Hydrothermal Liquefaction Of Biomass Research Articles

Role of sea salt in modulating biomass-to-biocrude conversion via hydrothermal liquefaction

Hydrothermal liquefaction (HTL) employs subcritical water as a reaction medium for the conversion of biomass into biocrude, however, most research relies on freshwater as the reaction medium, posing a critical resource challenge. Although preliminary investigations have demonstrated the viability of seawater as an alternative medium for HTL, the mechanistic role of sea salt in HTL of biomass remains poorly understood. In the present study, individual biomass components—carbohydrate, lignin, protein, and lipid—were subjected to HTL under varying sea salt concentrations and temperatures. Sea salt exerted an inhibitory effect on biocrude yield during carbohydrate HTL process, but no inhibitory effect was observed in HTL of lignin, protein, and lipid. As for the impact of sea salt relative to that of sodium chloride (NaCl), it was found that at a moderate sea salt concentration (13.5 wt% relative to feedstock mass) and temperature (270 °C), the minor constituents in sea salt were favorable for HTL of protein and lipid in terms of biocrude yield, energy recovery, and carbon recovery. Our findings offer valuable insights into the benefits and challenges of employing seawater as a reaction medium and illuminate the complex roles that sea salt and its minor constituents play in HTL processes.

Unraveling the effects of sodium carbonate on hydrothermal liquefaction through individual biomass model component and machine learning-enabled prediction

Despite sodium carbonate (Na2CO3) being commonly utilized as a catalyst in actual biomass hydrothermal liquefaction (HTL), its impact on individual biomass components hasn't been well-examined. This study thus delves into the role of Na2CO3 in HTL of biomass model components (carbohydrate, lignin, protein, and lipid) at varying conditions. Na2CO3 at 5 wt% amplified carbohydrate degradation into biocrude and aqueous-gaseous products (AG), resonating with previous work on carbohydrate-rich feedstocks. While Na2CO3 has a marginal effect on lignin HTL, it negatively influences lipid HTL. Here, 5 wt% and 13.5 wt% Na2CO3 decrease the biocrude yield from 95.6% to less than 10%, simultaneously increasing the AG yield to approximately 90%. This is presumably due to the interaction of lipid decomposition intermediates (fatty acids) with sodium cations, resulting in water-soluble soap. For protein HTL, a low Na2CO3 concentration (5 wt%) has no significant impact on product formation, but excessive Na2CO3 (27 wt%) converts a considerable portion of biocrude into AG. In addition to these explorations that provided insights for actual biomass HTL, we also developed a machine learning model to adequately predict HTL biocrude yield by taking Na2CO3 effect into consideration. The Adaboost machine learning model displayed the most satisfactory prediction performance (training and testing R2: 0.96, 0.8) among the three investigated machine learning models. The feature importance analysis reveals lipid content and Na2CO3 concentration as pivotal factors over HTL process conditions and other biochemical components. This research provides fundamental insights into the Na2CO3 role in HTL product formation pathway and offers an intelligent and precise prediction model for HTL biocrude yield, thereby advancing the development of more efficient and sustainable bioenergy production processes.

Agricultural Waste Liquefied Hydrothermally using Heterogeneous Catalyst

Due to its abundance and sustainability, lignocellulosic biomass is a possible replacement for petroleum oil in the production of energy and chemicals. Numerous thermochemical processes have been used in significant study to turn biomass into products with added value. One of the best methods for creating bio-fuels and bio-based compounds among them is hydrothermal liquefaction (HTL). However, a number of technological obstacles still need to be removed before HTL technology can be widely used in industry. Hydrothermal liquefaction is now thought to be amongst the most popular effective processes to converting moist biomass for bio crude, but it requires costly renovation procedures to be utilized as biofuel. It is crucial to employ catalysts that may straightforwardly improve the bio crude yield as well as the efficiency of the reaction process; the benefit of raising the operation’s overall production; the impacts of adding heterogeneous catalysts and how they affect the bio-crude yield. In lignocellulosic biomass hydrothermal liquefaction, a typical catalytic activity was discovered, dividing the various catalysts into four separate groups (transition metal, lanthanide oxide, alkaline metal oxide, and zeolite). The purpose study is to objectively evaluate the hydrothermal liquefaction of lignocellulosic biomass and know effecting of adding a zeolite catalyst on it, with a focus on increasing the production and efficiency of the biofuel. In addition, it has drawn attention to the natural stimulatory effects associated with zeolite catalysts.

Disposition of Solids from Hydrothermal Liquefaction of Biomass: Current Understanding, Research Gaps, and Opportunities

Disposition of Solids from Hydrothermal Liquefaction of Biomass: Current Understanding, Research Gaps, and Opportunities

Process simulation for mass balance of continuous biomass hydrothermal liquefaction with reaction kinetics

Hydrothermal Liquefaction (HTL) process has been investigated for biomass conversion to bio-crude and other valuable materials. However, this process modeling remains a challenge due to the high complexity of its feedstock and product compositions and the numerous chemical reactions occurring simultaneously. Here, we investigate the mass balance of the HTL reaction using a process simulation with a kinetic model previously developed for the HTL of microalgae. This model was implemented in UniSim-Design software, and the simulation's performance was validated against published experimental results for various feedstocks, including microalgae, macroalgae, food waste, and model protein. Bio-crude had the best performance for predicting the products' yields from HTL of microalgae, and aqueous-phase had the poorest. For predicting the bio-crude yield from HTL of different feedstock categories, microalgae had the best performance, and model proteins had the poorest. Insights are suggested for improved modeling towards a more robust system-design-oriented approach for process simulations.

Prediction of biocrude oil yields from hydrothermal liquefaction using a gradient tree boosting machine approach with principal component analysis

Biomass can be converted to bio-fuels and bio-chemicals via many thermo-chemical conversion platforms such as hydrothermal liquefaction (HTL) which occurs at high temperature and pressure conditions. HTL can be used for converting wet or high moisture content biomass to biocrude oils effectively. There are wide variety of relevant parameters affecting the system of biomass HTL, for example, operating conditions, biomass characteristics, solvent properties, or catalyst identities used. This brings about the difficulty in making highly accurate prediction of biocrude oil yields from biomass HTL. In this work, a gradient tree boosting machine learning (GTB-ML) model with principal component analysis (PCA) was applied to predict the yields of biocrude oils from biomass HTL, using 15 input variables from biomass characteristics, operating conditions, and solvent properties. PCA is a statistical method to find correlations in multivariate data, and it can be used to specify highly influential variables in input datasets for ML models. PCA results confirmed that input variables from biomass characteristics were as important as those from HTL operating conditions. The GTB-ML developed from principal components 1 to 8, representing about 90% of the whole original dataset can be used to predict the yields of bio-crude oils from biomass HTL at acceptable accuracy with correlation coefficient, R2=0.8 and root mean square error = 0.005.

SCC susceptibility of chromized type 409 stainless steel in alkaline chloride solutions at ambient temperature and pressure

Biomass hydrothermal liquefaction (HTL) is operated in a harsh reaction medium that contains hot pressurized water, inorganic acidic or basic catalyst, and inorganic/organic corrosive components released during the conversion. Candidate alloys for this application require suitable resistance to both corrosion and stress corrosion cracking (SCC) to withstand the HTL process conditions (250°C–374°C and 4–22 MPa). Ferritic iron-chromium (Fe-Cr) steels are more prone to corrosion but less susceptible to SCC compared to austenitic iron-chromium-nickel (Fe-Cr-Ni) steels. Chromizing can significantly reduce corrosion of Type 409 stainless steel (Fe-11Cr) in a simulated aqueous HTL solution. The objective of this study is to determine the SCC susceptibility of chromized Type 409 stainless steel, relative to the bare (non-chromized) case. The slow strain rate testing (SSRT) technique was used for this purpose. For simplicity of experimentation, SSRT was conducted using simulated HTL water containing 800 ppm potassium chloride (KCl), 1 M potassium carbonate (K2CO3), and 10 wt% acetic acid at ambient temperature and pressure. Complementary potentiodynamic polarization measurements and surface analysis by X-ray photoelectron spectroscopy (XPS) were also made to help interpret the SSRT results. The SSRT results show no significant difference in SCC susceptibility, regardless of the starting surface. Thus, chromizing, while significantly reducing the corrosion of Type 409 stainless steel, does not adversely affect SCC susceptibility, at least under the conditions tested.

Statistical Models for Predicting Oil Composition from Hydrothermal Liquefaction of Biomass

Statistical Models for Predicting Oil Composition from Hydrothermal Liquefaction of Biomass

Roles of major alloying elements in steels and alloys on corrosion under biomass hydrothermal liquefaction (HTL) conversion

Hydrothermal liquefaction (HTL) is a promising thermochemical technology to convert wet biomass and biowaste streams into bioenergy products. Significant knowledge gaps exist due to reactors’ corrosion damage. This study investigated the roles of alloying elements (Cr, Fe, Ni and Mo) on corrosion in simulated HTL environments via thermodynamic calculation and autoclave tests. Cr acts as the dominant alloying element, and the tested Fe-based steels and Ni-based alloys with Cr content over about 16% exhibit satisfied resistance to the HTL environments. Influences of alloying elements Ni and Mo are different depending on Cr content in the steels and alloys.

Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass

In this work, we present and validate a methodology for generating reaction networks from spectroscopic data using data-driven methods by applying it to the hydrothermal liquefaction (HTL) of Monterrey pine biomass and its constituents, viz., cellulose and lignin. This work is presented as a step toward automated inference of chemistry of the hydrothermal liquefaction process, thus limiting the need for human expertise. Bayesian hierarchical clustering of spectra and self-modeling multivariate spectral curve resolution are used to generate groups of chemically similar species, the reaction networks among which have been developed using Bayesian networks. Fourier transform infrared spectroscopy and proton nuclear magnetic resonance spectroscopy-based measurements are used as input data. The data-driven reaction network includes pathways representing decomposition of the biomass components, large molecule hydrolysis, and reformation of produced molecules and is consistent with the literature. Furthermore, the comparison of the networks generated for biomass and its components (levoglucosan, representing cellulose, and 2-phenoxy-ethyl benzene, representing lignin) reveals the relationship between the biomass HTL reaction network and the reaction networks of the components. The data-driven approach provides a diagnostic tool to identify the most probable reaction chemistry for complex biomass feedstocks and can be used for process understanding, design, and control.

Hydrothermal liquefaction of biomass with molybdenum, aluminum, cobalt metal powder catalysts and evaluation of wastewater by fungus cultivation

In this study, the Ammi visnaga plant was liquefied in the presence of Mo, Al and Co metal powder catalysts and without catalyst. Liquefaction trials were carried out at 225, 250, 275 and 300 °C reaction temperatures and reaction times of 0, 15, 30 and 45 min. The highest liquid product yield was determined as 42.31% in the experiment without catalyst and 39.42% with Mo catalyst at 300 °C. GC-MS, XRD, TOC, SEM-EDX and Elemental analysis methods were used to characterization of solid and liquid product. The highest energy values were determined as 25.34 MJ/kg for light oil and 29.69 MJ/kg for heavy oil with Co catalyst. In order to investigate the agricultural application potential, the effects of the wastewater obtained at the end of the trials on Trichoderma harzianum, Clonostachys rose and Rhizoctonia solani fungi were investigated. It was determined the bioagent fungi developed and pathogen growth was inhibited.

Biofuel potential of liquid products from protein- and lipid-rich biomass hydrothermal liquefaction

Hydrothermal liquefaction (HTL) of oleaginous microorganisms is an economical method with which to transform all compounds of wet biomass into competitive biofuels without the need for pretreatments such as feedstock drying and lipid extraction. In the present work, liquefaction of microalgae or yeasts in subcritical water is investigated in a self-built HTL system. Fractional yield distribution of the end-products and characterization of biocrudes from algae HTL are studied by considering the effects of the reaction temperature and residence time. The identified compounds in the biocrudes are compared to those in yeast-derived HTL biocrudes and the reaction scheme is reported. The present work reveals that no significant quality change of algae-derived HTL biocrudes is observed in a moderate temperature range thus the N-containing compounds vary complicatedly at different residence times. Compared to the pyrolysis biocrudes of algae and yeast, the collected HTL biocrudes do not perform the advantage, in terms of biofuel potential of a high portion of N-containing compounds, especially heterocyclic compounds. It should be noted that the low protein content in the feedstocks cannot definitely be proven to have led to the reduced heterocyclic compounds in HTL biocrudes. If lipids had been present, this would have enhanced the formation of N-containing heterocyclic compounds. Instead, amino acid degradation was accelerated by the high level of carbohydrates, forming amides. This paper presents a promising effort to develop an efficient route to transform oleaginous microorganisms into biofuels.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R2 of 0.92 and 0.95, respectively. Acceptable predictive performance (test R2 of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.

Microbial shifts in anaerobic digestion towards phenol inhibition with and without hydrochar as revealed by metagenomic binning

The inhibition of anaerobic digestion (AD) by phenolic compounds is an obstacle to the efficient treatment of organic wastes. Besides, hydrochar produced from hydrothermal liquefaction of biomass has been previously reported to enhance AD. The present study aimed to provide deep insights into the microbial shifts at the species level to phenol (0–1.5 g/L) inhibition in AD of glucose with and without hydrochar by metagenomic analysis. Phenol higher than 1 g/L had severe inhibition on both the amount and rate of methane production in control experiments, while hydrochar significantly enhanced methane production, especially at phenol 1 g/L and 1.5 g/L. From metagenomic analysis, 78 High-quality metagenome-assembled genomes (MAGs) were obtained. Principal components analysis showed that the microbial communities were shifted when phenol concentration was increased to 0.25 g/L in control experiments and 1 g/L in hydrochar experiments. In control experiments, no MAGs involved in acetogenesis were found at phenol 1.5 g/L and Methanothrix sp.FDU243 was also inhibited. However, hydrochar resulted in the maintenance of several MAGs involved in acetogenesis and Methanothrix sp.FDU243 even at phenol 1.5 g/L, ensuring a persistent methane production. Furthermore, 6 phenol-degrading MAGs were identified, shifting dependent on the concentrations of phenol and the presence of hydrochar.

Impacts of catalyst, inorganic and organic corrodants on corrosion under batch-mode catalytic biomass hydrothermal liquefaction conversion

Catalytic hydrothermal liquefaction (CHTL) is used to convert wet biomass for highly promising carbon conversion efficiency. This study investigated three major environmental factors, including catalyst, inorganic corrodants and organic acid on the corrosion of two candidate reactor steels (SS310 and P91) using autoclave methodology and advanced characterization techniques. The addition of 0.5 M K2CO3 resulted in a remarkable increase in corrosion rates and significantly changed the corrosion layers formed. The acetic acid generated from CHTL also noticeably enhanced corrosion while the presence of Cl- and S2- only had marginable influence. SS310 exhibited much better performance than P91 under the CHTL conditions.

Machine learning predicts and optimizes hydrothermal liquefaction of biomass

The hydrothermal liquefaction process has recently attracted more attention in biorefinery design and implementation because of its capability of handling various wet biomass feedstocks. However, measuring the quantitative and qualitative characteristics of hydrothermal liquefaction (by)products is challenging because of the need for time-consuming and cost-intensive experiments. Machine learning technology can cope with this issue thanks to its ability to learn from past datasets and mechanisms. Hence, machine learning was applied herein to quantitatively and qualitatively characterize hydrothermal liquefaction (by)products based on biomass composition and reaction conditions. The data patterns compiled from the published literature were used to develop a universal machine learning model applicable to a wide range of biomass feedstocks and reaction conditions. The collected data were statistically analyzed and mechanistically discussed. Among the four machine learning models considered, Gaussian process regression could provide the highest accuracy, with a correlation coefficient higher than 0.926 and a mean absolute error lower than 0.031. An effort was also made to maximize biocrude oil quantity and quality and minimize byproducts quantity using the objective functions developed by the selected model. The optimal biocrude oil yield (48.7–53.5%) was obtained when the carbon, hydrogen, nitrogen, oxygen, sulfur, and ash contents of biomass were in the range of 40.9–48.3%, 9.72–9.80%, 11.9–13.6%, 15.2–15.6%, 0.0–0.94%, and 0.0–2.92%, respectively. The optimal operating conditions were: operating dry matter = 31.4–33.0%, temperature = 394–400 °C, reaction time = 5–9 min, and pressure = 30.0–35.6 MPa. An easy-to-use software package was developed based on the selected machine learning model to pave the way for bypassing unnecessary lengthy and costly experiments without requiring extensive machine learning knowledge. The present study highlights the vast potential of machine learning for modeling biomass hydrothermal liquefaction.

Recent Advancements in Microalgal Mediated Valorisation of Wastewater from Hydrothermal Liquefaction of Biomass

Recent Advancements in Microalgal Mediated Valorisation of Wastewater from Hydrothermal Liquefaction of Biomass

The Role of Catalysts in Biomass Hydrothermal Liquefaction and Biocrude Upgrading

Hydrothermal liquefaction (HTL) of biomass is establishing itself as one of the leading technologies for the conversion of virtually any type of biomass feedstock into drop-in biofuels and renewable materials. Several catalysis strategies have been proposed for this process to increase the yields of the product (biocrude) and/or to obtain a product with better properties in light of the final use. A number of different studies are available in the literature nowadays, where different catalysts are utilized within HTL including both homogeneous and heterogeneous approaches. Additionally, catalysis plays a major role in the upgrading of HTL biocrude into final products, in which field significant developments have been observed in recent times. This review has the ambition to summarize the different available information to draw an updated overall picture of catalysis applied to HTL. The different catalysis strategies are reviewed, highlighting the specific effect of each kind of catalyst on the yields and properties of the HTL products, by comparing them with the non-catalyzed case. This allows for drawing quantitative conclusions on the actual effectiveness of each catalyst, in relation to the different biomass processed. Additionally, the pros and cons of each different catalysis approach are discussed critically, identifying new challenges and future directions of research.

Valorization of the spent catalyst from flue gas denitrogenation by improving bio-oil production from hydrothermal liquefaction of pinewood sawdust

Normally, the spent catalyst (V2O5-WO3/TiO2, denoted as VWTi) is dumped as a solid waste after denitrogenation of flue gas through selective catalyst reduction (SCR). However, this used catalyst contains an abundant amount of acid sites and thus can be potentially utilized as the catalyst in hydrothermal liquefaction (HTL) to produce bio-oil. Thus, in this study, the influences of operational variables on the main HTL products distribution and physicochemical properties of bio-oil were evaluated at 240–320 °C for 0–60 min and 0–20 wt% of catalyst dosage. The results demonstrated that the spent VWTi catalyst significantly promoted the production of bio-oil, yielding the highest bio-oil yield (33.9 wt%) at 300 °C for 30 min with a 10 wt% of catalyst loading. The bio-oil obtained with catalyst under the optimal conditions has a HHV of 28.69 MJ/kg, along with an energy recovery of 51.93%. In addition, the use of the spent VWTi catalyst in the HTL could obtain a higher fraction of phenolics compounds with lower boiling point and lower molecular weight, as identified by TGA and GPC analysis. Overall, this study provides an alternative route to valorize the spent catalyst from flue gas denitrogenation by applying as a catalyst to produce high quality bio-oil production in the biomass HTL process.

EFFECT of surface treatment on corrosion control of P91 hydrothermal aqueous solutions

ABSTRACT The proven efficacy of surface treatment on ferritic-martensitic (F/M) steels in subcritical and supercritical water environments has spawned interest in its application for the construction of biomass hydrothermal liquefaction (HTL) conversion reactors. The objective of this paper is to determine the relative extent to which well-established surface treatments, including mechanical abrasion (baseline), sandblasting, shot peening, and pickling, affect corrosion of the steels in a simulated HTL biomass conversion environment. These treatments were administered to P91 steel for the purpose of improving corrosion resistance towards that exhibited by Type 304 austenitic stainless steel. Samples were immersed in a simulated HTL conversion environment at 310 °C and 10 MPa for 10 days using a static autoclave testing system. None of the surface treatments administered improved corrosion control of P91. The structure and chemistry of the oxide scale formed during immersion were not altered by the surface treatments administered, i/e., none of which promoted of the formation of a more protective Cr-enriched scale. Shot peening was particularly problematic as it promoted the formation of Cr-rich carbides in the near-surface region, which, when incorporated into the oxide scale that formed during high temperature immersion, served to reduce the amount Cr available for oxidization.