International audienceChemical industries have the potential to become a driving force to introduce efficient production practices for reducing the negative impact on the environment. In order to meet these environmental challenges, innovation is a key factor in turning the concept of green growth into a reality through the development of eco-friendly technologies and sustainable production. Therefore, to accelerate and improve the design of eco-inventive solutions, new approaches must be created and adapted to integrate the constraints of eco invention in the preliminary design. The purpose of this paper is to present the first elements of a computer aided eco-innovation system to support the engineers in preliminary design. This research paper proposes a method based on a synergy between the Theory of Inventive Problem Solving (TRIZ) and the Case Based Reasoning. However, the typical level of abstraction of the TRIZ tools is modified. Indeed, TRIZ only gives way or guidelines to explore in order to find an inventive solution, which are often too abstract and hard to traduce into an inventive concept. To reduce this level of abstraction, this work proposes to apply the physical, chemical, biological, geometrical effects or phenomenon as solutions as they are more concrete. This is done thanks to a resources oriented search in order to better exploit the resources encompassed in the system. A case study on a new production process in chemical engineering illustrates the effectiveness of the proposed approach
International audienceClassical approaches for remote visualization and collaboration used in Computer-Aided Design and Engineering (CAD/E) applications are no longer appropriate due to the increasing amount of data generated, especially using standard networks. We introduce a lightweight and computing platform for scientific simulation, collaboration in engineering, 3D visualization and big data management. This ICT based platform provides scientists an "easy-to-integrate" generic tool, thus enabling worldwide collaboration and remote processing for any kind of data. The service-oriented architecture is based on the cloud computing paradigm and relies on standard internet technologies to be efficient on a large panel of networks and clients. In this paper, we discuss the need of innovations in (i) pre and post processing visualization services, (ii) 3D large scientific data set scalable compression and transmission methods, (iii) collaborative virtual environments, and (iv) collaboration in multi-domains of CAD/E. We propose our open platform for collaborative simulation and scientific big data analysis. This platform is now available as an open project with all core components licensed under LGPL V2.1. We provide two examples of usage of the platform in CAD/E for sustainability engineering from one academic application and one industrial case study. Firstly, we consider chemical process engineering showing the development of a domain specific service. With the rise of global warming issues and with growing importance granted to sustainable development, chemical process engineering has turned to think more and more environmentally. Indeed, the chemical engineer has now taken into account not only the engineering and economic criteria of the process, but also its environmental and social performances. Secondly, an example of natural hazards management illustrates the efficiency of our approach for remote collaboration that involves big data exchange and analysis between distant locations. Finally we underline the platform benefits and we open our platform through next activities in innovation techniques and inventive design
Please cite this article as: Ségolène Belletante, Ludovic Montastruc, Stéphane Negny, Serge Domenech, Optimal design of an efficient, profitable and sustainable biorefinery producing acetone, butanol and ethanol: Influence of the insitu separation on the purification structure, Biochemical Engineering Journal http://dx.
Effective retrieval and new indexing method for case based reasoning: Application in chemical process design. (2010) Engineering Applications of Artificial Intelligence, vol. 23 (n° 6). pp. 880-894.
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