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Consortium for Computational Physics and Chemistry

A research collaboration of national laboratories for the U.S. DOE Bioenergy Technologies Office



Process Scale Modeling of Bioenergy Reactors

The Reactor Analysis and Scale-Up task of CCPC addresses a primary challenge with biomass processes: Can the process be scaled up and achieve the necessary efficiency to be economically sustainable?

For bioenergy processes, the complexity of biomass creates a challenge to address when scaling up processes, but the diverse nature of biomass feedstocks and resulting product chemistries is a critical part of a sustainable bio-economy. Thus, we focus on two challenges:

  1. Ensuring scalability of processes
  2. Ensuring efficient processes for feedstocks with diverse biocomplexity

In addition, the results from the Reactor Analysis and Scale-Up task enable translation of BETO program successes at lab bench scale to relevant technoeconomic analyses at commercial plant scales.

A combination of low-order and high-order modeling approaches

In the CCPC, we utilize both low-order and high-order models; here “low-order” refers to modeling with simplified approximations of complexity to enable more computationally feasible methods and “high- order” refers to models that capture full or near full complexity but require computationally intensive methods. The complexity of biomass feedstocks is extremely high and very difficult to fully capture in high-order modeling; so, the CCPC uses a combination of high-order and low-order modeling to capture the biocomplexity yet produce models that are computationally feasible even for larger scale reactors.

Results: capturing particle size distribution effects on pyrolysis yields

In close collaboration with the CCPC Feedstock Impact Analysis Task as well as experimentalists in the BETO program, the CCPC has developed low-order reactor models of bubbling bed pyrolysis reactors that capture the complexity of biomass feedstock particle size. Feedstock particle size is a critical parameter for pyrolysis since the time that a particle takes to heat up to pyrolysis temperatures varies greatly with larger particles taking more time. If particles spend too much time in the reactor, then the resulting oil can oxidize further and reduce product yields. Thus, controlling the combination of residence time and particle size is necessary for optimizing pyrolysis yields.

The CCPC team has developed a low-order model to predict the effect of particle size on pyrolysis yield by capturing the complexity of feedstock size and shape and corresponding effects on heat transfer and pyrolysis. The model consists of a series of CSTRs (continuously stirred tank reactors) to capture the proper profile of residence time in the reactor. Two pine feedstocks with different particle size distributions were modelled and compared with experimental results from a 2” diameter fluidized bed reactor at NREL. The model predicted experimental results to within a tolerance of less than 3% to demonstrate that particle size distribution effects can be captured effectively.

loworder

Results: understanding bubbling to slugging transitions to guide optimal reactor operation

High-order models are being used to understand the transitions from bubbling to slugging and their effect on pyrolysis yield in fluidized bed reactors. Here computational fluid dynamics (CFD) models of the reactor are capturing the dynamic effects of bed bubbling. The bubbling becomes more frequent as the gas velocity (U0) increases relative to the minimum fluidization velocity (Umf); the term U0/Umf captures this parameter. Results show that product yield or “tar” yield increase with increasing U0/Umf, but as U0/Umf increases, the reactor can enter a “slugging” phase were large bubbles violently pass through the reactor and decrease yield. These results are being utilized to optimize yield in reactor and to understand safe operating regimes for fluidized bed reactors.

highorder

Results: defining optimal residence times for vapor phase upgrading reactors

In addition to pyrolysis reactor modeling, the CCPC is modeling vapor phase upgrading reactors where pyrolysis oils are chemically converted to chemistries more suitable for used in fuel or specialty chemical products. In vapor phase upgrading, catalyst particles circulate in the reactor along with the pyrolysis oil vapors. Here the high-order model captures the flow of both catalyst particles and oil and product vapors. These high-order models are being used to determine residence times of both the catalyst particles and gas vapors so that low-order models can capture accurately the catalyst upgrading efficiency obtained by the process. The movie shown below is a model of the R-Cubed reactor riser section in the TCPDU system at NREL; these models are guiding experimentalists as they prepare to commission and operate this new large reactor system.

Computational modeling of the upgrading of fast pyrolysis oil

Barriers addressed:

  1. Yields of hydrocarbons
  2. Improve utilization of hydrogen

The CPC has targeted the modeling of two aspects of liquid phase upgrading (LPU) of bio-oil: the engineering of the reactors and the engineering of the reactions. The performance (yield) of a chemical reactor depends on three factors:

  • intrinsic kinetics of the reactions
  • residence time distribution of the reactants
  • micromixing of the contents, for reactions that are not 0 or 1st order,
    Yield = kinetics x RTD x micromixing

Intrinsic reaction kinetics can be measured in reactors that afford rates of mass transport and residence times sufficient to span the relevant ranges of rates and conversions. The residence time distribution can, at least in principle, be measured using a bolus of an inert but detectable tracer. While micromixing can be difficult to characterize experimentally, modern simulations of the fluid dynamics can help define it.

Kinetics. Because measurements of detailed kinetics of the reactions involved in LPU are still pending, for the sake of exercising the differences occasioned by nonidealized residence time distributions, we set the kinetics represent two parallel reactions, a bimolecular deactivation and a first order hydrogenation, both as macroscopic expressions that included Langmuir-type adsorption of the reactant, A (a highly lumped representation of the bio-oil) and the product, B (the precursor of “gunk”, which fouls the catalyst) and the product, C (the desired upgraded product):

equation2

The rate constants, ki and equilibrium constants, Ki, (Table 1; note that all concentrations were normalized by the concentration of the feed, A) were chosen to reflect that, at the typical operating temperature of the stabilization reactor of the LPU process, the deactivation reaction is about 10-times faster than the hydrogenation.

Table 1. Parameters employed to model the nominal reaction kinetics.
Parameter Value
k1, k2 1 h-1
k3 0.1 h-1
K 1, K3 1
K2 10
τ 1.0

The resulting concentration profiles (Figure 1) were calculated using the Matlab differential equation solver, ODE45.

concprofile
Figure 1. Calculated Concentration profiles for the assumed kinetics.

Residence time distributions. Figure 3 compares the normalized Residence Time Distributions for the two scales of reactor with those of three idealized RTDs. The characteristics of the two scaled reactors were chosen to approximate those employed in a bench scale reactor (2.5 cm diameter) with the demonstration scale reactor (10.5 cm diameter) in operation at PNNL. The fluid dynamics were modeled the continuum multi-fluid flow and transport simulator STOMP (White and Oostrom, 2006).

comparertd
Figure 2. Comparison of Residence Time Distributions from the CFD of two packed bed reactors (top) with those of idealized reactors (botttom). The time scale has been normalized such that the mean residence time for each reactor = 1.0.
Table 2. Parameters employed to model the pilot (bench) and demonstration scale reactors.
parameters

In lieu of detailing the geometry of the beds, we employed an empirical correlation devised by Cohen and Metzner (1981) that describes the radial changes in void fraction in the bed (Figure 4), given the diameter of the packing and the diameter of the cylindrical reactor.

comparertd
Figure 4. Radial variation in the density of random packings in the pilot and demonstration scale reactor.

The bimodal residence time distributions for the packed bed reactors arise from channeling at the wall, which is evident from the flow fields calculated by the fluid dynamics code (Figure 5).

gasbypass
Figure 5. While both scales of reactor exhibit gas bypass along the walls, channeling is severe in the pilot scale reactor, as would be expected from the radial dependence of packing density (vide infra).

Results. The residence time distributions were then used to estimate the conversion of bio-oil to gunk (product B), according to the simplified kinetics scheme described above. The model results show marked differences between the large and small reactors (Table 2). Happily, it appears that the larger reactor, in which less of the volume is channeled, could perform better. Yet, all the reactors appear to be susceptible to the formation of gunk because they all will maintain the polymerizable reaction mixture at temperatures sufficient to promote gunking for times long enough to complete the desired reactions. Evidently, all the results need to be validated to assure that we have employed faithful representations of the reactor geometry and, most importantly, of the reaction kinetics.

Table 3. Results of convolving the Residence Time Distributions of the reactors with the time dependent conversions for macromixing.
Reactor Estimate yield of "gunk"
Bench scale 54%
Idealized plug flow 34%
Demonstration scale 33%
Idealized laminar flow 30%
Idealized continuous stirred tank 25%

Next Steps: We are currently approaching the reaction engineering of the catalysts by calculating the effects of the solvophilicity of the support on the approach of the reactants to the vicinity of the surfaces of supported, small metal particles that catalyze the hydrodeoxygenation reactions. (See http://dx.doi.org/10.1016/j.cattod.2016.08.025).


Links

chemcat

The CCPC is an enabling project in the ChemCatBio consortium

energynetwork

ChemCatBio is part of DOE’s Energy Materials Network

fcic

Feedstock-Conversion Interface Consortium

BioESep

Bioprocessing Separations Consortium

BETO
U.S. DOE Bioenergy Technologies Office

Billion Ton Report
2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy

NREL Thermal and Catalytic Process Development Unit
Home to thermochemical reactors and pilot plants that CCPC models

PNNL Bioproducts, Sciences, and Engineering Laboratory
Home to upgrading reactors and pilot plants that CCPC models

Open source code and tools

GitHub
Computational models and functions developed by consortium members.

Surface Phase Explorer
Create interactive and downloadable surface phase diagrams from ab initio data.