Hydrothermal liquefaction of municipal sludge and its products applications

Coupling hydrothermal liquefaction and anaerobic digestion for waste biomass valorization: A review in context of circular economy

In light of the substantial global production of biomass waste, effective waste management and energy recovery solutions are of paramount importance. Hydrothermal liquefaction (HTL) and anaerobic digestion (AD) have emerged as innovative techniques for converting biomass waste into valuable resources. Their integration creates a synergistic framework that mitigates inherent limitations, leading to improved efficiency, enhanced product quality, and the comprehensive utilization of biomass. This review paper investigates the integration of HTL and AD, highlighting its significance and potential benefits as well as the optimal sequencing (HTL followed by AD and AD followed by HTL). The review encompasses experimental procedures, factors influencing both sequencing options, energy recovery characterizations, final product outcomes, as well as toxicological assessments and discussions on reduction. Additionally, it delves into the transition towards a circular bioeconomy and discusses the challenges and opportunities intrinsic to these processes. The findings presented in this review offer valuable insights to shape future research in this evolving field.

Ultrafiltration fractionation of potentially inhibitory substances of hydrothermal liquefaction aqueous phase derived from municipal sludge

Hydrothermal liquefaction (HTL) is a promising thermo-chemical technology for municipal sludge treatment due to its potential for biocrude oil recovery and minimizing biosolids management costs. However, the process generates a high volume of an aqueous byproduct that needs to be treated due to its high chemical oxygen demand (COD) and various organic and inorganic compounds. Although the aqueous phase is known to contain recalcitrant and potentially inhibitory substances that may affect its biological treatment, their molecular weight distribution (MwD) and its impact on anaerobic biodegradability are poorly understood. Ultrafiltration (UF) was conducted to fractionate HTL aqueous into different molecular weight (Mw) fractions using 300, 100, 10, and 1 kDa membranes. Mesophilic biochemical methane potential (BMP) assays were conducted to assess the anaerobic biodegradability of each fraction, and the first-order model was used to calculate the degradation kinetics of potential inhibitory compounds. The highest percentage of organics (65 %) was found in the Mw<1 kDa range, whereas the 10>Mw>1 kDa had the lowest percentage (8 %). There was no significant difference in the cumulative specific methane produced from various Mw fractions (p>0.05). The Mw<1 kDa fraction had the highest first-order specific methane production rate (0.53 day-1), whereas the unfiltered HTL had the lowest (0.38 day-1). Although UF fractionation increased the rate of anaerobic degradation of HTL aqueous for the Mw<1 kDa fraction, the observed methane potential was only 55 % of the theoretical value. This implies that 45 % of COD remains undegraded even after permeation through the lowest Mw cut-off membrane. Therefore, further characterization of HTL aqueous is needed for compounds with molecular weights below 1 kDa to fully understand the nature of inhibitory organics and their impact on anaerobic digestion. Furthermore, pretreatments utilizing techniques such as adsorption and advanced oxidation may be necessary to enhance the specific methane yields from various HTL aqueous fractions, thereby bringing them closer to the theoretical yield.

Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate

Machine learning (ML), a powerful artificial intelligence tool, can effectively assist and guide the production of bio-oil from hydrothermal liquefaction (HTL) of wet biomass. However, for hydrothermal co-liquefaction (co-HTL), there is a considerable lack of application of experimentally verified ML. In this work, two representative wet biomasses, sewage sludge and algal biomass, were selected for co-HTL. The Gradient Boosting Regression (GBR) and Random Forest (RF) algorithms were employed for regression and feature analyses on yield (Yield_oil, %), nitrogen content (N_oil, %), and energy recovery rate (ER_oil, %) of bio-oil. The single-task results revealed that temperature (T, °C) was the most significant factor. Yield_oil and ER_oil reached their maximum values around 350 °C, while that of N_oil was around 280 °C. The multi-task results indicated that the GBR-ML model of the dataset#4 (n_estimators = 40, and max_depth = 7,) owed the highest average test R2 (0.84), which was suitable for developing a prediction application. Subsequently, through experimental validation with actual biomass, the best GBR multi-task ML model (T ≥ 300 °C, Yield_oil error < 11.75 %, N_oil error < 2.40 %, and ER_oil error < 9.97 %) based on the dataset#6 was obtained for HTL/co-HTL. With these steps, we developed an application for predicting the multi-object of bio-oil, which is scarcely reported in co-hydrothermal liquefaction studies.

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.