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The effect of silage additive on the kinetics of biogas production from lignocellulosic perennial crops

Kupryś-Caruk Marta, Lisowski Aleksander, Chomontowski Chrystian

Journal of Water and Land Development

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2023

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no. 56

58--66

EN

The aim of the study was to assess the effect of silage additive containing heterofermentative lactic acid bacteria (LAB) strain of Lactobacillus buchneri species on ensiling quality, as well as methane yield and the kinetics of biogas production from ensiled perennial energy grasses: Miscanthus × giganteus (miscanthus), Spartina pectinata (cordgrass), Panicum virgatum (switchgrass) and Andropogon gerardii (big bluestem). The listed plants are not commonly used for biogas production, their susceptibility to ensiling is also little known, hence the need to investigate their suitability for these processes. Effective methods for increasing the biogas yield from biomass are still demand, hence the research on the use of LAB for this purpose. After harvesting the grasses were cut and ensiled in barrels with and without (controls) the usage of commercial silage inoculant containing Lactobacillus buchneri LN40177. After 90 days of ensiling obtained silages were analysed in order to compare their chemical composition: organic acids content, the loss of dry matter, the differences in particular fibres composition. The silages were then subjected to methane fermentation using OxiTop® sensors and exposed to air in order to check their aerobic stability. The silages prepared with LAB additive had higher concentration of acetic acid than the control silages prepared without LAB addition, which contributed to increased aerobic stability but had no effect on the methane yield of miscanthus, switchgrass and big bluestem. Using the microbial inoculant during ensiling had beneficial effect in terms of reducing the duration of biogas production process from obtained silages: lag phase was shortened, daily biogas production rate was increased and 90% of biogas was produced in a shorter period of time compared to the control silages from investigated grasses. The modified Gompertz model well reflected the kinetics of biogas production process.

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Biogas Recovery from Refinery Oily Sludge by Co-Digestion Followed by Sustainable Approach for Recycling the Residual Digestate in Concrete Mixes

Jasim Hassan S., Ismail Zainab Z.

Advances in Science and Technology. Research Journal

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2022

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Vol. 16, no 5

178--191

EN

This study investigated the potential of biogas recovery from refinery oily sludge (ROS) inoculated with animals’ manure by co-digestion of in lab-scale biodigesters at mesophilic conditions. Cow dung (CD), cattle manure (CM), and poultry manure (PM) were utilized as co-substrates. The biogas production from the co-digestion process exceeds its production from uninoculated ROS by approximately 67.5 %, 22.13% and 21.6% for PM, CM, and CD, respectively. Kinetics of the co-digestion process was well described by the modified Gompertz model. The predicted and experimental values of biogas production were well fitted with R2 > 0.96, suggesting favorable conditions of the digestion process. New approach for recycling the residual digestate to replace freshwater in concrete mixes was carried out. Results of examining the mechanical properties of the residual digestate-modified concrete mixes demonstrated a potential sustainable approach for the disposal of residual digestate in concrete mixes.

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Nonlinear mixed effect modeling in recognition of underlying population dynamics

Foryś Urszula

Mathematica Applicanda

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2020

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Vol. 48, no. 1

87--97

EN

Nonlinear mixed effect modeling (NMEM) is a useful method allowing to fit parameters of the assumed model to the repeated data. In this paper we present results of a theoretical experiment designed to study the effectiveness of NMEM in recognition of the underlying population dynamics. In this experiment we used the logistic equation with fixed parameters to sample population data assuming the log-normal distribution of the parameters. Two sets of data have been created, each of them containing ten time-measurements of the size for one hundred virtual populations. Then, we used NMEM to fit parameters of three most recognizable in tumor dynamics models: logistic, Gompertz and Greenspan model. It occurs that NMEM properly recognized the model structure, that is the fit of Gompertz model is worse (in terms of mean square error) comparing to the others. However, the difference between the fits for the logistic and Greenspan models is not very significant. Moreover, visually all the fits look equally good.

PL

Statystyczna metoda „nonlinear mixed effect modeling” (NMEM) jest przydatną metodą, która pozwala dopasować parametry przyjętego modelu do rzeczywistych danych (np. eksperymentalnych czy klinicznych) pochodzących od różnych osobników tej samej populacji (jak np. dane dotyczące wzrostu konkretnego nowotworu w danej populacji), bądź też różnych populacji mających taką samą dynamikę. W artykule przedstawiamy wyniki pewnego teoretycznego eksperymentu, zaprojektowanego w celu zbadania skuteczności metody NMEM w rozpoznawaniu wyjściowej dynamiki populacji. W eksperymencie wykorzystano równanie logistyczne z ustalonymi parametrami i - zakładając rozkład log-normalny parametrów - wylosowano dane. Utworzono dwa zestawy danych, z których każdy zawiera dziesięć pomiarów liczebności (w zależności od czasu) dla stu wirtualnych populacji. Następnie wykorzystaliśmy NMEM w celu dopasowania do tych danych parametrów trzech najczęściej stosowanych w dynamice nowotworów modeli: logistycznego, Gompertza i Greenspana. Okazuje się, że metoda NMEM pozwoliła na prawidłowe rozpoznanie struktury modelu, co oznacza, że dopasowanie modelu Gompertza jest gorsze (pod względem średniego błędu kwadratowego) w porównaniu do pozostałych modeli. Jednak różnica między dopasowaniem modelu logistycznego i Greenspana nie jest znacząca. Ponadto wizualnie wszystkie dopasowania są porównywalne.

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Zastosowanie modelu Gompertz'a w inżynierii rolniczej

Hanusz Z., Siarkowski Z., Ostrowski K.

Inżynieria Rolnicza

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2008

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R. 12, nr 7(105)

71-77

PL

W pracy przedstawiono charakterystykę modelu Gompertz'a. Opisano znaczenie parametrów występujących w modelu. Pokazano typowe wykresy funkcji oraz przykłady zastosowania ich w rolnictwie. Zasygnalizowano możliwość wykorzystania modelu w optymalizujących systemach ekspertowych dla producentów rolnych.

EN

The paper presents characteristics of the Gompertz model. Definitions of parameters occurring in the model have been provided. Typical function graphs and examples of their application in agriculture are shown, and possibility to use the model in optimising expert systems for agricultural manufacturers is indicated.