• Energy Research

Abstract Under conditions of low funding for the production of “green energy” in Poland, it became necessary to search for other – cheaper sources of biomass and the development of more efficient technologies. The maize straw is waste material arising in the production of grain. Therefore currently has no wider application and the cost of acquisition is several times lower than in case of maize silage. This paper presents the results of research on biogas efficiency of the maize straw silage, the dynamics of the fermentation process and the decomposition time of biomass under the meso- and thermophilic conditions. Moreover, the exploitation costs of a biogas plant working on this substrate and maize silage have been compared. It has been proved that thermophilic fermentation is significantly shorter (17%) than mesophilic and permits to increase biogas production (8.6%) and methane content (9.3%). In turn, mesophilic fermentation has more stable pH changes in comparison with the thermophilic technology. However, it is related to inhibition of the propionic acid, which can be of great importance in case of continuous fermentation. On the basis of energetic calculations it was shown that the substitution of the maize silage with the maize straw silage allows for nearly three-fold costs reduction and thus increase of the biogas plant profitability.

Abstract Under conditions of low funding for the production of “green energy” in Poland, it became necessary to search for other – cheaper sources of biomass and the development of more efficient technologies. The maize straw is waste material arising in the production of grain. Therefore currently has no wider application and the cost of acquisition is several times lower than in case of maize silage. This paper presents the results of research on biogas efficiency of the maize straw silage, the dynamics of the fermentation process and the decomposition time of biomass under the meso- and thermophilic conditions. Moreover, the exploitation costs of a biogas plant working on this substrate and maize silage have been compared. It has been proved that thermophilic fermentation is significantly shorter (17%) than mesophilic and permits to increase biogas production (8.6%) and methane content (9.3%). In turn, mesophilic fermentation has more stable pH changes in comparison with the thermophilic technology. However, it is related to inhibition of the propionic acid, which can be of great importance in case of continuous fermentation. On the basis of energetic calculations it was shown that the substitution of the maize silage with the maize straw silage allows for nearly three-fold costs reduction and thus increase of the biogas plant profitability.

Abstract The project was designed to develop, test and validate an original Neural Model describing ammonia emissions generated in composting sewage sludge. The composting mix was to include the addition of such selected structural ingredients as cereal straw, sawdust and tree bark. All created neural models contain 7 input variables (chemical and physical parameters of composting) and 1 output (ammonia emission). The α data file was subdivided into three subfiles: the learning file (ZU) containing 330 cases, the validation file (ZW) containing 110 cases and the test file (ZT) containing 110 cases. The standard deviation ratios (for all 4 created networks) ranged from 0.193 to 0.218. For all of the selected models, the correlation coefficient reached the high values of 0.972–0.981. The results show that he predictive neural model describing ammonia emissions from composted sewage sludge is well suited for assessing such emissions. The sensitivity analysis of the model for the input of variables of the process in question has shown that the key parameters describing ammonia emissions released in composting sewage sludge are pH and the carbon to nitrogen ratio (C:N).

Abstract The project was designed to develop, test and validate an original Neural Model describing ammonia emissions generated in composting sewage sludge. The composting mix was to include the addition of such selected structural ingredients as cereal straw, sawdust and tree bark. All created neural models contain 7 input variables (chemical and physical parameters of composting) and 1 output (ammonia emission). The α data file was subdivided into three subfiles: the learning file (ZU) containing 330 cases, the validation file (ZW) containing 110 cases and the test file (ZT) containing 110 cases. The standard deviation ratios (for all 4 created networks) ranged from 0.193 to 0.218. For all of the selected models, the correlation coefficient reached the high values of 0.972–0.981. The results show that he predictive neural model describing ammonia emissions from composted sewage sludge is well suited for assessing such emissions. The sensitivity analysis of the model for the input of variables of the process in question has shown that the key parameters describing ammonia emissions released in composting sewage sludge are pH and the carbon to nitrogen ratio (C:N).

Abstract The paper presents the use of classical statistical methods and methods based on neural modeling in short-term forecasting of electric energy from photovoltaic conversion. A detailed analysis of the input data measured in central Poland (Poznan, 52°25′ N, 16°56′ E) showed that some variables like air pressure and the length of the day are statistically insignificant. The values of kurtosis, skewness and results of applied tests, to check the normality of the distribution of dependent variable in the form of daily electricity production, indicate that the linear regression models should not be the only method in forecast process. The result of neural modeling using implemented network designer is RBF 6: 6-5-1: 1 model with quality test approximately 93% and the RMS error of 0.02%. The input parameters necessary for the operation of proposed ANN model are: number of sunny hours, length of the day, air pressure, maximum air temperature, daily insolation and cloudiness.

Abstract The paper presents the use of classical statistical methods and methods based on neural modeling in short-term forecasting of electric energy from photovoltaic conversion. A detailed analysis of the input data measured in central Poland (Poznan, 52°25′ N, 16°56′ E) showed that some variables like air pressure and the length of the day are statistically insignificant. The values of kurtosis, skewness and results of applied tests, to check the normality of the distribution of dependent variable in the form of daily electricity production, indicate that the linear regression models should not be the only method in forecast process. The result of neural modeling using implemented network designer is RBF 6: 6-5-1: 1 model with quality test approximately 93% and the RMS error of 0.02%. The input parameters necessary for the operation of proposed ANN model are: number of sunny hours, length of the day, air pressure, maximum air temperature, daily insolation and cloudiness.

The integrated production of bioethanol and biogas makes it possible to optimise the production of carriers from renewable raw materials. The installation analysed in this experimental paper was a hybrid system, in which waste from the production of bioethanol was used in a biogas plant with a capacity of 1 MWe. The main objective of this study was to determine the energy potential of biomass used for the production of bioethanol and biogas. Based on the results obtained, the conversion rate of the biomass—maize, in this case—into bioethanol was determined as the efficiency of the process of bioethanol production. A biomass conversion study was conducted for 12 months, during which both maize grains and stillage were sampled once per quarter (QU-I, QU-II, QU-III, QU-IV; QU—quarter) for testing. Between 342 L (QU-II) and 370 L (QU-I) of ethanol was obtained from the organic matter subjected to alcoholic fermentation. The mass that did not undergo conversion to bioethanol ranged from 269.04 kg to 309.50 kg, which represented 32.07% to 36.95% of the organic matter that was subjected to the process of bioethanol production. On that basis, it was concluded that only two-thirds of the organic matter was converted into bioethanol. The remaining part—post-production waste in the form of stillage—became a valuable raw material for the production of biogas, containing one-third of the biodegradable fraction. Under laboratory conditions, between 30.5 m3 (QU-I) and 35.6 m3 (QU-II) of biogas per 1 Mg of FM (FM—fresh matter) was obtained, while under operating conditions, between 29.2 m3 (QU-I) and 33.2 m3 (QU-II) of biogas was acquired from 1 Mg of FM. The Biochemical Methane Potential Correction Coefficient (BMPCC), which was calculated based on the authors’ formula, ranged from 3.2% to 7.4% in the analysed biogas installation.

The integrated production of bioethanol and biogas makes it possible to optimise the production of carriers from renewable raw materials. The installation analysed in this experimental paper was a hybrid system, in which waste from the production of bioethanol was used in a biogas plant with a capacity of 1 MWe. The main objective of this study was to determine the energy potential of biomass used for the production of bioethanol and biogas. Based on the results obtained, the conversion rate of the biomass—maize, in this case—into bioethanol was determined as the efficiency of the process of bioethanol production. A biomass conversion study was conducted for 12 months, during which both maize grains and stillage were sampled once per quarter (QU-I, QU-II, QU-III, QU-IV; QU—quarter) for testing. Between 342 L (QU-II) and 370 L (QU-I) of ethanol was obtained from the organic matter subjected to alcoholic fermentation. The mass that did not undergo conversion to bioethanol ranged from 269.04 kg to 309.50 kg, which represented 32.07% to 36.95% of the organic matter that was subjected to the process of bioethanol production. On that basis, it was concluded that only two-thirds of the organic matter was converted into bioethanol. The remaining part—post-production waste in the form of stillage—became a valuable raw material for the production of biogas, containing one-third of the biodegradable fraction. Under laboratory conditions, between 30.5 m3 (QU-I) and 35.6 m3 (QU-II) of biogas per 1 Mg of FM (FM—fresh matter) was obtained, while under operating conditions, between 29.2 m3 (QU-I) and 33.2 m3 (QU-II) of biogas was acquired from 1 Mg of FM. The Biochemical Methane Potential Correction Coefficient (BMPCC), which was calculated based on the authors’ formula, ranged from 3.2% to 7.4% in the analysed biogas installation.

Poland ranks among the leading European countries in terms of greenhouse gas (GHG) emissions. Many European countries have higher emissions per capita than the EU average. This research aimed to quantify the complex relationships between the consumption variables of the main fossil fuels, accounting for economic indicators such as population and gross domestic product (GDP) in relation to GHG emissions. This research attempted to find similarities in the group of 16 analyzed European countries. The hypothesis of an inverted U-shaped environmental Kuznets curve (EKC) was tested. The resulting multiple regression models showed similarities in one group of countries, namely Poland, Germany, the Czech Republic, Austria and Slovakia, in which most of the variables related to the consumption of fossil fuels, including HC and BC simultaneously, are statistically significant. The HC variable is also significant in Denmark, Estonia, the Netherlands, Finland and Bulgaria, and BC is also significant in Lithuania, Greece and Belgium. Moreover, results from Ireland, the Netherlands, and Belgium indicate a negative impact of population on GHG emissions, and in the case of Germany, the hypothesis of an environmental Kuznets curve can be accepted.