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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

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Nowadays, chemical engineering has to face a new industrial context with, for example, the gradually falling of hydrocarbon reserves after 2020-2030, relocation, emerging of new domains of application (nano-micro technologies) which necessitate new solutions and knowledges . . . All these tendencies and demands accelerate the need of tool for design and innovation (technically, technologically). In this context, this paper presents a tool to accelerate innovative preliminary design. This model is based on the synergy between: TRIZ (Russian acronym for Theory of Inventive Problem Solving) and Case Based Reasoning (CBR). The proposed model offers a structure to solve problem, and also to store and make available past experiences in problems solving. A tool dedicated to chemical engineering problems, is created on this model and a simple example is treated to explain the possibilities of this tool.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

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