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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hempel, Gotthilf;

    Johann Heinrich von Thünen-Institute, Federal Research Institute of Rural Areas, Forestry and Fisheries began publishing the Informationen aus der Fischereiforschung - Information on Fishery Research in 2010

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ OceanDocsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    OceanDocs
    Article . 1963
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ OceanDocsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      OceanDocs
      Article . 1963
      Data sources: OceanDocs
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: R. Sierens; J. Demuynck; M. De Paepe; S. Verhelst;
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Juelich Shared Elect...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Juelich Shared Elect...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Beier, H.;
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DESY Publication Dat...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DESY Publication Dat...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Hampel, Uwe; Dittmeyer, Roland; Patyk, Andreas; Wetzel, Thomas; +6 Authors

    Energieeffizienz ist ein wesentlicher Baustein nachhaltigen Wirtschaftens und unabdingbare Voraussetzung zur Erreichung aktueller klimapolitischer Ziele. Die von der Helmholtz‐Gemeinschaft Deutscher Forschungszentren geförderte Energie‐Allianz „Energieeffiziente chemische Mehrphasenprozesse”︁ bündelt Kompetenzen und Forschungsaktivitäten im Bereich der chemischen Verfahrenstechnik mit dem Ziel der Weiterentwicklung von Entwurfs‐ und Auslegungsmethoden, numerischen und experimentellen Techniken sowie neuer Messeverfahren zur Effizienzsteigerung chemischer Mehrphasenprozesse. Energy efficiency is an essential building block of a sustainable economy and an indispensable prerequisite for achieving current international climate goals. The Energy Alliance "Energy efficient multiphase chemical processes" funded by the Helmholtz Association combines expertise and research activities in the field of chemical process engineering with the objective to further develop design tools, numerical and experimental techniques as well as new measurement techniques for increased efficiency of chemical multiphase processes.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Chemie Ingenieur Tec...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Chemie Ingenieur Tec...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pitz-Paal, Robert;

    1. Warum Solarenergienutzung massiv ausbauen ? 2. Markt und Kostenentwicklung bei der PV 3. Markt und Kostenentwicklung bei der CSP mit Speicher 4. Szenarien für den Mix aus PV und CSP 5. Fazit > HGF PÜW > Prof. Robert DLR.de • Chart 2 Pitz-Paal , • > 08.11.2016

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DLR publication serv...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DLR publication server
    Conference object . 2016
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DLR publication server
    Other literature type . 2016
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DLR publication serv...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DLR publication server
      Conference object . 2016
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DLR publication server
      Other literature type . 2016
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bauernhansl, Thomas;

    Der Vortrag DIE PRODUKTION DER ZUKUNFT ZWISCHEN MASS PERSONALIZATION UND MASS SUSTAINABILITY behandelt die Veränderungen in der Produktion im Rahmen von Industrie 4.0. Zunächst wird Deutschlands Position im internationalen Vergleich ausführlich dargestellt und die Potenziale der Smart Factory aufgezeigt. Im Anschluss wird der innovative Stuttgarter Forschungscampus Arena2036 vorgestellt und zum Schluss werden die disruptiven Geschäftsmodelle präsentiert, die mit Industrie 4.0 implementiert werden. Bauernhansl erläutert aus internationaler Perspektive die regionalen Rahmenbedingungen der Produktion und erklärt, wie im Rahmen einer neuen Produktionsarchitektur alles in der Fabrik smart wird. Er gibt eine Einschätzung der Industrie 4.0 Readiness, wobei er zum Schluss kommt, dass Deutschland als Frontrunner die beste Position in Europa hat. Der Vortrag wirft einen Blick in die Zukunft der Smart Factory samt Anwendungsbeispielen. Er beschreibt die nächste Ebene der Dezentralisierung hin zur Fabrik als cyber-physischem Produktionssystem. Die Optimierung der Wertschöpfung im Rahmen von I4.0 sowie die Potenziale dazu werden beschrieben und schließlich wird ARENA2036 - der Stuttgart Research Campus Active Research Environment for the Next Generation of Automobiles mit seinen innovativen Produktionsansätzen vorgestellt. Schließlich fasst Bauernhansl die innovativen disruptiven Geschäftsmodelle im Rahmen von Industrie 4.0 und die Erfolgsfaktoren für die Industrie zusammen.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fraunhofer-ePrintsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Fraunhofer-ePrintsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Schumann, Karen;
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Library Ideasarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Article . 2008
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Library Ideasarrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Michaelis, Peter;

    Der Konflikt zwischen Ökologie und Ökonomie wird dadurch unnötig verschärft, daß wirtschaftswissenschaftliche Prinzipien und Erkenntnisse bei der Gestaltung einer ökonomisch rationalen Umweltpolitik nur unzureichend berücksichtigt werden. Die Ziele der Umweltpolitik werden von der Politik häufig nicht nach ihrer tatsächlichen Dringlichkeit, sondern nach dem Kriterium ihrer Öffentlichkeitswirkung festgelegt. Beispielhaft hierfür steht die Verpackungsverordnung mit ihren Recyclingquoten, die ihrer Höhe nach mit ökonomischer Rationalität teilweise nicht mehr zu vereinbaren sind. Im Gegensatz zum Ordnungsrecht mit seinen Ge- und Verboten lassen ökonomische Instrumente im Umweltschutz (Abgaben, Steuern, handelbare Nutzungsrechte) den Betroffenen Spielraum für individuelle ökonomische Anpassungsentscheidungen. Dementsprechend werden die für den Umweltschutz aufgewendeten Ressourcen dorthin gelenkt, wo sie den größten Entlastungseffekt erzielen. Ökonomische Instrumente ermöglichen mithin mehr Umweltschutz für weniger Geld. Darüber hinaus löst der permanente Kostendruck bei Einsatz ökonomischer Instrumente Innovationsanreize aus, die bei ordnungsrechtlichem Vorgehen durch den staatlicherseits vorgeschriebenen „Stand der Technik" blockiert sind. Die Anwendung ökonomischer Instrumente in der umweltpolitischen Praxis der Bundesrepublik beschränkt sich auf wenige Ausnahmen, die zudem einer marktwirtschaftlichen Umweltpolitik kaum entsprechen. Beispielsweise büßte die 1981 als richtungsweisend konzipierte Abwasserabgabe bereits im Vorfeld der Beratungen die ihr ursprünglich zugedachte Anreizwirkung weitgehend ein. Sie wurde zu einem Vollzugsinstrument des Ordnungsrechts degradiert. Die Einführung ökonomischer Instrumente stellt eine grundlegende institutionelle Innovation dar, die die bestehenden Besitzstände verändern würde. Der für ihre Einführung erforderliche Konsens zwischen den Gesellschaftsgruppen ist daher nur schwer zu erreichen. Den praktischen Einsatzmöglichkeiten ökonomischer Instrumente sind bei realistischer Betrachtung der technischen, ökonomischen, ökologischen und rechtlichen Rahmenbedingungen engere Grenzen gesetzt, als eine Betrachtung erwarten läßt, die lediglich auf die theoretische Vorteilhaftigkeit dieser Instrumente abstellt. Dennoch kann die stärkere Berücksichtigung ökonomischer Konzepte im Umweltschutz zu einem wesentlichen Fortschritt in Richtung auf eine ökonomisch rationale Umweltpolitik beitragen.

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    EconStor
    Research . 1996
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    OPUS Augsburg
    Part of book or chapter of book . 2019
    Data sources: OPUS Augsburg
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      EconStor
      Research . 1996
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      OPUS Augsburg
      Part of book or chapter of book . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Köllner, Patrick;

    Für das erste Quartal 2006 wurde ein Wachstum von 6,2 Prozent vermeldet; dies war das beste Ergebnis seit dem 4. Quartal 2002. Für das gesamte Jahr 2006 wird ein Wirtschaftswachstum von rund 5 Prozent prognostiziert (KDI: 5,3%, KERI: 4,9%). Damit dürfte die Entwicklung der weltweit elftgrößten Wirtschaft in diesem Jahr besser ausfallen, als in den drei vorangegangenen Jahren, in denen Wachstumsraten zwischen 3,1 und 4 Prozent erreicht wurden. Getragen wird das Wirtschaftswachstum nicht zuletzt von einer Wiederbelebung der inländischen Nachfrage, die zuvor noch die wirtschaftliche Entwicklung gebremst hatte. Angesichts der weiterhin dynamischen Entwicklung im benachbarten China dürften auch die Exporte weiter anziehen, wenn auch nicht mehr in dem Tempo wie in den Jahren zuvor. Grund hierfür ist das schlechter gewordene Wechselkursverhältnis zum US-Dollar, das koreanische Exporte teurer macht. Ende Juni 2006 entsprachen knapp 970 koreanische Won einem US-Dollar, 2005 waren es im Jahresdurchschnitt 1.024 Won pro US-Dollar gewesen. Mit einem Handelsvolumen von gut 545 Mrd. US-Dollar war Korea 2005 weltweit die Nummer 12. Koreas Leistungsbilanzüberschuss, der 2005 noch 16,1 Mrd. US-Dollar aufmachte, dürfte 2006 aber deutlich zurückgehen; prognostiziert wurde im April ein Überschuss von rund 3 Mrd. US-Dollar (AHK Informationsdienst Seoul, April 2006; KERI Economic Bulletin, April 2006). Korea: Politik, Wirtschaft, Gesellschaft, 2006: Korea: Politik, Wirtschaft, Gesellschaft

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    https://dx.doi.org/10.11588/kj...
    Article . 2018
    License: CC BY SA
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      https://dx.doi.org/10.11588/kj...
      Article . 2018
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    Authors: Beltramo, Tanja;

    Die Biogasherstellung ist ein sehr komplexer Prozess, der durch das Zusammenspiel unterschiedlicher Mikroorganismen und einem mehrstufigen Prozessablauf gekennzeichnet ist. Variationen in der Mikroflora können durch das verwendete Substrat, die Beschickung sowie die Temperatur gegeben sein, wobei ein Großteil der beteiligten Mikroorganismen sogar unbekannt sein kann. Die Überwachung und Analyse solcher Prozesse sind zumeist sehr zeit- und kostenintensiv. Die VDI-Richtlinie 4630 beschreibt analytische Methoden zur Untersuchung der Vergärung organischer Stoffe, Substratcharak¬terisierung, Probenahme, Stoffdatenerhebung und Gärversuche, die zur Optimierung von Biogasanalgen dienen. Die in der Richtlinie beschriebenen Methoden erfordern jedoch eine entsprechende Laborausstattung, dafür ausgebildetes Personal und sind sehr aufwändig. Deshalb ist eine moderne Alternative zur Prozessanalyse und -optimierung dringend erforderlich, um gezielt wesentliche Prozessgrößen schnell, effizient und kostengünstig zu identifizieren. Das Ziel der vorliegenden Dissertation ist es eine Methode zu entwickeln, welche eine schnelle und effiziente Analyse der Biogasproduktionsprozesse ermöglicht. In dieser Arbeit soll mithilfe von computerbasierten Verfahren untersucht werden, ob wesentliche Prozessgrößen identifiziert werden können, deren Messung für die Führung der Biogasanlagen ausschlaggebende Informationen zur Verfügung stellt. Neben mathematischen Modellen werden auch Optimierungsalgorithmen für die Identifikation der wesentlichen Einflussgrößen verwendet. Die für die Modellierung verwendeten Datensätze wurden experimentell im Rahmen des Projektes ‚,Biogas Biocoenosis‘‘ (FNR 22010711, Dr. Michael Klocke, Leibniz-Institut für Agrartechnik und Bioökonomie e. V., Potsdam) und „Biogas-Enzyme“ (FKZ 22027707, Dr. Monika Heiermann, Leibniz-Institut für Agrartechnik und Bioökonomie e. V., Potsdam) erhoben. Zusätzlich wurden Prozessdaten mithilfe des Anaerobic Digestion Modell No.1(ADM1) simuliert. Die chemischen Prozessgrößen stellen die unabhängigen Prozessvariablen dar. Die Biogasausbeute wird als die abhängige Zielprozessgröße verwendet. Die Vorhersage der Biogasausbeute wird mithilfe linearer und nicht linearer mathematischer Modelle durchgeführt. Dafür werden die Partial-Least-Squares-Regression (PLSR), die Locally-Weighted-Regression (LWR) und künstliche neuronale Netzte (ANN, Artificial Neural Networks) implementiert. Um die wesentlichen unabhängigen Prozessvariablen zu identifizieren, werden folgende Optimierungsalgorithmen verwendet: ein Ameisen-Kolonie-Optimierungsalgorithmus (ACO) und der genetische Algorithmus (GA). Die Qualität der Vorhersage wird anhand des Vorhersagefehlers (Root Mean Square Error, RMSE) und des Bestimmtheitsmaßes (R2) bewertet. Anhand der entwickelten Methode konnte die gewünschte Zielgröße, Biogasausbeute, vorhergesagt werden. Die durchgeführte Variablenselektion mittels metaheuristischer Optimierungsverfahren verbesserte die Vorhersagekraft der Modelle und reduzierte gleichzeitig die Anzahl der unabhängigen Prozessvariablen. Als wesentliche Prozessvariablen wurden folgende identifiziert: hydraulische Verweilzeit, Trockensubstanz, Gehalt an Neutral-Detergentien-Faser, Säure-Detergentien-Lignin-Gehalt und n-Buttersäure. Die besten Vorhersageergebnisse wurden mit Hilfe der künstlichen neuronalen Netze erzielt. Die Modelle hatten den kleinsten Fehler der Vorhersage und das höchste Bestimmtheitsmaß. Die erfolgreiche Umsetzung der mathematischen Verfahren zeigt, dass moderne mathematische Methoden eine gute Alternative für die Analyse und Optimierung komplexer biologischer Prozesse darstellen. Für eine abschließende Bewertung sind jedoch auch weitere experimentelle Untersuchungen notwendig, in denen die Prozessführung basierend auf den identifizierten Prozessvariablen durchgeführt wird. The production of biogas is very complex process, which runs in some stages involving different microorganisms. Microbiological diversity of the process depends mainly on the composition of substrate and ambient conditions, such as process temperature. The fact is, the development and composition of the microbiological communities of the process are difficult to predict. Thus, the control and evaluation of such complex biological processes are very time consuming and expensive. In Germany the evaluation of the biogas plants can be performed according to the VDI-Norm 4630, which describes the methods for the evaluation of fermentation of organic materials including characterization of the substrate, sampling, collection of material data and fermentation tests. For that specially equipment and skilled personnel are required. Moreover, the evaluation procedure is very time consuming. That is why a new state-of-the-art alternative for the evaluation purposes is necessary to simplify and to speed up the assessment of the biogas production processes. The aim of this doctoral thesis is the development of a fast and reliable method for the evaluation of the biogas production processes. Therefore the mathematical modelling should identify significant process variables able to evaluate the whole process. For the optimization of mathematical models metaheuristic tools were used. In this doctoral thesis two different data sets were used – experimental data and simulated data. The experimental data were collected in projects “Biogas-Biocoenosis” (FKZ 22010711, Dr. Michael Klocke, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam) and “Biogas-Enzyme” (FKZ 22027707, Dr. Monika Heiermann, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam). The simulated data set was generated using the Anaerobic Digestion Model No.1 (ADM1). The chemical process variables were used as the independent process variable set, while the biogas production output represented the dependent process variable. Prediction of the biogas production was done using linear and nonlinear mathematic models. Here, Partial-Least-Square-Regression (PLSR), Locally-Weighted-Regression (LWR) and Artificial Neural Networks (ANN) were implemented. In order to identify the most significant undependable process variables optimization algorithms were used, Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Prediction capacity was evaluated using two model evaluation variables, Root Mean Square Error (RMSE) and Coefficient of Determination (R2). Figure 1 in Supplementary represents the flow chart of the developed methodology applied for ADM1 generated data set. In Figure 2 (Supplementary) there is a flow chart of the developed methodology applied for the experimentally collected data. The developed approaches could be successfully used for the prediction of the desired process variable, biogas production rate. The variable selection done with the help of metaheuristic optimization algorithms improved the prediction results and reduced number of the independent process variables. Hydraulic retention time, dry matter, neutral detergent fibre, acid detergent fibre and n-butyric acid were identified as the most significant ones. The best prediction was obtained using ANN models. Here, the error of prediction was low and the coefficient of determination high. The successful implementation of the developed methodology proved mathematical models to be an effective alternative method capable to evaluate and to optimize complicated biological processes. Furthermore, it would be mandatory further experimental evaluation of the developed strategy, using the model-based process information.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hempel, Gotthilf;

    Johann Heinrich von Thünen-Institute, Federal Research Institute of Rural Areas, Forestry and Fisheries began publishing the Informationen aus der Fischereiforschung - Information on Fishery Research in 2010

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    OceanDocs
    Article . 1963
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      Article . 1963
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    Authors: R. Sierens; J. Demuynck; M. De Paepe; S. Verhelst;
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    Authors: Beier, H.;
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    Authors: Hampel, Uwe; Dittmeyer, Roland; Patyk, Andreas; Wetzel, Thomas; +6 Authors

    Energieeffizienz ist ein wesentlicher Baustein nachhaltigen Wirtschaftens und unabdingbare Voraussetzung zur Erreichung aktueller klimapolitischer Ziele. Die von der Helmholtz‐Gemeinschaft Deutscher Forschungszentren geförderte Energie‐Allianz „Energieeffiziente chemische Mehrphasenprozesse”︁ bündelt Kompetenzen und Forschungsaktivitäten im Bereich der chemischen Verfahrenstechnik mit dem Ziel der Weiterentwicklung von Entwurfs‐ und Auslegungsmethoden, numerischen und experimentellen Techniken sowie neuer Messeverfahren zur Effizienzsteigerung chemischer Mehrphasenprozesse. Energy efficiency is an essential building block of a sustainable economy and an indispensable prerequisite for achieving current international climate goals. The Energy Alliance "Energy efficient multiphase chemical processes" funded by the Helmholtz Association combines expertise and research activities in the field of chemical process engineering with the objective to further develop design tools, numerical and experimental techniques as well as new measurement techniques for increased efficiency of chemical multiphase processes.

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    Authors: Pitz-Paal, Robert;

    1. Warum Solarenergienutzung massiv ausbauen ? 2. Markt und Kostenentwicklung bei der PV 3. Markt und Kostenentwicklung bei der CSP mit Speicher 4. Szenarien für den Mix aus PV und CSP 5. Fazit > HGF PÜW > Prof. Robert DLR.de • Chart 2 Pitz-Paal , • > 08.11.2016

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    Authors: Bauernhansl, Thomas;

    Der Vortrag DIE PRODUKTION DER ZUKUNFT ZWISCHEN MASS PERSONALIZATION UND MASS SUSTAINABILITY behandelt die Veränderungen in der Produktion im Rahmen von Industrie 4.0. Zunächst wird Deutschlands Position im internationalen Vergleich ausführlich dargestellt und die Potenziale der Smart Factory aufgezeigt. Im Anschluss wird der innovative Stuttgarter Forschungscampus Arena2036 vorgestellt und zum Schluss werden die disruptiven Geschäftsmodelle präsentiert, die mit Industrie 4.0 implementiert werden. Bauernhansl erläutert aus internationaler Perspektive die regionalen Rahmenbedingungen der Produktion und erklärt, wie im Rahmen einer neuen Produktionsarchitektur alles in der Fabrik smart wird. Er gibt eine Einschätzung der Industrie 4.0 Readiness, wobei er zum Schluss kommt, dass Deutschland als Frontrunner die beste Position in Europa hat. Der Vortrag wirft einen Blick in die Zukunft der Smart Factory samt Anwendungsbeispielen. Er beschreibt die nächste Ebene der Dezentralisierung hin zur Fabrik als cyber-physischem Produktionssystem. Die Optimierung der Wertschöpfung im Rahmen von I4.0 sowie die Potenziale dazu werden beschrieben und schließlich wird ARENA2036 - der Stuttgart Research Campus Active Research Environment for the Next Generation of Automobiles mit seinen innovativen Produktionsansätzen vorgestellt. Schließlich fasst Bauernhansl die innovativen disruptiven Geschäftsmodelle im Rahmen von Industrie 4.0 und die Erfolgsfaktoren für die Industrie zusammen.

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    Authors: Schumann, Karen;
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    Library Ideas
    Article . 2008
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    Authors: Michaelis, Peter;

    Der Konflikt zwischen Ökologie und Ökonomie wird dadurch unnötig verschärft, daß wirtschaftswissenschaftliche Prinzipien und Erkenntnisse bei der Gestaltung einer ökonomisch rationalen Umweltpolitik nur unzureichend berücksichtigt werden. Die Ziele der Umweltpolitik werden von der Politik häufig nicht nach ihrer tatsächlichen Dringlichkeit, sondern nach dem Kriterium ihrer Öffentlichkeitswirkung festgelegt. Beispielhaft hierfür steht die Verpackungsverordnung mit ihren Recyclingquoten, die ihrer Höhe nach mit ökonomischer Rationalität teilweise nicht mehr zu vereinbaren sind. Im Gegensatz zum Ordnungsrecht mit seinen Ge- und Verboten lassen ökonomische Instrumente im Umweltschutz (Abgaben, Steuern, handelbare Nutzungsrechte) den Betroffenen Spielraum für individuelle ökonomische Anpassungsentscheidungen. Dementsprechend werden die für den Umweltschutz aufgewendeten Ressourcen dorthin gelenkt, wo sie den größten Entlastungseffekt erzielen. Ökonomische Instrumente ermöglichen mithin mehr Umweltschutz für weniger Geld. Darüber hinaus löst der permanente Kostendruck bei Einsatz ökonomischer Instrumente Innovationsanreize aus, die bei ordnungsrechtlichem Vorgehen durch den staatlicherseits vorgeschriebenen „Stand der Technik" blockiert sind. Die Anwendung ökonomischer Instrumente in der umweltpolitischen Praxis der Bundesrepublik beschränkt sich auf wenige Ausnahmen, die zudem einer marktwirtschaftlichen Umweltpolitik kaum entsprechen. Beispielsweise büßte die 1981 als richtungsweisend konzipierte Abwasserabgabe bereits im Vorfeld der Beratungen die ihr ursprünglich zugedachte Anreizwirkung weitgehend ein. Sie wurde zu einem Vollzugsinstrument des Ordnungsrechts degradiert. Die Einführung ökonomischer Instrumente stellt eine grundlegende institutionelle Innovation dar, die die bestehenden Besitzstände verändern würde. Der für ihre Einführung erforderliche Konsens zwischen den Gesellschaftsgruppen ist daher nur schwer zu erreichen. Den praktischen Einsatzmöglichkeiten ökonomischer Instrumente sind bei realistischer Betrachtung der technischen, ökonomischen, ökologischen und rechtlichen Rahmenbedingungen engere Grenzen gesetzt, als eine Betrachtung erwarten läßt, die lediglich auf die theoretische Vorteilhaftigkeit dieser Instrumente abstellt. Dennoch kann die stärkere Berücksichtigung ökonomischer Konzepte im Umweltschutz zu einem wesentlichen Fortschritt in Richtung auf eine ökonomisch rationale Umweltpolitik beitragen.

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      Research . 1996
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    Authors: Köllner, Patrick;

    Für das erste Quartal 2006 wurde ein Wachstum von 6,2 Prozent vermeldet; dies war das beste Ergebnis seit dem 4. Quartal 2002. Für das gesamte Jahr 2006 wird ein Wirtschaftswachstum von rund 5 Prozent prognostiziert (KDI: 5,3%, KERI: 4,9%). Damit dürfte die Entwicklung der weltweit elftgrößten Wirtschaft in diesem Jahr besser ausfallen, als in den drei vorangegangenen Jahren, in denen Wachstumsraten zwischen 3,1 und 4 Prozent erreicht wurden. Getragen wird das Wirtschaftswachstum nicht zuletzt von einer Wiederbelebung der inländischen Nachfrage, die zuvor noch die wirtschaftliche Entwicklung gebremst hatte. Angesichts der weiterhin dynamischen Entwicklung im benachbarten China dürften auch die Exporte weiter anziehen, wenn auch nicht mehr in dem Tempo wie in den Jahren zuvor. Grund hierfür ist das schlechter gewordene Wechselkursverhältnis zum US-Dollar, das koreanische Exporte teurer macht. Ende Juni 2006 entsprachen knapp 970 koreanische Won einem US-Dollar, 2005 waren es im Jahresdurchschnitt 1.024 Won pro US-Dollar gewesen. Mit einem Handelsvolumen von gut 545 Mrd. US-Dollar war Korea 2005 weltweit die Nummer 12. Koreas Leistungsbilanzüberschuss, der 2005 noch 16,1 Mrd. US-Dollar aufmachte, dürfte 2006 aber deutlich zurückgehen; prognostiziert wurde im April ein Überschuss von rund 3 Mrd. US-Dollar (AHK Informationsdienst Seoul, April 2006; KERI Economic Bulletin, April 2006). Korea: Politik, Wirtschaft, Gesellschaft, 2006: Korea: Politik, Wirtschaft, Gesellschaft

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    https://dx.doi.org/10.11588/kj...
    Article . 2018
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      https://dx.doi.org/10.11588/kj...
      Article . 2018
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    Authors: Beltramo, Tanja;

    Die Biogasherstellung ist ein sehr komplexer Prozess, der durch das Zusammenspiel unterschiedlicher Mikroorganismen und einem mehrstufigen Prozessablauf gekennzeichnet ist. Variationen in der Mikroflora können durch das verwendete Substrat, die Beschickung sowie die Temperatur gegeben sein, wobei ein Großteil der beteiligten Mikroorganismen sogar unbekannt sein kann. Die Überwachung und Analyse solcher Prozesse sind zumeist sehr zeit- und kostenintensiv. Die VDI-Richtlinie 4630 beschreibt analytische Methoden zur Untersuchung der Vergärung organischer Stoffe, Substratcharak¬terisierung, Probenahme, Stoffdatenerhebung und Gärversuche, die zur Optimierung von Biogasanalgen dienen. Die in der Richtlinie beschriebenen Methoden erfordern jedoch eine entsprechende Laborausstattung, dafür ausgebildetes Personal und sind sehr aufwändig. Deshalb ist eine moderne Alternative zur Prozessanalyse und -optimierung dringend erforderlich, um gezielt wesentliche Prozessgrößen schnell, effizient und kostengünstig zu identifizieren. Das Ziel der vorliegenden Dissertation ist es eine Methode zu entwickeln, welche eine schnelle und effiziente Analyse der Biogasproduktionsprozesse ermöglicht. In dieser Arbeit soll mithilfe von computerbasierten Verfahren untersucht werden, ob wesentliche Prozessgrößen identifiziert werden können, deren Messung für die Führung der Biogasanlagen ausschlaggebende Informationen zur Verfügung stellt. Neben mathematischen Modellen werden auch Optimierungsalgorithmen für die Identifikation der wesentlichen Einflussgrößen verwendet. Die für die Modellierung verwendeten Datensätze wurden experimentell im Rahmen des Projektes ‚,Biogas Biocoenosis‘‘ (FNR 22010711, Dr. Michael Klocke, Leibniz-Institut für Agrartechnik und Bioökonomie e. V., Potsdam) und „Biogas-Enzyme“ (FKZ 22027707, Dr. Monika Heiermann, Leibniz-Institut für Agrartechnik und Bioökonomie e. V., Potsdam) erhoben. Zusätzlich wurden Prozessdaten mithilfe des Anaerobic Digestion Modell No.1(ADM1) simuliert. Die chemischen Prozessgrößen stellen die unabhängigen Prozessvariablen dar. Die Biogasausbeute wird als die abhängige Zielprozessgröße verwendet. Die Vorhersage der Biogasausbeute wird mithilfe linearer und nicht linearer mathematischer Modelle durchgeführt. Dafür werden die Partial-Least-Squares-Regression (PLSR), die Locally-Weighted-Regression (LWR) und künstliche neuronale Netzte (ANN, Artificial Neural Networks) implementiert. Um die wesentlichen unabhängigen Prozessvariablen zu identifizieren, werden folgende Optimierungsalgorithmen verwendet: ein Ameisen-Kolonie-Optimierungsalgorithmus (ACO) und der genetische Algorithmus (GA). Die Qualität der Vorhersage wird anhand des Vorhersagefehlers (Root Mean Square Error, RMSE) und des Bestimmtheitsmaßes (R2) bewertet. Anhand der entwickelten Methode konnte die gewünschte Zielgröße, Biogasausbeute, vorhergesagt werden. Die durchgeführte Variablenselektion mittels metaheuristischer Optimierungsverfahren verbesserte die Vorhersagekraft der Modelle und reduzierte gleichzeitig die Anzahl der unabhängigen Prozessvariablen. Als wesentliche Prozessvariablen wurden folgende identifiziert: hydraulische Verweilzeit, Trockensubstanz, Gehalt an Neutral-Detergentien-Faser, Säure-Detergentien-Lignin-Gehalt und n-Buttersäure. Die besten Vorhersageergebnisse wurden mit Hilfe der künstlichen neuronalen Netze erzielt. Die Modelle hatten den kleinsten Fehler der Vorhersage und das höchste Bestimmtheitsmaß. Die erfolgreiche Umsetzung der mathematischen Verfahren zeigt, dass moderne mathematische Methoden eine gute Alternative für die Analyse und Optimierung komplexer biologischer Prozesse darstellen. Für eine abschließende Bewertung sind jedoch auch weitere experimentelle Untersuchungen notwendig, in denen die Prozessführung basierend auf den identifizierten Prozessvariablen durchgeführt wird. The production of biogas is very complex process, which runs in some stages involving different microorganisms. Microbiological diversity of the process depends mainly on the composition of substrate and ambient conditions, such as process temperature. The fact is, the development and composition of the microbiological communities of the process are difficult to predict. Thus, the control and evaluation of such complex biological processes are very time consuming and expensive. In Germany the evaluation of the biogas plants can be performed according to the VDI-Norm 4630, which describes the methods for the evaluation of fermentation of organic materials including characterization of the substrate, sampling, collection of material data and fermentation tests. For that specially equipment and skilled personnel are required. Moreover, the evaluation procedure is very time consuming. That is why a new state-of-the-art alternative for the evaluation purposes is necessary to simplify and to speed up the assessment of the biogas production processes. The aim of this doctoral thesis is the development of a fast and reliable method for the evaluation of the biogas production processes. Therefore the mathematical modelling should identify significant process variables able to evaluate the whole process. For the optimization of mathematical models metaheuristic tools were used. In this doctoral thesis two different data sets were used – experimental data and simulated data. The experimental data were collected in projects “Biogas-Biocoenosis” (FKZ 22010711, Dr. Michael Klocke, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam) and “Biogas-Enzyme” (FKZ 22027707, Dr. Monika Heiermann, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam). The simulated data set was generated using the Anaerobic Digestion Model No.1 (ADM1). The chemical process variables were used as the independent process variable set, while the biogas production output represented the dependent process variable. Prediction of the biogas production was done using linear and nonlinear mathematic models. Here, Partial-Least-Square-Regression (PLSR), Locally-Weighted-Regression (LWR) and Artificial Neural Networks (ANN) were implemented. In order to identify the most significant undependable process variables optimization algorithms were used, Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Prediction capacity was evaluated using two model evaluation variables, Root Mean Square Error (RMSE) and Coefficient of Determination (R2). Figure 1 in Supplementary represents the flow chart of the developed methodology applied for ADM1 generated data set. In Figure 2 (Supplementary) there is a flow chart of the developed methodology applied for the experimentally collected data. The developed approaches could be successfully used for the prediction of the desired process variable, biogas production rate. The variable selection done with the help of metaheuristic optimization algorithms improved the prediction results and reduced number of the independent process variables. Hydraulic retention time, dry matter, neutral detergent fibre, acid detergent fibre and n-butyric acid were identified as the most significant ones. The best prediction was obtained using ANN models. Here, the error of prediction was low and the coefficient of determination high. The successful implementation of the developed methodology proved mathematical models to be an effective alternative method capable to evaluate and to optimize complicated biological processes. Furthermore, it would be mandatory further experimental evaluation of the developed strategy, using the model-based process information.

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