Compositional analysis of bio-oils from hydrothermal liquefaction of tobacco residues using two-dimensional gas chromatography and time-of-flight mass spectrometry.

Sustainable energy from biomass is one of the most promising alternative energy sources and is expected to partially replace fossil fuels. Tobacco industries have normally rid their processing residues by landfilling or incineration, affecting the environment negatively. These residues can be used to either extract high-value chemicals or generate bio-energy via hydrothermal liquefaction.

Biocrude oil production via hydrothermal liquefaction of food waste in a simplified high-throughput reactor.

In this study, the hydrothermal liquefaction of food waste was investigated for generating bio-oils or biocrudes using a simplified high-throughput reactor. Different operating conditions were parametrically tested for obtaining maximum yield of biocrudes at temperatures from 280 to 340 °C and sample-to-water mixture ratios from 1:3 to 1:7, with 30 min residence time. The biocrude yields were found to increase significantly with increasing temperature and mixture ratio.

Production and characterization of bio-oils from fast pyrolysis of tobacco processing wastes in an ablative reactor under vacuum.

Application of advanced pyrolysis processes to agricultural waste for liquid production is gaining great attention, especially when it is applied to an economic crop like tobacco. In this work, tobacco residues were pyrolyzed in an ablative reactor under vacuum. The maximum bio-oil yield of 55% w/w was obtained at 600°C with a particle size of 10 mm at a blade rotation speed of 10 rpm.

Conversion of tobacco processing waste to biocrude oil via hydrothermal liquefaction in a multiple batch reactor.

Fossil fuels are the primary energy source of almost all societies and economies, but it is finite and scarce. The use of non-renewable fossil fuels threatens earth's environment. At the same time, waste from agricultural and industrial activities is increasing.

Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents.

Ionic liquid solvents (ILSs) have been effectively utilized in biomass pretreatment to produce cellulose-rich materials (CRMs). Predicting CRM properties and evaluating multi-dimensional relationships in this system are necessary but complicated. In this work, machine learning algorithms were applied to predict CRM properties in terms of cellulose enrichment factor (CEF) and solid recovery (SR), using 23-feature datasets from biomass characteristics, operating conditions, ILSs identities, and catalyst.