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Biomass gasification is a thermochemical process that produces a gaseous end product known as syngas, the main components of which are CO, CO2, and H2. Starting from syngas it is possible to upgrade this mixture into Synthetic Natural Gas (SNG) through a catalytic methanation process. In this work, a thermodynamic study was conducted to see how operative parameters such as the temperature of the methanation reactor, the quality of the syngas (related to different gasification technologies), and the presence of a conditioning stage based on water gas shift reaction, can influence the output variables as the conversion degree of the reactants, the mass yield and the low heating value of the final products.

Fermentation biomasses can be defined as a complex mixture of different natural components and microbes, having biodegradable and organic waste as the primary source. Its correct characterization is crucial to have proper processing in fermentative units. Firstly, proximate analysis is done to retrieve the content of specific compounds in the mixture, such as fat, proteins, and carbohydrates. However, this is often not enough to achieve the sufficient precision, since some low-concentration species are not easily found through this methodology (i.e., sulfate compounds, ethanol, caproic acid). Consequently, ultimate analysis is performed to evaluate the exact amount of every element in the mixture. For biomass-based compounds, atoms content can be synthesized in carbon, hydrogen, oxygen, nitrogen, and sulfur. The total content of these elements is also known as CHONS. From this, it is possible to derive the exact amount of the relative species in the biomass. However, the experimental procedure for its determination is rather time and budget-consuming. On the other hand, the amount of data collected in the literature, from both experimental and industrial analysis, can be exploited to build a numerical model, based on the multivariate statistical analysis and machine learning principles that predict the CHONS content for every type of biomass. In this work, a data-driven model has been developed to achieve this aim, having as input a set of relevant variables. Consequently, a dataset has been built to gather all these data. The multivariate statistical technique of Canonical Correlation Analysis (CCA) is used to find 'hidden' correlations and predict CHON content for 27 different biomass types. In future research, machine learning techniques will be applied to compare the results obtained.

The identification of the most efficient biomass valorization paths is vital for reaching the target of Renewable Energy Sources consumption by 2030. In this context, within a National project named ‘Biofeedstock’, the applicability of multivariate statistical analysis, i.e. Canonical Correlation Analysis (CCA), is implemented for the definition of specific correlations describing quantitatively and qualitatively the fast pyrolysis process outputs. The database used for the CCA contains 59 observations and it has been built up using literature data specifically on fluidized bed fast pyrolysis without any catalyst, in the temperature range of 450-550°C. The results show that the CCA correctly describes the process analysed with a discrete degree of confidence. However, it shows two main drawbacks, firstly the dataset constitution, and secondly possibility to individuate only linear correlations between inputs and outputs.

In this work, bimetallic catalysts (Pt-Ni/CeO2-Al2O3) in the form of powder and structured samples were employed for the oxidative steam reforming of fuel grade bioethanol. The stability performance of the above samples was investigated in a stainless steel tubular reactor at 500°C and 1 atm by feeding a commercial fuel grade ethanol stream with a H2O/C2H5OH = 4 and O2/C2H5OH = 0.5. Preliminarily, the ceria loading (between 25 and 45 wt%) as well as the Pt content (between 2 and 5 wt%) were optimized with respect to the washcoat (wc) content for the powder sample. Stability tests were carried out for 24 hours at 500°C and WHSV (Weight Hourly Space Velocity) = 12 h-1. The highest endurance performance was recorded over the 3Pt-10Ni/35CeO2/wc, which assured an ethanol conversion of almost 98% at the end of the test with a corresponding hydrogen yield of 50%. When the most promising formulation was transferred on a Ni-Fe substrate (made of an open cell foam), a clear improvement in the catalyst performance was recorded. In particular, the structured catalyst, able to assure a very good heat management within the catalytic bed as well as an improved mass transport, displayed a more stable behaviour compared to the corresponding powder, even in the presence of the typical bioethanol impurities; moreover, no significant formation of unwanted by-products (coke precursors) was observed during 24 hours of time-on- stream (TOS).

Crude bio-oil obtained from fast pyrolysis of biomass and wastes is typically characterised by the presence of high levels of oxygenated compounds, which are mainly responsible for its unfavourable characteristics (e.g., low heating value, high acidity, and poor storage stability). In order to overcome this drawback and favourably produce drop-in fuels, the fast pyrolysis of olive stone (OS), has been studied by giving particular attention to the exploration of operating conditions (i.e. pyrolysis temperature) and strategies (i.e. catalytic pyrolysis and co-pyrolysis) suitable to promote efficient de-oxygenation of bio-oils and improve the quality of the product streams. Steady state fast pyrolysis tests were performed in a bench scale fluidized bed reactor (gas residence time ~1s). Pyrolysis tests were carried out at 500 °C and 600 °C by using either inert sand or ?-alumina catalyst as bed material. Outcomes from the non-catalytic and the catalytic co-pyrolysis of low-density polyethylene (LDPE) and OS (plastic-to-biomass ratio of 20/80) at two different temperatures (500 and 600 °C) are also presented. Preliminary findings highlight that the co-processing of LDPE and OS under non-catalytic conditions stands out for the formation of long-chain aliphatic hydrocarbons in the form of both liquid paraffins and wax deposits, which are well-known to be the primary products evolved from the pyrolysis of polyolefins. The addition of ?-alumina catalyst significantly affects both the distribution and the quality of the pyrolytic products (char, bio-oils, and gas). Under catalytic co-pyrolysis conditions, a marked reduction in the yield of bio-liquid is observed, compensated by a remarkable improvement in its quality, particularly in terms of the formation of light mono-aromatics and a marked decrease in the total amount of the oxygenated compounds. On the downside, however, a significant increase in the production of polycyclic aromatic hydrocarbons (PAHs) is detected. Remarkable benefits are also detected by increasing the co-pyrolysis temperature to 600 °C, particularly in terms of content of oxygenated compounds in the bio-oils, as well as in terms of PAHs and water formation, which decreased considerably. Altogether, preliminary findings of this study suggest that further research efforts are required in order to improve the process performance, for example by optimizing the operating conditions as well as the physicochemical properties of catalysts.