IMPROOF: Integrated Model Guided Process Optimization of Steam Cracking Furnaces

Djokic, Marko; Heynderickx, Geraldine; Dekeukeleire, S.; Vangaever, Stijn; Battin‐Leclerc, Frédérique; Bellos, Georgios; Buysschaert, Wim; Cuenot, Bénédicte; Faravelli, Tiziano; Henneke, Michael; Jakobi, Dietlinde; Lenain, P.; Muñoz, Andrés Emanuel Chicaíza; Olver, John W.; Goethem, M. Van; Oud, Peter

doi:10.1007/978-3-319-57078-5_56

Cited by 2 publications

(1 citation statement)

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“…Regardless of their specific classification, the simultaneous minimisation or maximisation of different KPIs is not generally possible and, thus, optimisation studies are necessary to design and operate manufacturing systems [4,12]. To achieve this goal, it is of critical importance to quantify precisely attributes also in the cases when indicators are more easily defined qualitatively rather than quantitatively.…”

Section: Decision Making Metrics In Manufacturing Systemsmentioning

confidence: 99%

Metal Casting Energy Efficient Metrics for Material Selection of Automotive Parts

Pagone

Papanikolaou

Salonitis

et al. 2018

Sustainable Design and Manufacturing 2018

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The automotive sector is one of the main end-use markets for metal casting worldwide. The strong competitive pressure typical of this industry have been influenced in the recent years by sustainability as a new factor promoted by legislation, increased societal awareness of relevant instances and resource scarcity. Energy efficiency, although only a part of sustainability, is important for the metal casting practice because of its nature of large consumer of energy per unit product. Therefore, the effective use of appropriate energy efficient metrics in foundries is of great interest. In this work, a set of indicators developed by the authors (and derived by traditional metrics) to analyse the energy performance of foundries will be used to compare high pressure die casting processes producing car transfer cases with different suitable materials. On the basis of this analysis, it will be shown that the most energy efficient material can be identified whereas the traditional metrics cannot detect such opportunity.

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Section: Decision Making Metrics In Manufacturing Systemsmentioning

confidence: 99%