Browsing by Subject "modeling"
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(2013)The trend of energy policy in European Union as well as in international context has lately been to increase the share of renewable biofuels. The causes for this are global warming, shrinking reserves of fossil fuels and governments' aspiration for energy independence. Microalgae have shown to be a potential source of biofuels. Though cultivation of microalgae has a long history, has production for fuel yet been unprofitable. Production has become more effective as cultivation has shifted from open ponds to controlled photobioreactors but to achieve effective cultivation methods substantially more understanding on the ecophysiology of microalgae is needed. The aim of my thesis was to research the optimal light intensity and temperature of photosynthesis for three microalgae (Chlorella pyrenoidosa, Euglena gracilis and Selenastrum sp.), which are the main parameters limiting the level of photosynthesis in nutrient rich environments such as photobioreactor. The research strains were incubated in eight light intensities (0,15-250 µmol m-2 s-2) and in 5-6 temperatures (10-35 °C). Photosynthetic activity was determined with radiocarbon method which is based on the stoichiometry of photosynthesis. The purpose of radiocarbon method is to estimate how much dissolved carbon dioxide do the algae assimilate when photosynthesizing. In the method the algae are incubated in light and dark bottles where certain amount of radiocarbon (14C) has been added as a tracer. The algae fix 14C in the proportion to available 12C. 14C method has become the most common way to measure the photosynthesis of microalgae. All of the algal strains grew in 10-30 °C but C. pyrenoidosa was the only one which grew also in 35 °C. The data was analyzed by fitting them with two photosynthesis-light intensity relationship models and one photosynthesis-temperature relationship model and as a result values of essential parameters, i.e. optimal light intensity (Iopt) and temperature (Topt) for photosynthesis, could be estimated. The model which gave the best fit was chosen to describe the photosynthesis-light intensity relationship. The optimal light intensity for C. pyrenoidosa ranged between 121–242 µmol m-2 s-2 and optimal temperature was 15 °C. Corresponding values for E. gracilis were 117-161 µmol m-2 s-2 and 24,1 °C, and for Selenastrum sp. 126-175 µmol m-2 s-2 and 16,7 °C. Q10-values were also determined. With all research strains, the level of photosynthesis increased as light intensity and temperature grew until optimal values were reached. The strains tolerated higher light intensities in warmer temperatures but after reaching the optimal temperature, the level of photosynthesis did not increase any more with elevating temperature. Robust algal strains, i.e. strains, that are most adaptable in terms of light intensity and temperature, are the most prominent ones for biofuel production. From these research strains the most adaptable strain in terms of light intensity was C. pyrenoidosa and in terms of temperature Selenastrum sp. C. pyrenoidosa had superior carbon fixation rate in relation to cell size. Therefore it can be concluded that C. pyrenoidosa is the most suitable algal strains for biofuel applications of the strains assessed here.
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(2011)The aim of this study was to develop mathematical energy balance models for early and middle lactation period of dairy cows. The traits for predicting were information of diet, feed, milk production, milk composition, body weight and body condition score. This study was a part of development work of KarjaKompassi-project. The data used in this study was based on 12 feeding experiments performed in Finland. The complete data from the studies included 2647 weekly records from multiparous dairy cows and 1070 weekly records from primiparous dairy cows. The data was collected from calving to 8-28 weeks of lactation. Three-fourths of the totals of 344 dairy cows were Finnish Ayshire cows and the rest of the cows were Friesian Cattle. The cows were fed by the Finnish feeding standards. The data was handled by the Mixed-procedure of the SAS-programme. The outliers were removed with Tukey´s method. The relationship between energy balance and predictor traits was studied with correlation analysis. The regression analysis was used to predicting energy balance. To quantify the relationship of lactation day to energy balance, 5 functions were fitted. The random factor was a cow in the experiment. The model fit was assessed by residual mean square error, coefficient of determination and Bayesian information criterion. The best models were validated in the independent data. Ali-Schaeffer achieved the highest fit functions. It was used by the basal model. The error in every model grew after the 12th lactation week, because the number of records decreased and energy balance turned positive. The proportion of concentrate in the diets and concentrate dry matter intake index were the best predictors of energy balance from traits of diet. Milk yield, ECM, milk fat and milk fat-protein ratio were good predictors during lactation period. The RMSE was lower when ECM was standardized. The body weight and body condition score didn’t improve the predictive value of the basal model. The models can be used to predict energy balance in the herd level, but they are not applicable for predicting individual cow energy balance.
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(2023)This thesis investigates the impact of the energy crisis on air quality in the Po Valley, Italy, with a focus on the effect of changes in methane consumption over the potential changes in wood burning consumption. The study employs the WRF-CHIMERE model to simulate the consequences of varying wood burning consumption on air quality. Observational data of benzo[a]pyrene and PM2.5 concentrations in the Po Valley are also analyzed. The results indicate that meteorological conditions have a significant influence on air quality, overshadowing the potential effects of emission changes resulting from the energy crisis. However, we observed a model’s bias correlated to the planetary boundary layer height, which could have influenced this result. Further investigation is necessary to correct this bias and comprehensively understand the relationship between energy consumption, air quality, and meteorology in the Po Valley.
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(2022)Determining the optimal rental price of an apartment is typically something that requires a real estate agent to gauge the external and internal features of the apartment, and similar apartments in the vicinity of the one being examined. Hedonic pricing models that rely on regression are commonplace, but those that employ state of the art machine learning methods are still not widespread. The purpose of this thesis is to investigate an optimal machine learning method for predicting property rent prices for apartments in the Greater Helsinki area. The project was carried out at the behest of a client in the real estate investing business. We review what external and inherent apartment features are the most suitable for making predictions, and engineer additional features that result in predictions with the least error within the Greater Helsinki area. Combining public demographic data from Tilastokeskus (Statistics Finland) and data from the online broker Oikotie Oy gives rise to a model that is comparable to contemporary commercial solutions offered in Finland. Using inverse distance weighting to interpolate and generate a price for the coordinates of the new apartment was also found to be crucial in developing an performant model. After reviewing models, the gradient boosting algorithm XGBoost was noted to fare the best for this regression task.
Now showing items 1-4 of 4