Influence of catalyst and solvent on the hydrothermal liquefaction of woody biomass

Distribution characteristics and migration pathways of metals during hydrothermal liquefaction of municipal sewage sludge in the presence of various catalysts

A series of hydrothermal liquefaction (HTL) experiments with two different samples of municipal sewage sludge (MSS) were conducted at 350 °C for 30 min residence time in a high pressure batch reactor. The main aim of the study was to explore the distribution and migration pathways of a broad range of metals and metalloids in the HTL products (bio-oil, char and aqueous phase) obtained in the presence of various homogeneous and heterogeneous catalysts (Na2CO3, Li2CO3, K2CO3, Ba(OH)2, Fe2O3, CeO2, NiMo/MoO3, MoS2, Ni/NiO, SnO2, FeS). The elements under study included 16 environmentally significant metals and metalloids (As, B, Ba, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Se, Sn, Zn and Hg). The study showed that the quantitative migration of the tested metals and metalloids to the particular HTL products, relative to their initial content in the raw sludge, is different for the individual elements. Most metals exhibited a particularly strong affinity to the solid fraction (biochar). In the obtained HTL bio-oils, all tested elements were identified, except of Cd. It was also found that B and As have high affinity to the aqueous phase. A direct effect of catalysts on the contents of some elements in the products was also proved by the study, e.g. increased concentration of Cr in the biochar when Fe2O3 was used as a process catalyst. Due to the wide scope of the tested elements and broad range of catalyst used, the results obtained represent a unique and comprehensive set of environmental data compared to similar HTL studies previously conducted for MSS.

Liquefaction of Oak Wood Using Various Solvents for Bio-oil Production

Rapidly increasing global energy demand resulting from the growing population and worldwide development has increased the consumption of limited fossil fuel. The usage causes severe environmental deterioration by CO2 emission, which has sparked interest in finding green, renewable, and sustainable alternative sources of energy. Bio-oil, derived from several biomasses via liquefaction, is a promising candidate to replace fossil fuels. Turkey's land (27%) is covered with forested areas (consisting of mostly oak trees). Therefore, it has great potential for cheap lignocellulosic feedstock forest residues from industrial applications and harvesting. In the present study, the thermal liquefaction of oak wood particles (OWP) was performed using various solvents in addition to water, namely, ethanol, 1-butanol, and 1,4-dioxane. The experiments were carried out in a batch reactor for 1 and 2 h residence times at different temperatures (210, 240, and 270 °C). Bio-oil samples obtained at 270 °C and a 1 h residence time determined as optimum conditions were analyzed with TGA, CHNS elemental analysis, FTIR, and GC-MS. 1,4-Dioxane showed the best performance in yielding the maximum bio-oil with 51.8% at those conditions. The higher heating values of the bio-oils ranged from 22.1 to 35 MJ/kg. Phenolic groups were the predominant components of bio-oil produced from OWP, while the intensity of alcohols, ketones, and acids varied based on used solvents. Based on energy recovery calculations, the enhancement of pristine OWP's energy efficiency depended on bio-oil yield, and quality was confirmed for all solvent types (1,4-dioxane > 1-butanol > water > ethanol).

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

Biomass carbon mining to develop nature-inspired materials for a circular economy

A transition from a linear to a circular economy is the only alternative to reduce current pressures in natural resources. Our society must redefine our material sources, rethink our supply chains, improve our waste management, and redesign materials and products. Valorizing extensively available biomass wastes, as new carbon mines, and developing biobased materials that mimic nature's efficiency and wasteless procedures are the most promising avenues to achieve technical solutions for the global challenges ahead. Advances in materials processing, and characterization, as well as the rise of artificial intelligence, and machine learning, are supporting this transition to a new materials' mining. Location, cultural, and social aspects are also factors to consider. This perspective discusses new alternatives for carbon mining in biomass wastes, the valorization of biomass using available processing techniques, and the implementation of computational modeling, artificial intelligence, and machine learning to accelerate material's development and process engineering.

Hydrothermal Liquefaction of Lignocellulosic and Protein-Containing Biomass: A Comprehensive Review

Hydrothermal liquefaction (HTL) is a thermochemical depolymerization technology, also known as hydrous pyrolysis, that transforms wet biomass into biocrude and valuable chemicals at a moderate temperature (usually 200–400 °C) and high pressure (typically 10–25 MPa). In HTL, water acts as a key reactant in HTL activities. Several properties of water are substantially altered as the reaction state gets closer to the critical point of water, which can result in quick, uniform, and effective reactions. The current review covers the HTL of various feedstocks, especially lignocellulosic and high protein-containing feeds with their in-depth information of the chemical reaction mechanisms involved in the HTL. Further, this review gives insight and knowledge about the influencing factors such as biomass pretreatment, process mode, process conditions, etc., which could affect the efficiency of the hydrothermal process and biocrude productivity. In addition, the latest trends, and emerging challenges to HTL are discussed with suitable recommendations.

Trends on CO2 Capture with Microalgae: A Bibliometric Analysis

The alarming levels of carbon dioxide (CO₂) are an environmental problem that affects the economic growth of the world. CO₂ emissions represent penalties and restrictions due to the high carbon footprint. Therefore, sustainable strategies are required to reduce the negative impact that occurs. Among the potential systems for CO₂ capture are microalgae. These are defined as photosynthetic microorganisms that use CO₂ and sunlight to obtain oxygen (O₂) and generate value-added products such as biofuels, among others. Despite the advantages that microalgae may present, there are still technical–economic challenges that limit industrial-scale commercialization and the use of biomass in the production of added-value compounds. Therefore, this study reviews the current state of research on CO₂ capture with microalgae, for which bibliometric analysis was used to establish the trends of the subject in terms of scientometric parameters. Technological advances in the use of microalgal biomass were also identified. Additionally, it was possible to establish the different cooperation networks between countries, which showed interactions in the search to reduce CO₂ concentrations through microalgae.

Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments

To investigate the effects of temperature and biomass concentration of Hydrothermal liquefaction (HTL) on chemical properties of biocrudes, machine learning (ML) was used to predict the weight of hydration parameters on the properties of biocrudes. The elemental compositions, molecular weights, functional groups, thermal degradation, molecular structure of biocrudes were studied. The optimum yield of biocrudes was 65% and the highest heat value reached up to 34.28 kJ/g, showing comparable fuel properties. It was found that the hydration temperature significantly affects the elemental components, functional groups and molecular weight and structures of biocrudes. In addition, biomass concentration also affect the functional groups and structures of biocrudes. ML results indicated that Support Vector Machine Linear Kernel method is suitable for heat value prediction.