Key Performance Indicators for Implementing Sustainability and Environmental Protection in Early Process Design Activities

The adoption of a sustainability perspective in chemical industry shall start from the early phases of process design (e.g. conceptual design, technology selection, process development) where the key drivers in the environmental, economical, and hazard fingerprint of a process are defined. These phases also allow the opportunities for the lower cost of design change. A sound support of design activities requires quantitative tools, allowing for the assessment of the sustainability profile of a process, the identification of possible improvements and supporting informed tradeoffs. Though several tools for process development were proposed in last decades, application is still limited in the current practice because of issues on data requirement, indicator definition and customization to specific application needs (e.g. PFD definition in design of polypropylene production plants). This study focuses on the application to the early process design of environmental and exergy Key Performance Indicators (KPIs) to support sustainability-oriented design activities. It was tailored on the specific industrial application of polypropylene production plants. The choice of a specific sector allowed customization of the method, promoting ease of application and allowing the assessment of multiple scenarios (e.g. sensitivity on material and energy supply strategies, comparison of different technologies). Results obtained draw up sustainable guidelines to improve design activities within the scope in a lifecycle perspective

Some like it Hoax: Automated fake news detection in social networks

In the recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic online hoax detection systems. As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who \ue2\u80\u9cliked\ue2\u80\u9d them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems

Innovation in conceptual design of chemical processes

Nowadays, the chemical industry has reached significant goals to produce essential components for human being. The growing competitiveness of the market caused an important acceleration in R&D activities, introducing new opportunities and procedures for the definition of process improvement and optimization. In this dynamicity, sustainability is becoming one of the key aspects for the technological progress encompassing economic, environmental protection and safety aspects. With respect to the conceptual definition of sustainability, literature reports an extensive discussion of the strategies, as well as sets of specific principles and guidelines. However, literature procedures are not completely suitable and applicable to process design activities. Therefore, the development and introduction of sustainability-oriented methodologies is a necessary step to enhance process and plant design. The definition of key drivers as support system is a focal point for early process design decisions or implementation of process modifications. In this context, three different methodologies are developed to support design activities providing criteria and guidelines in a sustainable perspective. In this framework, a set of key Performance Indicators is selected and adopted to characterize the environmental, safety, economic and energetic aspects of a reference process. The methodologies are based on heat and material balances and the level of detailed for input data are compatible with available information of the specific application. Multiple case-studies are defined to prove the effectiveness of the methodologies. The principal application is the polyolefin productive lifecycle chain with particular focus on polymerization technologies. In this context, different design phases are investigated spanning from early process feasibility study to operative and improvements assessment. This flexibility allows to apply the methodologies at any level of design, providing supporting guidelines for design activities, compare alternative solutions, monitor operating process and identify potential for improvements

Experiments with Wikipedia Cross-Language Data Fusion

Abstract. There are currently Wikipedia editions in 264 different languages. Each of these editions contains infoboxes that provide structured data about the topic of the article in which an infobox is contained. The content of infoboxes about the same topic in different Wikipedia editions varies in completeness, coverage and quality. This paper examines the hypothesis that by extracting infobox data from multiple Wikipedia editions and by fusing the extracted data among editions it should be possible to complement data from one edition with previously missing values from other editions and to increase the overall quality of the extracted dataset by choosing property values that are most likely correct in case of inconsistencies among editions. We will present a software framework for fusing RDF datasets based on different conflict resolution strategies. We will apply the framework to fuse infobox data that has been extracted from the English, German, Italian and French editions of Wikipedia and will discuss the accuracy of the conflict resolution strategies that were used in this experiment

Do you have a Pop face? Here is a Pop song. Using profile pictures to mitigate the cold-start problem in Music Recommender Systems

When a new user registers to a recommender system service, the system does not know her taste and cannot propose meaningful suggestions (cold-start problem). This preliminary work attempts to mitigate the cold-start problem using the profile picture of the user as a sole information, following the intuition that a correspondence may exist between the pictures that people use to represent themselves and their taste. We proved that, at least in the small music community we used for our experiments, our method can improve the precision of both a classifier and a Top-N music recommender system in a cold-start condition

Early Detection of Social Media Hoaxes at Scale

The unmoderated nature of social media enables the diffusion of hoaxes, which in turn jeopardises the credibility of information gathered from social media platforms. Existing research on automated detection of hoaxes has the limitation of using relatively small datasets, owing to the difficulty of getting labelled data. This in turn has limited research exploring early detection of hoaxes as well as exploring other factors such as the effect of the size of the training data or the use of sliding windows. To mitigate this problem, we introduce a semi-automated method that leverages the Wikidata knowledge base to build large-scale datasets for veracity classification, focusing on celebrity death reports. This enables us to create a dataset with 4,007 reports including over 13 million tweets, 15% of which are fake. Experiments using class-specific representations of word embeddings show that we can achieve F1 scores nearing 72% within 10 minutes of the first tweet being posted when we expand the size of the training data following our semi-automated means. Our dataset represents a realistic scenario with a real distribution of true, commemorative and false stories, which we release for further use as a benchmark in future research.Comment: ACM Transactions on the Web (TWEB