Search results for: M. Graus

Authors: Petri Vaattovaara, Zoran D. Ristovski, Martin Graus, Marcus Müller, EijaAsmi, Luca Di Liberto, StaffanSjögren, Douglas Orsini, Caroline Leck, Ari Laaksonen

Abstract:

Due to climate warming and consequently due to ice and snow melting of the Arctic Ocean, the highly biologically active ocean surface area has been expanding quickly making possible longer marine biota growth seasons during polar summers. That increase the probability of the remote marine environment secondary contribution, especially secondary organic contribution, to the particle production and particle growth events and particle properties, consequently effecting on the open ocean, pack ice and ground based regions radiation budget and thus on the feedbacks between arctic biota, particles, clouds, and climate.

Keywords: Arctic Ocean, ice melting, nucleation, secondary organics, clouds, climate.

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Authors: M. Graus, K. Westhoff, X. Xu

Abstract:

The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: Data analytics, green production, industrial energy management, optimization, renewable energies, simulation.

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