Acid gas removal from gaseous streams such as flue gas, natural gas and biogas is mainly performed by chemical absorption with amines, but the process is highly energy intensive and can generate emissions of harmful compounds to the atmosphere. Considering the emerging interest in carbon capture, mainly associated with increasing environmental concerns, there is much current effort to develop innovative solvents able to lower the energy and environmental impact of the acid gas removal processes. To be competitive, the new blends must show a CO₂ uptake capacity comparable to the one of the traditional MEA benchmark solution. In this work, a review of the state of the art of attractive solvents alternative to the traditional MEA amine blend for acid gas removal is presented. These novel solvents are classified into three main classes: biphasic blends—involving the formation of two liquid phases, water-lean solvents and green solvents. For each solvent, the peculiar features, the level of technological development and the main expected pros and cons are discussed. At the end, a summary on the most promising perspectives and on the major limitations is provided.

Pyrolysis, a key waste-to-X technology, enables converting a wide portfolio of biomass waste into valuable chemicals and fuels. However, raw pyrolysis oils are chemically and physically unstable. A multi-step stabilisation is necessary to reduce acidity and the content of reactive components, mainly carbonyls and carboxylic acids. During stabilisation, which involves deoxygenation and hydrogenation as the main steps, an aqueous phase is generated as a by-product. This stream contains mainly water, but relevant amounts of methanol and ethanol (2-8 wt%) are also present, together with minor concentrations of higher alcohols, C1-C4 carboxylic acids, and light esters. The aim of this work is to design and optimise a process to isolate the methanol and ethanol embodied in the aqueous phase and exploit them as intermediates to generate biofuels, biochemicals, and pharmaceutical products. The process consists of a train of four distillation columns to maximise the recovery rate and purity of the two targeted products. COCO-COFE, a CAPE-OPEN process simulator, is used for flowsheet development. The optimal number of theoretical stages and the optimal positioning of the feed to each unit have been optimised, considering a trade-off CAPEX-OPEX as the criterion. Results show that up to 95% and 98% yield can be achieved for methanol and ethanol, respectively. Methanol purity is limited to a maximum of 92 wt% due to the presence of volatile impurities, while ethanol contains residual propanol (6 wt%). The overall heat demand is in the order of 1 MJ/kg feed.

Pyrolysis is an emerging green pathway to produce bulk chemicals and sustainable fuels. However, pyrolysis oil requires stabilisation via hydrotreatment, and this process generates an aqueous waste containing alcohols (mainly methanol and ethanol), carboxylic acids, and some ketones. To increase the economic sustainability of biofuels production, there is increasing interest in recovering these valuable chemicals from water. Reliable thermodynamics are necessary to address the separation and design of equipment to fractionate such complex mixtures, with multiple azeotropes and non-idealities. The Non-Random Two-Liquids (NRTL) models in both Aspen Plus V12.0 and COFE V3.7, a license-free software released by AmsterChem, do not accurately reproduce the equilibrium measurements of most of the binary and ternary mixtures involving water, a C1-C4 alcohol, and a light carboxylic acid. This work aims to retune the activity-based model to improve the NRTL model predictivity, using experimental data retrieved from NIST ThermoData Engine V10.1. The regression is performed in Aspen Plus V12 using the Maximum Likelihood algorithm. The resulting re-parametrised NRTL shows improved accuracy and reliability compared to the existing models. For all investigated binary and ternary mixtures, the Average Relative Deviation (ARD) is lower than 20%, and for 75% of them, ARD is lower than 10%. The ARD in the prediction of vapour phase composition drops from 16.0% and 12.1% of the original COFE and Aspen Plus models, respectively, down to 7.0%.