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The use of fossil resources and their negative environmental impacts has awakened the awareness of the petrochemical industry. Hereby, we are presenting some upstream industrial scalable and commercial solutions to process sustainable feedstocks, either biogenic or recycled, to produce drop-in hydrocarbons that can be converted into light olefins using the same assets and infrastructure currently established in the petrochemical industry (e.g. steam crackers), reducing the environmental impact of large-volume chemicals such as ethylene, propylene and benzene, which are the most demanded building blocks in the petrochemical value chain. Mass balanced certified co-processing of biogenic and recycled waste plastics as raw materials, are the key for the de-fossilisation of the petrochemical industry. Production of polypropylene (PP) using renewable feedstock can reduce the GHG above 80% or 3.8 kg CO2eq/kg in comparison with the fossil-based. For making a higher impact in the plastic industry, a full integration of the value chain is needed to guarantee allocation of the sustainable credits to targeted products. As a showcase, a collaboration project between partners in different parts of the value chain to produce biobased PP thermoformed plastic cups, is presented. As a result from this collaboration, PP cups with final properties identical in range to the traditional fossil were obtained and the renewable hydrocarbons could be identified in the product using C14. Drop-in solutions using renewable or recycled feedstock is paving the way in the petrochemical industry to obtaining sustainable products with low impact in the current downstream infrastructure.
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Nowadays continuous GPS time series are considered a crucial product of GPS permanent networks, useful in many geo-science fields, such as active tectonics, seismology, crustal deformation and volcano monitoring (Altamimi et al. 2002, Elósegui et al. 2006, Aloisi et al. 2009). Although the GPS data elaboration software has increased in reliability, the time series are still affected by different kind of noise, from the intrinsic noise (e.g. thropospheric delay) to the un-modeled noise (e.g. cycle slips, satellite faults, parameters changing). Typically GPS Time Series present characteristic noise that is a linear combination of white noise and correlated colored noise, and this characteristic is fractal in the sense that is evident for every considered time scale or sampling rate. The un-modeled noise sources result in spikes, outliers and steps. These kind of errors can appreciably influence the estimation of velocities of the monitored sites. The outlier detection in generic time series is a widely treated problem in literature (Wei, 2005), while is not fully developed for the specific kind of GPS series. We propose a robust automatic procedure for cleaning the GPS time series from the outliers and, especially for long daily series, steps due to strong seismic or volcanic events or merely instrumentation changing such as antenna and receiver upgrades. The procedure is basically divided in two steps: a first step for the colored noise reduction and a second step for outlier detection through adaptive series segmentation. Both algorithms present novel ideas and are nearly unsupervised. In particular, we propose an algorithm to estimate an autoregressive model for colored noise in GPS time series in order to subtract the effect of non Gaussian noise on the series. This step is useful for the subsequent step (i.e. adaptive segmentation) which requires the hypothesis of Gaussian noise. The proposed algorithms are tested in a benchmark case study and the results 2ff7e9595c
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