Consortium for Computational Physics and Chemistry

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

Reactor analysis and scale-up (NETL, NREL, ORNL)

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.


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.


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.

Open source code and tools

Computational models and functions developed by consortium members.

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



The CCPC is an enabling project in the ChemCatBio consortium


ChemCatBio is part of DOE’s Energy Materials Network

U.S. DOE Bioenergy Technologies Office

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

NREL Thermochemical Users Facility
Home to thermochemical reactors and pilot plants that CCPC models

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