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Biogas has emerged as a valid feedstock for biomethanol production from steam reforming. This study investigates an alternative layout based on an auto-thermal electrified reforming assuming a 1 MW equivalent anaerobic digestion plant as a source for methanol synthesis. The process considers an oxy-steam combustion of biogas and direct carbon sequestration with the presence of a reverse water–gas shift reactor to convert CO2 and H2 produced by a solid oxide electrolyzer cell to syngas. Thermal auto-sufficiency is ensured for the reverse water–gas shift reaction through the biogas oxy-combustion, and steam production is met with the integration of heat network recovery, with an overall process total electrical demand. This work compares the proposed process of electrification with standard biogas reforming and data available from the literature. To compare the results, some key performance indicators have been introduced, showing a carbon impact of only 0.04 kgCO2/kgMeOH for the electrified process compared to 1.38 kgCO2/kgMeOH in the case of biogas reforming technology. The auto-thermal electrified design allows for the recovery of 66.32% of the carbon available in the biogas, while a similar electrified process for syngas production reported in literature reaches only 15.34%. The overall energy impact of the simulated scenarios shows 94% of the total energy demand for the auto-thermal scenario associated with the electrolyzer. Finally, the introduction of the new layout is taken into consideration based on the country's carbon intensity, proving carbon neutrality for values lower than 75 gCO2/kWh and demonstrating the role of renewable energies in the industrial application of the process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Anaerobic Digestion represents an economically and environmentally friendly technology that allows the production of biogas starting from substrates made of waste (e.g., animal manure, agro-industrial and organic waste types, sludges) while also disposing and valorising them. Single substrate digestion frequently unexploits the true bacterial capacity, resulting in low methane production. On the other hand, it has been demonstrated that it might be significantly increased by combining two or more substrates, performing an anaerobic codigestion. In the last years, many studies have been carried out to understand how different feedstocks interact with each other when put together. However, an easy-to-use and quick technology for the calculation of their optimal blending ratios doesn't exist in the literature, being able to estimate the optimal feedstock composition of co-digestion configurations. Consequently, this work aims to develop a tool that, by understanding how substrates should be combined, allows to obtain the highest possible methane yield. The high number of possible raw materials and the high variability of their composition depending on their source reflects the high complexity of the problem, leading to the creation of a wide database where data about commonly used substrates have been collected from literature. These data have been then analysed and exploited to build a data-driven optimization algorithm - elaborated using PythonTM programming language - that, through the maximization of an objective function, it can evaluate the optimal blending ratios of the substrates entering industrial batch and CSTR-based digesters. Furthermore, the model considers supply-chain issues such as substrate availability and storage options to be more trustworthy in a wide range of industrial settings. Finally, the model was validated by comparing it to experimental batch tests published in the literature as well as industrial data provided by two Italian companies, yielding satisfactory and practical findings. [ABSTRACT FROM AUTHOR]