Life Cycle Assessment of electricity production in Italy from anaerobic co-digestion of pig slurry and energy crops

This study aims to evaluate the environmental consequences and energy requirements of a biogas production system and its further conversion into bioenergy by means of the Life Cycle Assessment (LCA) methodology. To do so, an Italian biogas plant operating with pig slurry and two energy crops (maize and triticale silages) as feedstock was assessed in detail in order to identify the environmental hotspots. The environmental pro\ufb01le was estimated through six impact categories: abiotic depletion potential (ADP), acidi\ufb01cation potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone layer depletion potential (ODP) and photochemical oxidation potential (POFP). An energy analysis related to the cumulative non-renewable fossil and nuclear energy demand (CED) was also performed, considering this indicator as an additional impact category. According to the results, the biomass production subsystem was identi\ufb01ed as the main environmental key issue in terms of ADP, AP, EP, ODP and CED, with contributions ranging from 26% to 61% of the total impact. Regarding ADP, ODP and CED, these results are mainly related with diesel requirements in agricultural machinery, derived combustion emissions and mineral fertilizers production. Concerning AP and EP the production \ufb01eld emissions derived from fertilizers application was observed as the main contributor. Concerning GWP, this step presents an environmental credit due to the uptake of CO2 during crop growth, which contributes to offset the GHG emissions. The bioenergy production plant signi\ufb01- cantly contributes to the environmental impact in categories such as GWP (43%) and POFP (59%), mostly related with emissions produced in the gas engine and biogas losses. Emissions derived from digestate storage contribute to AP (52%) and EP (41%). The use of the digestate as an organic fertilizer has a bene\ufb01cial role because this action avoids the production and use of mineral fertilizers. A sensitivity analysis was also conducted to assess the in\ufb02uence of variations in important parameters of biogas systems. The environmental pro\ufb01le of the biogas system turned out to be highly dependent on the selection of system boundaries and the allocation method. To sum up, this study aims to assess the environmental performance of a biogas technology available not only in Italy but also in other European countries. The environmental analysis of the process under study highlights the environmental bene\ufb01ts of the co-digestion processes, which not only produces biofuel but also reduces the disposal of solid wastes and produces digestate, with special value in the fertilization of agricultural soil

A model to cost effectively improve productivity in an aluminum cutting and drilling station

The purpose of this study is from a case study develop a model that aims to cost effectively improve productivity in a manufacturing production process. The study highlights the importance of having knowledge about customer needs and using a holistic process perspective when improving productivity to identify the relations between the process stations and by this find problems that cause waste and losses in productivity. Tools and methods used to make production more efficient such as single minute exchange of die, spaghetti diagram, 5s and master production schedule is presented an applied in the model to see how these effect costs, productivity etc. Maintenance effects on quality and productivity in a manufacturing company will as well be covered in this thesis. The results and conclusions finally reveals how the process improvement tools, maintenance and production planning for example effects each other and why it is important to establish an attitude in the company where continuous process improvement should be emphasized

Afrika wohin? – Politik, Wirtschaft und Migration

Osnabrücker Universitätsrede, gehalten am 31. Januar 2019, zugleich Beitrag zur Friedens- und Konfliktforschung im Jahrbuch der Osnabrücker Friedensgespräche 201

Towards a Practical Crowdsensing System for Road Surface Conditions Monitoring

The Internet of Things (IoT) infrastructure, systems, and applications demonstrate potential in serving smart city development. Crowdsensing approaches for road surface conditions monitoring can benefit smart city road information services. Deteriorated roads induce vehicle damage, traffic congestion, and driver discomfort which influence traffic management. In this paper, we propose a framework for monitoring road surface anomalies. We analyze the common road surface types and irregularities as well as their impact on vehicle motion. In addition to the traditional use of sensors available in smart devices, we utilize the vehicle motion sensors (accelerometers and gyroscopes) presently available in most land vehicles. Various land vehicles were used in this paper, spanning different sizes, and year model for extensive road experiments. These trajectories were used to collect and build multiple labeled data sets that were used in the system structure. In order to enhance the performance of the sensor measurements, wavelet packet de-noising is used in this paper to enable efficient classification of road surface anomalies. We adopt statistical, time domain, and frequency domain features to distinguish different road anomalies. The descriptive data sets collected in this paper are used to build, train, and test a system classifier through machine learning techniques to detect and categorize multiple road anomalies with different severity levels. Furthermore, we analyze and assess the capabilities of the smart devices and the other vehicle motion sensors to accurately geo-reference the road surface anomalies. Several road test experiments examine the benefits and assess the performance of the proposed architecture.ManuscriptreceivedSeptember15,2017;revisedDecember20,2017andJanuary29,2018;acceptedFebruary12,2018.DateofpublicationFebruary19,2018;dateofcurrentversionJanuary16,2019.ThisworkwassupportedinpartbytheNaturalSciencesandEngineeringResearchCouncilofCanadaunderGrantSTPGP479248andinpartbytheNPRPthroughtheQatarNationalResearchFund(amemberoftheQatarFoundation)underGrantNPRP9-185-2-096.(Correspondingauthor:AmrS.El-Wakeel.)A.S.El-WakeelandJ.LiarewiththeDepartmentofElectricalandComputerEngineering,Queen?sUniversity,Kingston,ONK7L3N6,Canada(e-mail:[email protected];[email protected])