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Bestimmung der metabolischen Aktivität von Mikroorganismen während des Biogasbildungsprozesses
(2009)
Characterizing volcanic ash elements from the 2015 eruptions of bromo and raung volcanoes, Indonesia
(2020)
The volcanic eruptions of Mt. Bromo and Mt. Raung in East Java, Indonesia, in 2015 perturbed volcanic materials and affected surface-layer air quality at surrounding locations. During the episodes, the volcanic ash from the eruptions influenced visibility, traffic accidents, flight schedules, and human health. In this research, the volcanic ash particles were collected and characterized by relying on the detail of physical observation. We performed an assessment of the volcanic ash elements to characterize the volcanic ash using two different methods which are aqua regia extracts followed by MP-AES and XRF laboratory test of bulk samples. The analysis results showed that the volcanic ash was mixed of many materials, such as Al, Si, P, K, Ca, Ti, V, Cr, Mn, Fe, Ni, and others. Fe, Si, Ca, and Al were found as the major elements, while the others were the trace elements Ba, Cr, Cu, Mn, P, Mn, Ni, Zn, Sb, Sr, and V with the minor concentrations. XRF analyses showed that Fe dominated the elements of the volcanic ash. The XRF analysis showed that Fe was at 35.40% in Bromo and 43.00% in Raung of the detected elements in bulk material. The results of aqua regia extracts analyzed by MP-AES were 1.80% and 1.70% of Fe element for Bromo and Raung volcanoes, respectively.
Heavy-duty trucks are one of the main contributors to greenhouse gas emissions in German traffic. Drivetrain electrification is an option to reduce tailpipe emissions by increasing energy conversion efficiency. To evaluate the vehicle’s environmental impacts, it is necessary to consider the entire life cycle. In addition to the daily use, it is also necessary to include the impact of production and disposal. This study presents the comparative life cycle analysis of a parallel hybrid and a conventional heavy-duty truck in long-haul operation. Assuming a uniform vehicle glider, only the differing parts of both drivetrains are taken into account to calculate the environmental burdens of the production. The use phase is modeled by a backward simulation in MATLAB/Simulink considering a characteristic driving cycle. A break-even analysis is conducted to show at what mileage the larger CO2eq emissions due to the production of the electric drivetrain are compensated. The effect of parameter variation on the break-even mileage is investigated by a sensitivity analysis. The results of this analysis show the difference in CO2eq/t km is negative, indicating that the hybrid vehicle releases 4.34 g CO2eq/t km over a lifetime fewer emissions compared to the diesel truck. The break-even analysis also emphasizes the advantages of the electrified drivetrain, compensating the larger emissions generated during production after already a distance of 15,800 km (approx. 1.5 months of operation time). The intersection coordinates, distance, and CO2eq, strongly depend on fuel, emissions for battery production and the driving profile, which lead to nearly all parameter variations showing an increase in break-even distance.
In many cities, diesel buses are being replaced by electric buses with the aim of reducing local emissions and thus improving air quality. The protection of the environment and the health of the population is the highest priority of our society. For the transport companies that operate these buses, not only ecological issues but also economic issues are of great importance. Due to the high purchase costs of electric buses compared to conventional buses, operators are forced to use electric vehicles in a targeted manner in order to ensure amortization over the service life of the vehicles. A compromise between ecology and economy must be found in order to both protect the environment and ensure economical operation of the buses.
In this study, we present a new methodology for optimizing the vehicles’ charging time as a function of the parameters CO₂eq emissions and electricity costs. Based on recorded driving profiles in daily bus operation, the energy demands of conventional and electric buses are calculated for the passenger transportation in the city of Aachen in 2017. Different charging scenarios are defined to analyze the influence of the temporal variability of CO₂eq intensity and electricity price on the environmental impact and economy of the bus. For every individual day of a year, charging periods with the lowest and highest costs and emissions are identified and recommendations for daily bus operation are made. To enable both the ecological and economical operation of the bus, the parameters of electricity price and CO₂ are weighted differently, and several charging periods are proposed, taking into account the priorities previously set. A sensitivity analysis is carried out to evaluate the influence of selected parameters and to derive recommendations for improving the ecological and economic balance of the battery-powered electric vehicle.
In all scenarios, the optimization of the charging period results in energy cost savings of a maximum of 13.6% compared to charging at a fixed electricity price. The savings potential of CO₂eq emissions is similar, at 14.9%. From an economic point of view, charging between 2 a.m. and 4 a.m. results in the lowest energy costs on average. The CO₂eq intensity is also low in this period, but midday charging leads to the largest savings in CO₂eq emissions. From a life cycle perspective, the electric bus is not economically competitive with the conventional bus. However, from an ecological point of view, the electric bus saves on average 37.5% CO₂eq emissions over its service life compared to the diesel bus. The reduction potential is maximized if the electric vehicle exclusively consumes electricity from solar and wind power.
Due to the Renewable Energy Act, in Germany it is planned to increase the amount of renewable energy carriers up to 60%. One of the main problems is the fluctuating supply of wind and solar energy. Here biogas plants provide a solution, because a demand-driven supply is possible. Before running such a plant, it is necessary to simulate and optimize the process. This paper provides a new model of a biogas plant, which is as accurate as the standard ADM1 model. The advantage compared to ADM1 is that it is based on only four parameters compared to 28. Applying this model, an optimization was installed, which allows a demand-driven supply by biogas plants. Finally the results are confirmed by several experiments and measurements with a real test plant.