Ocena emisije gasova s efektom staklene bašte lanca snabdevanja kukuruzovine

Within this investigation, different scenarios for supply chains of corn stover intended for biogas production were evaluated in terms of emissions of the greenhouse gasses and their impact to global warming. A primary difference between the scenarios was due to application of different stover collection techniques i.e. forage harvester, baling of big rectangular and round bales. It was found that the supply chain, which includes the application of forage harvester for stover collection is characterized by the highest value of impact, approximately 70 and 85 kg CO2 eq MgDM–1, respectively for the usual and reduced corn stover yield. For the supply chain which includes baling of big rectangular bales, these values are around 62 and 68 kg CO2 eq MgDM–1, and for the baling of round bales, values are 61 and 67 kg CO2 eq MgDM–1. Among the analyzed greenhouse gases emissions, the dominant impact is due to the emissions of carbon dioxide. The reduction of the corn stover yield, caused by extreme drought, is followed by higher GHG emissions, first of all due to longer distances during harvest and transportation.U okviru istraživanja su, za različite scenarije lanca snabdevanja kukuruzovinom namenjene za proizvodnju biogasa, određene vrednosti emisija gasova s efektom staklene bašte i ocenjen njihov uticaj na doprinos globalnom zagrevanju. Osnovna razlika između scenarija proizilazi iz načina ubiranja kukuruzovine i to silažnim kombajnom i formiranjem velikih četvrtastih i valjkastih bala. Ustanovljeno je da se lanac snabdevanja sa primenom silažnog kombajna rezultuje najvišom vrednošću uticaja, koja iznosi 70 i 85 kg CO2 ekv MgSM–1, za visok i nizak prinos kukuruzovine respektivno. Za lanac sa ubiranjem u formi četvrtastih bala, te vrednosti iznose 62 i 68 kg CO2 ekv MgSM–1, a za valjkaste bale 61 i 67 kg CO2 ekv MgSM–1. Od analiziranih gasova s efektom staklene bašte, dominantan uticaj ima ugljen-dioksid. Smanjenje prinosa kukuruzovine, usled suše, nepovoljno se odražava na lanac snabdevanja, pre svega zbog dužine puta pri ubiranju i transportu

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

Big data are visually cluttered by overlapping data points. Rather than removing, reducing or reformulating overlap, we propose a simple, effective and powerful technique for density cluster generation and visualization, where point marker (graphical symbol of a data point) overlap is exploited in an additive fashion in order to obtain bitmap data summaries in which clusters can be identified visually, aided by automatically generated contour lines. In the proposed method, the plotting area is a bitmap and the marker is a shape of more than one pixel. As the markers overlap, the red, green and blue (RGB) colour values of pixels in the shared region are added. Thus, a pixel of a 24-bit RGB bitmap can code up to 224 (over 1.6 million) overlaps. A higher number of overlaps at the same location makes the colour of this area identical, which can be identified by the naked eye. A bitmap is a matrix of colour values that can be represented as integers. The proposed method updates this matrix while adding new points. Thus, this matrix can be considered as an up-to-time knowledge unit of processed data. Results show cluster generation, cluster identification, missing and out-of-range data visualization, and outlier detection capability of the newly proposed method

Webbasierte Schwachstellenanalyse an landwirtschaftlichen Biogasanlagen

Eine suboptimale Dimensionierung oder ein ineffizienter Betrieb landwirtschaftlicher Biogasanlagen haben negative Umweltwirkungen und eine mangelhafte Wirtschaftlichkeit zur Folge. Wir stellen hier eine Bewertungsmethode vor, mit der eine betriebsindividuelle Schwachstellenanalyse und eine vergleichende Bewertung von Biogasanlagen möglich werden. Die Methode kombiniert Elemente aus Fuzzy-Sets und Expertensystemen, um Biogasanlagen hinsichtlich verschiedener Kriterien zu bewerten. Die Bewertungsergebnisse für einzelne Anlagen und die Rangfolge in einem Anlagenvergleich sind hierbei unabhängig von der jeweiligen Stichprobe, da die einzelnen Kriterien auf Basis des Standes von Wissenschaft und Technik bewertet werden. Um den Bewertungsalgorithmus einem breiteren Nutzerkreis verfügbar zu machen, wurde die Web-Anwendung „Biogas Doc“ entwickelt, mit welcher die erforderlichen Informationen zur Anlagenkonfiguration sowie die Daten zum Betriebsergebnis der Biogasanlage strukturiert erfasst und dargestellt werden können. Mit der Anwendung kann der Nutzer prinzipiell auch die Auswirkungen von Repowering-Maßnahmen oder Änderungen bei den Einsatzstoffen auf wichtige Leistungskennwerte der Biogasanlage simulieren und damit zuverlässiger planen

Multi-Variable, Multi-Layer Graphical Knowledge Unit for Storing and Representing Density Clusters of Multi-Dimensional Big Data

A multi-variable visualization technique on a 2D bitmap for big data is introduced. If A and B are two data points that are represented using two similar shapes with m pixels, where each shape is colored with RGB color of (0, 0, k), when A ∩ B ≠ ɸ, adding the color of A ∩ B gives higher color as (0, 0, 2k) and the highlight as a high density cluster, where RGB stands for Red, Green, Blue and k is the blue color. This is the hypothesis behind the single variable graphical knowledge unit (GKU), which uses the entire bit range of a pixel for a single variable. Instead, the available bit range of a pixel is split, and a pixel can be used for representing multiple variables (multi-variables). However, this will limit the bit block for single variables and limit the amount of overlapping. Using the same size k (>1) bitmaps (multi-layers) will increase the number of bits per variable (BPV), where each (x, y) of an individual layer represents the same data point. Then, one pixel in a four-layer GKU is capable of showing more than four billion overlapping ones when BPV = 8 bits (2(BPV × number of layers)) Then, the 32-bit pixel format allows the representation of a maximum of up to four dependent variables against one independent variable. Then, a four-layer GKU of w width and h height has the capacity of representing a maximum of (2(BPV × number of layers)) × m × w × h overlapping occurrences

Indicative Marker Microbiome Structures Deduced from the Taxonomic Inventory of 67 Full-Scale Anaerobic Digesters of 49 Agricultural Biogas Plants

There are almost 9500 biogas plants in Germany, which are predominantly operated with energy crops and residues from livestock husbandry over the last two decades. In the future, biogas plants must be enabled to use a much broader range of input materials in a flexible and demand-oriented manner. Hence, the microbial communities will be exposed to frequently varying process conditions, while an overall stable process must be ensured. To accompany this transition, there is the need to better understand how biogas microbiomes respond to management measures and how these responses affect the process efficiency. Therefore, 67 microbiomes originating from 49 agricultural, full-scale biogas plants were taxonomically investigated by 16S rRNA gene amplicon sequencing. These microbiomes were separated into three distinct clusters and one group of outliers, which are characterized by a specific distribution of 253 indicative taxa and their relative abundances. These indicative taxa seem to be adapted to specific process conditions which result from a different biogas plant operation. Based on these results, it seems to be possible to deduce/assess the general process condition of a biogas digester based solely on the microbiome structure, in particular on the distribution of specific indicative taxa, and without knowing the corresponding operational and chemical process parameters. Perspectively, this could allow the development of detection systems and advanced process models considering the microbial diversity

Indicative Marker Microbiome Structures Deduced from the Taxonomic Inventory of 67 Full-Scale Anaerobic Digesters of 49 Agricultural Biogas Plants

Hassa J, Klang J, Benndorf D, et al. Indicative Marker Microbiome Structures Deduced from the Taxonomic Inventory of 67 Full-Scale Anaerobic Digesters of 49 Agricultural Biogas Plants. Microorganisms. 2021;9(7): 1457.There are almost 9500 biogas plants in Germany, which are predominantly operated with energy crops and residues from livestock husbandry over the last two decades. In the future, biogas plants must be enabled to use a much broader range of input materials in a flexible and demand-oriented manner. Hence, the microbial communities will be exposed to frequently varying process conditions, while an overall stable process must be ensured. To accompany this transition, there is the need to better understand how biogas microbiomes respond to management measures and how these responses affect the process efficiency. Therefore, 67 microbiomes originating from 49 agricultural, full-scale biogas plants were taxonomically investigated by 16S rRNA gene amplicon sequencing. These microbiomes were separated into three distinct clusters and one group of outliers, which are characterized by a specific distribution of 253 indicative taxa and their relative abundances. These indicative taxa seem to be adapted to specific process conditions which result from a different biogas plant operation. Based on these results, it seems to be possible to deduce/assess the general process condition of a biogas digester based solely on the microbiome structure, in particular on the distribution of specific indicative taxa, and without knowing the corresponding operational and chemical process parameters. Perspectively, this could allow the development of detection systems and advanced process models considering the microbial diversity

Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics

Hassa J, Tubbesing TJ, Maus I, et al. Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics. Microorganisms. 2023;11(10): 2412.The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD