This
paper presents a robust kinetic model for the dehydration
of xylose in concentrated sulfuric acid (i.e., 0.1– 2 M) at
120–160 °C, i.e., conditions that were not yet explored
in the literature and hold promise in terms of process intensification.
The model is built on an extensive set of batch experiments and an
integral analysis method of the kinetic data. Direct condensation
of furfural and xylose is not a major degradation route, but the former
reacts with other dehydrated intermediates. The kinetic constants
of xylose dehydration and furfural degradation show a non-linear dependency
with respect to the hydronium concentration at higher acid concentrations
(>1 M). This trend could be attributed to a simultaneous attack
of
two hydronium ions on the formyl group of the C1 atom and on the hydroxyl
group of the C3 atom occurring under high acid concentrations (>1
M). Unlike previously reported models, the developed kinetic model
is able to predict the experimental results (xylose conversion and
furfural yield) within a 95% confidence interval under a wide range
of temperatures and sulfuric acid concentrations. Even more, this
model is also able to predict the experimental results reported in
the literature obtained with sulfuric acid with high accuracy.
In this paper, we describe the continuous production of furfural coupled with in situ stripping using hydrogen gas. With respect to the conventional semibatch process, which requires excessive steam as stripping agent and results in highly diluted furfural, this new process configuration reduces the net energy input, increases the efficiency of the downstream hydrogenation of furfural, and proposes a shift toward a continuous operation. Based on well-established thermodynamic data and previously reported kinetics, we have developed a first-principle reactor model that successfully describes the experimental observations without the use of any fitting parameters. This robust predictive model is used to further optimize the continuous production of furfural via this route.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.