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.

Watermelon is a fruit produced around the world. Unfortunately, about half of it—the rind—is usually discarded as waste. To transform such waste into a useful product like flour, a thermal treatment is needed. The drying temperature for the rind that produces flour with the best characteristics is most important. A multiobjective optimization (MOO) procedure was applied to define the optimum drying temperature for the rind flour fabrication to be used in bakery products. A neural network model of the fabrication process was developed with the drying temperature as input and five process indicators as outputs. The group of process indicators comprised acidity, pH, water-holding capacity (WHC), oil-holding capacity (OHC), and batch time. Those indicators represent conflicting objectives that are to be balanced by the MOO procedure using the weighted distance method. The MOO process showed that the temperature interval from 67.3 °C to 73.1 °C holds the compromise solutions for the conflicting indicators based on the stakeholder’s preferences. Optimum indicator were 0.12–0.19 g malic acid/100 g dwb (acidity), 5.7–5.8 (pH), 8.93–9.08 g H₂O/g dwb (WHC), 1.46–1.56 g oil/g dwb (OHC), and 128–139 min (drying time).

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%.