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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.

Nowadays most of energy supply and chemicals are produced from fossil fuels. However, environmental concerns like global warming, depletion of non-renewable resources and pollution, force to search for more sustainable alternative feedstocks. In this framework, the valorization of renewable resources is currently one of the most promising strategies for the next future. In particular, the use of biomass is ranked first for production of chemicals. One of the most attractive compounds that can be obtained from lignocellulosic biomass is Levulinic acid (LA). The present work analyses, from a technical, economic and safety standpoint, the valorization of waste biomass (OPEFB, oil palm empty fruit bunch) to LA with different methodologies. Energy demand of the plant and the separation efficiency are evaluated carrying out process simulations by the commercial software Aspen HYSYS V10. The obtained results allowed an economic analysis of the alternatives, based on the evaluation of investment and main operating costs. The alternatives were screened on the basis of net present value (NPV) of costs. Safety performance was investigated by an inherent safety assessment method. This considered toxic dispersion and fire scenarios, calculating the damage distances associated with the potential outcomes of an accident and allowing for the evaluation of a potential hazard index for each main process equipment. The choice of the optimal layout configuration for the production of LA in early phases of design allowed to address, in a time and cost-effective way, further design activities in reducing the energy demand and ensuring potentially safer plants making the whole process more sustainable.

The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are traditionally either based on content analysis (i.e. analyzing the content of the news) or - more recently - on social context models, such as mapping the news' diffusion pattern. In this paper, we first propose a novel ML fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 4.8%. Second, we implement our method within a Facebook Messenger chatbot and validate it with a real-world application, obtaining a fake news detection accuracy of 81.7%.