Spent grains from microbreweries are mostly formed by malting barley (or malt) and are suitable for a further valorization process. Transforming spent grains from waste to raw materials, for instance, in the production of nontraditional flour, requires a previous drying process. A natural convection solar dryer (NCSD) was evaluated as an alternative to a conventional electric convective dryer (CECD) for the dehydration process of local microbrewers’ spent grains. Two types of brewer’s spent grains (BSG; Golden ale and Red ale) were dried with both systems, and sustainability indices, specific energy consumption (e_C), and CO₂ emissions were calculated and used to assess the environmental advantages and disadvantages of the NCSD. Then, suitable models (empirical, neural networks, and computational fluid dynamics) were used to simulate both types of drying processes under different conditions. The drying times were 30–85 min (depending on the drying temperature, 363.15 K and 333.15 K) and 345–430 min (depending on the starting daytime hour at which the drying process began) for the CECD and the NCSD, respectively. However, e_C and CO₂ emissions for the CECD were 1.68–1.88 · 10⁻³ (kW h)/kg and 294.80–410.73 kg/(kW h) for the different drying temperatures. Using the NCSD, both indicators were null, considering this aspect as an environmental benefit.

This work studies the performance of two open-source CFD codes, OpenFOAM and MFiX, to address bubbling fluidized bed system at different temperature and heat transfer conditions. Both codes are used to predict two parameters that are relevant for the design of fluidized units: the minimum fluidization velocity as a function of the temperature of the bed and wall-to-bed heat transfer coefficient from a lateral wall and from internal tubes. Although the CFD solvers are structuraly similar, there are some key differences (available models, meshing techniques, and balance formulations) that are often translated into differences in the fields prediction. The computational results are compared between both codes and against the experimental data. The minimum fluidization velocity can be correctly predicted with both codes at different temperatures while, in general, for the heat transfer and the fluidization patterns, MFiX shows slightly more accurate results compared to OpenFOAM but with low versatility for meshing curved geometries which might translate into higher computational costs for the same level of accuracy.

This study investigated the fast pyrolysis of biomass in fluidized-bed reactors using computational fluid dynamics (CFD) with an Eulerian multifluid approach. A detailed analysis was conducted on the influence of various modeling parameters, including hydrodynamic models, heat transfer correlations, and chemical kinetics, on the product yield. The simulation framework integrated 2D and 3D geometrical setups, with numerical experiments performed using OpenFOAM v11 and ANSYS Fluent v18.1 for cross-validation. While yield predictions exhibited limited sensitivity to drag and thermal models (with differences of less than 3% across configurations and computational codes), the results underline the paramount role of chemical kinetics in determining the distribution of bio-oil (TAR), biochar (CHAR), and syngas (GAS). Simplified kinetic schemes consistently underestimated TAR yields by up to 20% and overestimated CHAR and GAS yields compared to experimental data (which is shown for different biomass compositions and different operating conditions) and can be significantly improved by redefining the reaction scheme. Refined kinetic parameters improved TAR yield predictions to within 5% of experimental values while reducing discrepancies in GAS and CHAR outputs. These findings underscore the necessity of precise kinetic modeling to enhance the predictive accuracy of pyrolysis simulations.

The fluidization of certain biomasses used in thermal processes, such as sawdust, is particularly difficult due to their irregular shapes, varied sizes, and low densities, causing high minimum fluidization velocities (U_mf). The addition of an inert material causes its U_mf to drop significantly. The determination of the U_mf of the binary mixture is however hard to obtain. Generally, predictive correlations are based on a small number of specific experiments, and sphericity is seldom included. In the present work, three models, i.e., an empirical correlation and two artificial neural networks (ANN) models were used to predict the U_mf of biomass-inert mixtures. An extensive bibliographical survey of more than 200 datasets was conducted with complete data about densities, particle diameters, sphericities, biomass fraction, and U_mf. With the combined application of the partial dependence plot (PDP) and the ANN models, the average effect of sphericity on U_mf was quantitatively determined (inverse relationship) together with the average impact of the biomass fraction on U_mf (direct relationship). In comparison with the empirical correlations, the results showed that both ANN models can accurately predict the U_mf of the presented binary mixtures with errors lower than 25%.

The expansion of unconventional hydrocarbon extraction in the Vaca Muerta Formation (Argentina) has increased the generation of oil-based drill cuttings (OBDCs), a hazardous waste containing up to 20 wt% total petroleum hydrocarbons (TPHs) and trace metals. These characteristics pose risks to soil and groundwater, highlighting the need for sustainable treatment technologies that minimize environmental impacts and enable resource recovery. This study evaluates slow pyrolysis as a thermochemical route for OBDC stabilization and valorization. Representative samples were characterized through proximate, ultimate, and metal analyses, confirming a complex hydrocarbon–mineral matrix with 78.1 wt% ash, 15.9 wt% volatile matter, and 12.5 wt% TPH. Thermogravimetric analysis (10–20 °C min⁻¹), combined with isoconversional methods, identified three pseudo-components with activation energies ranging from 41.9 to 104.5 kJ mol⁻¹. Slow pyrolysis experiments in a fixed bed (400–650 °C) reduced residual TPH to below 1 wt% at temperatures ≥ 400 °C, meeting Argentine criteria for non-hazardous solids. The process also produced a condensed liquid organic fraction, supporting its potential within circular-economy strategies. Overall, the results show that slow pyrolysis is a viable and sustainable technology for reducing environmental risks from OBDC while enabling resource and energy recovery, contributing to a broader understanding of their thermochemical treatment.