Process and economic optimization of several pressure swing adsorption cycles were carried out to rank promising metal-organic framework (MOF) materials for post combustion carbon capture.
Carbon
capture technologies are expected to play a key role in
the global energy system, as it is likely that fossil fuels will continue
to be dominant in the world’s energy mix in the near future.
Pressure swing adsorption (PSA) is a promising alternative among currently
available technologies for carbon capture due to its low energy requirements.
Still, the design of the appropriate PSA cycle for a given adsorbent
material is a challenge that must be addressed to make PSA commercially
competitive for carbon capture applications. In this work, we propose
and test a model reduction-based approach that systematically generates
low-order representations of rigorous PSA models. These reduced-order
models are obtained by training artificial neural networks on data
collected from full partial differential algebraic equation (PDAE)
model simulations. The main contribution of this paper is the development
of surrogate models for every possible step in PSA cycles: pressurization,
adsorption, and depressurization steps in cocurrent and counter-current
operation. Three different PSA cycles (three-step, Skarstrom, and
five-step cycle) for postcombustion carbon capture applications were
employed for training purposes, and two adsorbents, Ni-MOF-74 and
zeolite 13X, were chosen to evaluate the surrogate models under optimized
cycle conditions. A good agreement was observed between the results
of the ANN models and the PDAE simulations. Average mean square errors,
for dimensionless state variables, of 1.7 × 10–8, 5.8 × 10–8, and 9.9 × 10–7 were obtained for the three PSA cycles analyzed in this work, and
the highest relative error, regarding the CO2 purity and
recovery, was 1.42%. These results suggest that the use of machine
learning techniques to develop PSA surrogate models is feasible and
that these models can be implemented in optimization environments
to synthesize PSA cycles.
Mineral
particles are detrimental to lignocellulosic biomass utilization
as fuel and feedstock of advanced biorefineries. Minerals may cause
corrosion, sintering, and vitrification in boilers, gasifiers, and
combustors as well as abrasion and erosion of mechanical equipment
used in biomass processing. In this work, we employed synchrotron
X-ray computed microtomography to analyze mineral particles in fibers
of sugar cane bagasse, the vast lignocellulosic residue of the sugar
cane industry. Hundreds of mineral particles with volumes from ∼102 to 104 μm3 were observed and
analyzed. Mineral particles were found mostly in three regions of
the biomass particles: (i) at external surfaces, (ii) at internal
surfaces associated with tissue cracks, and (iii) inside parenchyma
cells, which were ruptured for extraction of the sugar-rich juice.
These results provide novel insights for the development of bagasse
cleaning technologies aiming at improving feedstock quality for combustion
and biorefining.
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