Dynamic simulation of disintegration of wood chips caused by impact and collisions during the steam explosion pretreatment

Steam explosion (SE) pretreatment produces damaged and disintegrated biomass with a large surface area which facilitates enzymatic hydrolysis for the production of biofuels and other value-added chemicals. It was observed during experiments that wood chips disintegrate into smaller pieces because of collisions and impact with each other and the walls of the SE equipment. In this study, these events were simulated using the finite element method. Wood chips were simulated in this model as a linear elastic material until failure. The damage initiation was identified using Hashin’s damage initiation criteria. Once the damage was initiated, additional loading caused the evolution of damage, i.e. degradation and breakage of the material, which was modelled using the material property degradation model and deletion of the failed elements. Elastic and strength properties of spruce wood were estimated at ambient conditions (12 % moisture content at 20 °C) and at SE conditions (30 % moisture content at 160 °C). Comparison of simulations performed using material properties at ambient and SE conditions revealed that the damage in wood chips significantly increased because of the steam treatment. The effects of wood chip velocity and orientation at the time of impact were studied as well. It was found that wood chips moving at high velocity and impacting with the steel wall in the radial direction acquire the most damage.     

Diagnosing agrosilvopastoral practices using Bayesian networks

This article discusses the potential of BNs to complement the analytical toolkit of agricultural extension. Statistical modelling of the adoption of agricultural practices has tended to use categorical (logit/probit) regression models focusing on a single technology or practice, explained by a number of household and farm characteristics. Here, a Bayesian network (BN) is used to model household-level data on adoption of agrosilvopastoral practices in Tiby, Mali. We discuss the advantages of BNs in modelling more complex data structures, including (i) multiple practices implemented jointly on farms, (ii) correlation between probabilities of implementation of those practices and (iii) correlation between household and farm characteristics. This paper demonstrates the use of BNs for ‘deductive’ reasoning regarding adoption of practices, answering questions regarding the probability of implementation of combinations of practices, conditional on household characteristics. As such, BNs is a complementary modelling approach to logistic regression analysis, which facilitates exploring causal structures in the data before deciding on a reduced form regression model. More uniquely, BNs can be used ‘inductively’ to answer questions regarding the likelihood of certain household characteristics conditional on certain practices being adopted.