<p dir="ltr">Feedstock variability represents a challenge in the adoption of lignocellulosic biomass for biofuels and biochemicals production, due to the differences in critical chemical and physical properties like lignin content, and water absorption respectively. Thus, difficult continuous manufacturing processes in biorefineries, hinder the transition from liquid feedstocks to renewable materials that consisting of solid particles. Modeling of flow properties based on rheological measurements of treated biomass is a quantitative metric for identifying if different feedstocks form pumpable slurries. Additionally, the correlation of yield stress to physical and chemical properties gives a measure that accounts for the variability in the processing design. This research models rheological properties and relates these to compositional data from different non-pretreated fractions of corn stover biomass slurries. Slurries were formed with solids concentrations of 300 g/L in a 6 hours fed-batch process using the commercial enzymes Celluclast 1.5L or Ctec-2 at 1FPU/g or 3 FPU/g of dry solids, basis to enable the liquefaction (i.e., slurry-forming) mechanism. We found that insoluble lignin content of the different fractions was related to water absorption in pellets and free water on slurries and that free water was a good indicator of the potential for a material to form slurry. Higher flowability (lower yield stress) was found at higher content of lignin, particularly for materials containing 26% lignin where yield stress was reduced to 254Pa when compared with mixtures of 14% lignin that presented yield stresses of around 4000 Pa. We show that rheology modeling linked to compositional characteristics for biomass slurries can be used to predict material flow behavior in a biorefinery to optimize and achieve high solids loadings that enhance the production of ethanol for biofuels. This insight and the ability to form high concentration slurries before pretreatment holds the potential to develop new processing strategies that could help to foster a more efficient and sustainable bio-based industry. </p>

[pt] O gerenciamento correto das pastagens em regiões agropecuárias tem papel fundamental na própria produção, na prevenção ao desperdício da biomassa vegetal e a liberação de gases de efeito estufa (GEE). Além disso, é necessário evitar o movimento inapropriado do rebanho entre pastos, pois este é um processo demorado e pode ser estressante para o animal. O sucesso desta gestão requer uma avaliação eficiente dos recursos da plantação. Os estudos desenvolvidos com esta finalidade tem relação direta com a estimativa da quantidade de biomassa em uma região específica da pastagem, pois, na prática, ela não é realizada de forma precisa, devido à dificuldade de medição em toda a área delimitada. Este trabalho tem como objetivo desenvolver uma metodologia de estimativa de biomassa de baixo custo, baseada em modelos de regressão que correlacionem os atributos de entrada mais relevantes para a aplicação com o real peso da plantação, medido em g/m2 . Para os atributos, foi medida a altura da grama forrageira e calculados os índices de vegetação baseados em RGB a partir de imagens de veículos aéreos não tripulados (VANTs). Como metodologia, utilizou-se regressões lineares, não lineares, redes neurais artificiais baseados em perceptrons de múltiplas camadas e a combinação de todos os modelos gerados (stacking ensemble). Foram alcançados resultados satisfatórios utilizando modelos de redes neurais com ainda duas camadas e com a metodologia de empilhamento de modelos, alcançando um RMSE de 31.76 g/m2 , MAPE de 13.35 por cento e R 2 de 0.9. Portanto, a metodologia proposta pode se tornar uma solução promissora e acessível para a estimativa de biomassa vegetal para uma gestão eficiente e sustentável do rebanho. / [en] The correct management of pastures in agricultural regions plays a fundamental role in the production itself, in the prevention of plant biomass waste and the release of greenhouse gases (GHG). In addition, it is necessary to avoid inappropriate movement of the herd between pastures, as this is a time-consuming process and can be stressful for the animal. The success of this management requires an efficient assessment of the plant resources. The studies developed for this purpose are directly related to the amount estimation of biomass in a specific region of the pasture, because, in practice, it is not carried out accurately, due to the difficulty of measurement throughout the field. This work aims to develop a low-cost biomass estimation methodology, based on regression models that correlate the most relevant input features for the application with the actual density of the plantation, measured in g/m2 . For the features, the height of the forage grass was measured and the vegetation indexes based on RGB were calculated from images of unmanned aerial vehicles (UAV). Linear, nonlinear regression (MNLR), artificial neural networks (ANN) based on multi-layer perceptron (MLP) and the combination of all models generated (stacking ensemble) were the methodologies tested in order to achieve the best correlation. Satisfactory results were achieved using models of neural networks with two layers and using stacking ensemble methodology, reaching a RMSE of 31.76 g/m2 , MAPE of 13.35 percent and R-Squared of 0.9. Therefore, the proposed methodology may become a promising and affordable solution for plant biomass estimation toward efficient and sustainable herd management.