Hydrothermal carbonization (HTC) research has mainly focused on primary char production, with limited attention to secondary char, which is formed through polymerization and condensation of dissolved organic compounds in the liquid phase. This research aims to address this gap via an experimental investigation of the impact of stirring on the mass and carbon balance of HTC reaction products, surface functional groups, and surface morphology of secondary char, using fructose as a model compound. A 3D hydrodynamic simulation model was developed for a two-liter HTC stirred reactor. The experimental results indicated that stirring did not significantly influence the pH, mass, carbon balance, and surface functional groups of secondary char produced under the range of experimental conditions (180 °C, 10% biomass to water (B/W) ratio, and a residence time of 0-120 min) studied. Nonetheless, it was observed that a stirring rate of 200 rpm influenced the morphology and shape of the secondary char microspheres, leading to a significant increase in their size i.e., from 1-2 μm in unstirred conditions compared with 70 μm at a stirring rate of 200 rpm. This increase in size was attributed to the aggregation of microspheres into irregular aggregates at stirring rates > 65 rpm and residence times > 1 h. The hydrodynamic model revealed that high turbulence of Re > 10⁴ and velocities > 0.17 m s⁻¹ correlated with regions of secondary char formation, emphasizing their role in particle aggregation. Particle aggregation is significant above a stirring rate of 65 rpm, which corresponds to the onset of turbulent flow in the reactor. Finally, a mechanism is proposed, based on reactor hydrodynamics under stirred conditions, that explains secondary char deposition on the reactor walls and stirrer.

Hydrothermal carbonization (HTC) is a promising process for biomass valorization; however, optimizing HTC conditions and characterizing hydrochar through experimental methods remain costly and time-consuming. Artificial intelligence (AI) and its related machine learning (ML) techniques provide an efficient and cost-effective alternative, enabling efficient optimization and predictive analysis without extensive experimental tests. In this study, an analysis-ready database (ARD) comprising 544 data points was constructed using the authors' previous research (41) and scattered data compiled from the literature (503). An ensemble of eight diversified machine learning (ML) models was developed using biomass-agnostic properties and process conditions to predict hydrochar properties including elemental analysis, proximate analysis, and hydrochar yield. Tailor-made decision fusion models were developed for each target by merging the outputs of the best-performing models. Furthermore, a set of interpretable machine learning (IML) and explainable AI (XAI) techniques, leveraging feature importance analysis and SHapley Additive exPlanations (SHAP) values, indicated that biomass fixed carbon (FC) content and process temperature are the most influencing features, which were then considered as inputs for the ensemble models. The decision fusion models achieved high accuracy for target prediction, surpassing the currently used models in the literature, with adjusted R² values ranging from 0.98 to 1.0. For outputs that are typically difficult to predict, such as hydrochar yield, the model achieved an adjusted R² of 0.98, representing over a 5 % improvement compared to the best-performing model reported in the literature. All predictions were derived from white-box modeling by incorporating XAI into the ensemble-based learning, ensuring greater model transparency and interpretability.

Although industrial hydrothermal carbonization (HTC) uses wet feedstock, lab-scale studies tend to dry the feedstock under the assumption that the rehydration of the feedstock would restore its original properties. To the best of our knowledge, this assumption has not been thoroughly examined at the lab scale; therefore, its investigation is crucial to prevent any discrepancies that might affect the upscaling of HTC. This research aims to examine the effects of pre-drying biomass by comparing it to the use of wet biomass in HTC experiments, employing three different types of biomass (rejected tomatoes, rejected apples, and digestate). Additionally, the study investigates the influence of stirring on pre-dried and wet biomass under the selected HTC conditions. The results indicate a substantial disparity in studied hydrochar properties when using pre-dried biomass compared to wet biomass. For pre-dried biomass, there is a tendency for an increase in mass yield and solid carbon yield in most examined samples (5–10% dry basis) compared to the wet biomass. Regarding functional groups, wet tomatoes and apples exhibit more pronounced peaks than pre-dried samples. Conversely, digestate shows similar spectra across all examined scenarios. The effect of stirring appears insignificant for most of the studied scenarios; nevertheless, it reduced dehydration and decarboxylation reactions during HTC.

The paper proposes a novel methodology to locate and quantify entrapped air pockets created during pipe-filling events often found in intermittent water supply systems. Different filling conditions were tested in an experimental pipe with a high point. Measurements were taken and video recordings were carried out to assess air pocket volumes for different air release conditions at the downstream end of the pipe. The stochastic nature of air pocket creation resulted in varying air volumes. A new numerical model capable of simulating the air pocket creation, dragging and entrainment has been proposed. The new model, AirSWMM, was implemented as an extension of the Stormwater Management Model (SWMM) with stochasticity of air pocket formation reproduced by simulations with different air entrainment rates. The obtained numerical results show that the proposed model, even though based on a single-phase one-dimensional flow, can accurately locate and approximately quantify the entrapped air pocket volumes.

Intermittent water supply systems are prone to air entrapments during the pipe filling phase. This work aims to analyse and discuss the numerical results obtained by applying the recently developed AirSWMM model, an extension of SWMM incorporating air phase, to a laboratory network. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapments are collected in a single loop network with a high point, for different pipe-filling conditions, system layouts and node elevations. Experimental tests have shown that the air entrapment occurred not only at the high point but also throughout the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipes. AirSWWM model with air-entrapment formation, growth and transport is tested in the pipe network, and results are compared with measurements. AirSWWM model can correctly locate large air pockets but underestimates their volume.

According to existing literature, there are no conclusive results on the impact of stirring on hydrothermal carbonization (HTC); some studies report a significant impact on the product's properties, while others indicate no influence. This study investigates the influence of stirring rate on several responses and properties of HTC products, including solid mass yield, solid carbon fraction, surface area, surface functional groups, morphology, and the fate of inorganic elements during HTC. Waste biomass was introduced as a feedstock to a 2 L HTC reactor, where the effects of temperature (180–250 °C), residence time (4–12 h), biomass to water (B/W) ratio (1–10%), and stirring rate (0–130 rpm) were investigated. The findings of this study conclusively indicated that the stirring rate does not influence any of the studied responses or properties of hydrochar under the selected experimental conditions used in this study. Nevertheless, the results indicated that a low-stirring rate (5 RPM) is enough to slightly enhanced the heating-up phase of the HTC reactor. For future research, it is recommended to examine the impact of stirring rate on the HTC of other types of biomass using the methodology developed in this study.

Stormwater management model (SWMM) software has recently become a modeling tool for the simulation of intermittent water supply systems. However, SWMM is not capable of accurately simulating the air behavior in the pipe-filling phase, missing therefore a relevant factor during pipe pressurization. This work proposes the integration of a conventional accumulator model in the existing SWMM hydraulic model to overcome this gap. SWMM source code was modified to calculate the air piezometric head inside the pipe based on the system boundary conditions, and the air piezometric head was incorporated in the SWMM flow rate pressure component. Experimental data were collected during the rapid filling of a pipe system for three possible configurations that are likely to occur in intermittent water supply pipe systems: no air release, small air release, and large air release. Results show that the improved SWMM better describes the effect of the air behavior using the extended transport (EXTRAN) surcharge method when compared to the original SWMM. Results also show that the SLOT method with predefined slot width is not suitable for this purpose; thus, further research is needed to assess if an adjusted slot width could provide better results.