and CHP subsidies accordingly. The auxiliary cooling, dynamical operation and cost efficiency of the power plant is also investigated.

Computational studies of deformation in stainless steel rings due to torch heating

A support ring of AISI 304L stainless steel that holds vertical, parallel wires arranged in a circle forming a cylinder is studied. The wires are attached to the ring with heat-induced shrinkage. When the ring is heated with a torch the heat affected zone tries to expand while the adjacent cool structure obstructs the expansion causing upsetting. During cooling, the ring shrinks smaller than its original size clamping the wires. The most important requirement for the ring is that it should be as round as possible and the deformations should occur as overall shrinkage in the ring diameter. A three-dimensional nonlinear transient sequential thermo-structural Abaqus model is used together with a Fortran code that enters the heat flux to each affected element. The local and overall deformations in one ring inflicted by the heating are studied with a small amount of inspection on residual stresses. A variety of different cases are chosen to be studied with the model constructed to provide directional knowledge; torch flux with the means of speed, location of the wires, heating location and structural factors. The decrease of heating speed increases heat flux that rises the temperature increasing shrinkage. In a single progressive heating uneven distribution of shrinkage appears to the start/end region that can be partially fixed with using speeded heating’s to strengthen the heating of that region. Location of the wires affect greatly to the caused shrinkage unlike heating location. The ring structure affects also greatly to the shrinkage; smaller diameter, bigger ring height, thinner thickness and greater number of wires increase shrinkage.

Dendritic thickness: a morphometric parameter to classify mouse retinal ganglion cells

To study the dendritic morphology of retinal ganglion cells in wild-type mice we intracellularly injected these cells with Lucifer yellow in an in vitro preparation of the retina. Subsequently, quantified values of dendritic thickness, number of branching points and level of stratification of 73 Lucifer yellow-filled ganglion cells were analyzed by statistical methods, resulting in a classification into 9 groups. The variables dendritic thickness, number of branching points per cell and level of stratification were independent of each other. Number of branching points and level of stratification were independent of eccentricity, whereas dendritic thickness was positively dependent (r = 0.37) on it. The frequency distribution of dendritic thickness tended to be multimodal, indicating the presence of at least two cell populations composed of neurons with dendritic diameters either smaller or larger than 1.8 µm ("thin" or "thick" dendrites, respectively). Three cells (4.5%) were bistratified, having thick dendrites, and the others (95.5%) were monostratified. Using k-means cluster analysis, monostratified cells with either thin or thick dendrites were further subdivided according to level of stratification and number of branching points: cells with thin dendrites were divided into 2 groups with outer stratification (0-40%) and 2 groups with inner (50-100%) stratification, whereas cells with thick dendrites were divided into one group with outer and 3 groups with inner stratification. We postulate, that one group of cells with thin dendrites resembles cat ß-cells, whereas one group of cells with thick dendrites includes cells that resemble cat a-cells.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Interference of heating on the antimicrobial activity and chemical composition of Origanum vulgare L. (Lamiaceae) essential oil

Origanum vulgare L. (oregano), Lamiaceae, essential oil has a variety of biological properties and its antimicrobial activity has received a renewed interest for use in food conservation. The aim of this study was to evaluate the interference of heating on the antimicrobial activity and chemical composition of O. vulgare essential oil. The antimicrobial activity of the essential oil kept at room temperature and exposed to different heating temperatures (60, 80, 100 and 120 °C during 1 hour) was evaluated by observing antimicrobial effectiveness at absolute concentration and determining MIC values by the solid medium diffusion procedure. The essential oil chemical composition analysis was performed by GC-MS. O. vulgare essential oil showed interesting antimicrobial activity on all assayed microbial strains (Candida albicans, C.krusei, C. tropicalis, Bacillus cereus, Escherichia coli, Staphylococcus aureus, Yersinia enterocolitica, Salmonella enterica, Serratia marcencens), noted by large growth inhibition zones (30-42 mm). Heating treatment showed no significant interference (p < 0.05) on the essential oil antimicrobial activity, noted by the development of microbial growth inhibition zones with similar or close diameters when evaluating the essential oil kept at room temperature and after exposure to different thermal treatments. MIC values oscillated between 10and 40 µL.mL-1 (20µL.mL-1 for most strains). However, no significant difference (p < 0.05) was noted among the MIC values found for the essential oil aliquots exposed to different temperatures. Moreover, heating did not significantly (p < 0.05) affect the chemical composition of O. vulgare essential oil. Monoterpenes, terpenic compounds and sesquiterpenes were found in the essential oil, with carvacrol (68.06-70.27%) and p-cymene (12.85-15.81%) being the compounds found in the highest amounts. These results showed the thermal stability and intense antimicrobial properties of O. vulgare essential oil and support its possible concomitant use with heating temperatures in order to reach microbial safety in foods.

Heating effects on physicochemical characteristics and antioxidant activity of flaxseed hull oil (Linum usitatissimum L)

The aim of this study was to analyze the effects of heating on some quality characteristics and antioxidant activity of flaxseed hull oil. Polyunsaturated fatty acids (PUFA) and Cox value decreased during heating. Heating process led to considerable increase in saponification value (SV), peroxide value (PV), p-anisidine value (p-AnV), oxidative value (OV) and specific extinction at 232 and 270 nm. There was a significant decrease in oil stability during heating process (1.4-1.0 h). Fuel properties of flaxseed hull oil were also changed after heating treatment. Heating process caused loss of total phenolic acids, total flavanoids, carotenoids and chlorophyll pigments. Phospholipids (PL) content were less changed compared to other bioactive compounds. Antioxidant activity of flaxseed hull oil decreased during heating process.

Chemical changes in biomass during torrefaction

Torrefaction is moderate thermal treatment (~200-300 °C) of biomass in an inert atmosphere. The torrefied fuel offers advantages to traditional biomass, such as higher heating value, reduced hydrophilic nature, increased its resistance to biological decay, and improved grindability. These factors could, for instance, lead to better handling and storage of biomass and increased use of biomass in pulverized combustors. In this work, we look at several aspects of changes in the biomass during torrefaction. We investigate the fate of carboxylic groups during torrefaction and its dependency to equilibrium moisture content. The changes in the wood components including carbohydrates, lignin, extractable materials and ashforming matters are also studied. And at last, the effect of K on torrefaction is investigated and then modeled. In biomass, carboxylic sites are partially responsible for its hydrophilic characteristic. These sites are degraded to varying extents during torrefaction. In this work, methylene blue sorption and potentiometric titration were applied to measure the concentration of carboxylic groups in torrefied spruce wood. The results from both methods were applicable and the values agreed well. A decrease in the equilibrium moisture content at different humidity was also measured for the torrefied wood samples, which is in good agreement with the decrease in carboxylic group contents. Thus, both methods offer a means of directly measuring the decomposition of carboxylic groups in biomass during torrefaction as a valuable parameter in evaluating the extent of torrefaction. This provides new information to the chemical changes occurring during torrefaction. The effect of torrefaction temperature on the chemistry of birch wood was investigated. The samples were from a pilot plant at Energy research Center of the Netherlands (ECN). And in that way they were representative of industrially produced samples. Sugar analysis was applied to analyze the hemicellulose and cellulose content during torrefaction. The results show a significant degradation of hemicellulose already at 240 °C, while cellulose degradation becomes significant above 270 °C torrefaction. Several methods including Klason lignin method, solid state NMR and Py-GC-MS analyses were applied to measure the changes in lignin during torrefaction. The changes in the ratio of phenyl, guaiacyl and syringyl units show that lignin degrades already at 240 °C to a small extent. To investigate the changes in the extractives from acetone extraction during torrefaction, gravimetric method, HP-SEC and GC-FID followed by GC-MS analysis were performed. The content of acetone-extractable material increases already at 240 °C torrefaction through the degradation of carbohydrate and lignin. The molecular weight of the acetone-extractable material decreases with increasing the torrefaction temperature. The formation of some valuable materials like syringaresinol or vanillin is also observed which is important from biorefinery perspective. To investigate the change in the chemical association of ash-forming elements in birch wood during torrefaction, chemical fractionation was performed on the original and torrefied birch samples. These results give a first understanding of the changes in the association of ashforming elements during torrefaction. The most significant changes can be seen in the distribution of calcium, magnesium and manganese, with some change in water solubility seen in potassium. These changes may in part be due to the destruction of carboxylic groups. In addition to some changes in water and acid solubility of phosphorous, a clear decrease in the concentration of both chlorine and sulfur was observed. This would be a significant additional benefit for the combustion of torrefied biomass. Another objective of this work is studying the impact of organically bound K, Na, Ca and Mn on mass loss of biomass during torrefaction. These elements were of interest because they have been shown to be catalytically active in solid fuels during pyrolysis and/or gasification. The biomasses were first acid washed to remove the ash-forming matters and then organic sites were doped with K, Na, Ca or Mn. The results show that K and Na bound to organic sites can significantly increase the mass loss during torrefaction. It is also seen that Mn bound to organic sites increases the mass loss and Ca addition does not influence the mass loss rate on torrefaction. This increase in mass loss during torrefaction with alkali addition is unlike what has been found in the case of pyrolysis where alkali addition resulted in a reduced mass loss. These results are important for the future operation of torrefaction plants, which will likely be designed to handle various biomasses with significantly different contents of K. The results imply that shorter retention times are possible for high K-containing biomasses. The mass loss of spruce wood with different content of K was modeled using a two-step reaction model based on four kinetic rate constants. The results show that it is possible to model the mass loss of spruce wood doped with different levels of K using the same activation energies but different pre-exponential factors for the rate constants. Three of the pre-exponential factors increased linearly with increasing K content, while one of the preexponential factors decreased with increasing K content. Therefore, a new torrefaction model was formulated using the hemicellulose and cellulose content and K content. The new torrefaction model was validated against the mass loss during the torrefaction of aspen, miscanthus, straw and bark. There is good agreement between the model and the experimental data for the other biomasses, except bark. For bark, the mass loss of acetone extractable material is also needed to be taken into account. The new model can describe the kinetics of mass loss during torrefaction of different types of biomass. This is important for considering fuel flexibility in torrefaction plants.

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.

Receipt from Geo. Lloyd of St. Catharines for work done regarding heating and pipes

Receipt from Geo. Lloyd of St. Catharines for work done regarding heating and pipes, Jan. 1, 1877.