A DEM approach for modeling biomass fast pyrolysis in a double auger reactor

Thermochemical conversion of biomass via fast pyrolysis process is a promising way to produce renewable fuels and chemicals. In this paper, an extended discrete element method (DEM) is developed to predict the biomass fast pyrolysis process in a double auger reactor, which is described as a reacting granular flow. The thermodynamic state of each particle is properly predicted with an addition of a heat transfer model and a reaction model on top of the traditional DEM method. The results suggest that the predictions of the thermochemical decomposition kinetics of biomass components are consistent with the experimental observations. The results also indicate that the fast pyrolysis in the reactor is controlled by the heat transfer process. Any operating condition variation in favor of enhancing heat transfer is beneficial to the fast pyrolysis process and vice versa

A novel optimization approach to estimating kinetic parameters of the enzymatic hydrolysis of corn stover

Enzymatic hydrolysis is an integral step in the conversion of lignocellulosic biomass to ethanol. The conversion of cellulose to fermentable sugars in the presence of inhibitors is a complex kinetic problem. In this study, we describe a novel approach to estimating the kinetic parameters underlying this process. This study employs experimental data measuring substrate and enzyme loadings, sugar and acid inhibitions for the production of glucose. Multiple objectives to minimize the difference between model predictions and experimental observations are developed and optimized by adopting multi-objective particle swarm optimization method. Model reliability is assessed by exploring likelihood profile in each parameter space. Compared to previous studies, this approach improved the prediction of sugar yields by reducing the mean squared errors by 34% for glucose and 2.7% for cellobiose, suggesting improved agreement between model predictions and the experimental data. Furthermore, kinetic parameters such as K2IG2, K1IG, K2IG, K1IA, and K3IA are identified as contributors to the model non-identifiability and wide parameter confidence intervals. Model reliability analysis indicates possible ways to reduce model non-identifiability and tighten parameter confidence intervals. These results could help improve the design of lignocellulosic biorefineries by providing higher fidelity predictions of fermentable sugars under inhibitory conditions

Particle scale modeling of heat transfer in granular flows in a double screw reactor

Heat transfer in granular flows plays an important role in particulate material processing such as food production, pharmaceuticals and biorenewable energy production. Better understanding of the thermodynamics in granular flows is essential for equipment design and product quality control. In this research, a particle-scale heat transfer model was developed within the frame of traditional Discrete Element Method (DEM), which considers both conductive heat transfer and radiative heat transfer among particles. A particle-wall heat transfer model was also proposed for resolving particle-wall conductive and radiative heat transfer. The developed thermal DEM model was validated by modeling heat transfer in packed beds and comparing simulation predictions with experimental measurements. The thermal DEM model was successfully applied to the simulation of heat transfer in binary component granular flows in a double screw reactor designed for biomass fast pyrolysis to gain better understanding of the heat transfer in the system. The existence of both spatial and temporal temperature oscillations is observed in the double screw reactor. The effects of the operating conditions on the average temperature profile, biomass particle temperature probability distribution, heat flux and heat transfer coefficient are analyzed. Results indicate that the particle-fluid-particle conductive heat transfer pathways are the dominant contributors to the total heat flux, which accounts for approximately 70%–80% in the total heat flux. Radiative heat transfer contributes 14%–26% to the total heat flux and the conductive heat transfer through contact surface takes only 1%–5% in the total heat flux. The total heat transfer coefficient in the double screw reactor is also reported, which varies from 70 to 110 W / (m 2 • K) depending on the operating conditions

DEM simulation of dense granular flows in a vane shear cell: Kinematics and rheological laws

The rheology of dense granular flows is investigated through discrete element method (DEM) simulation of a vane shear cell. From the simulation, profiles of shear stress, shear rate, and velocity are obtained, which demonstrates that the flow features in the vane shear cell are equivalent to those in the classic annular Couette cell. A novel correlation for the shear viscosity is formulated and leads to a new expression for μKT in the kinetic theory analysis. The μKT formulation is able to qualitatively capture the μ-I relation in the shear cell. A correlation length is added in the energy dissipation term to account for the effects of the particle motion correlation. A simplified correlation length model is derived based on DEM results and is compared with the literature. The modified granular kinetic energy equation is able to correctly predict the granular temperature profiles in the shear cell

Prediction of the Chapman–Jouguet chemical equilibrium state in a detonation wave from first principles based reactive molecular dynamics

The combustion or detonation of reacting materials at high temperature and pressure can be characterized by the Chapman–Jouguet (CJ) state that describes the chemical equilibrium of the products at the end of the reaction zone of the detonation wave for sustained detonation. This provides the critical properties and product kinetics for input to macroscale continuum simulations of energetic materials. We propose the ReaxFF Reactive Dynamics to CJ point protocol (Rx2CJ) for predicting the CJ state parameters, providing the means to predict the performance of new materials prior to synthesis and characterization, allowing the simulation based design to be done in silico. Our Rx2CJ method is based on atomistic reactive molecular dynamics (RMD) using the QM-derived ReaxFF force field. We validate this method here by predicting the CJ point and detonation products for three typical energetic materials. We find good agreement between the predicted and experimental detonation velocities, indicating that this method can reliably predict the CJ state using modest levels of computation

The co-crystal of TNT/CL-20 leads to decreased sensitivity toward thermal decomposition from first principles based reactive molecular dynamics

To gain an atomistic-level understanding of the experimental observation that the cocrystal TNT/CL-20 leads to decreased sensitivity, we carried out reactive molecular dynamics (RMD) simulations using the ReaxFF reactive force field. We compared the thermal decomposition of the TNT/CL-20 cocrystal with that of pure crystals of TNT and CL-20 and with a simple physical mixture of TNT and CL-20. We find that cocrystal has a lower decomposition rate than CL-20 but higher than TNT, which is consistent with experimental observation. We find that the formation of carbon clusters arising from TNT, a carbon-rich molecule, plays an important role in the thermal decomposition process, explaining the decrease in sensitivity for the cocrystal. At low temperature and in the early stage of chemical reactions under high temperature, the cocrystal releases energy more slowly than the simple mixture of CL-20–TNT. These results confirm the expectation that co-crystallization is an effective way to decrease the sensitivity for energetic materials while retaining high performance

Discrete element method modeling of biomass fast pyrolysis granular flows

Organic biomass is an abundant renewable resource on the earth and properly utilizing biomass resources could provide an alternative energy to traditional fossil fuels and help to mitigate the impacts of energy consumption on environment and climate change. Fast pyrolysis is one way to achieve thermochemical conversion of biomass organic materials into bio-oil at mild temperature (500 oC) in the absence of oxygen. Due to high heating rate requirement and low thermal conductivities of biomass materials, physical processes such as particulate flows, mixing and heat transfer have complicated effects on biomass fast pyrolysis at both reactor scale and particle scale. Besides the intensive research of the chemistry of biomass fast pyrolysis, study of the underlying physics is also necessary for gaining more knowledge of biomass fast pyrolysis processes in practical reactors. In this research, the biomass pyrolysis reactive granular flow in a double screw reactor is numerically investigated and the underlying physics such as particle mixing and heat transfer in the reactor are studied. A new Discrete Element Method (DEM) model was proposed with extended capability of modeling particle-particle and particle-wall heat transfer and integrating biomass devolatilization reaction models for simulating reactive granular flows. In the DEM model, the particle hydrodynamics is modeled by adopting Hertz-Mindlin nonlinear soft sphere model. The particle-scale heat transfer model considers both conductive and radiative heat transfer between particle and particle/wall. The biomass devolatilization model involves coupling with energy equation in an adaptive time step manner and considers the variation of solid particle thermal properties with temperature and conversion process. Particle flow and mixing have a great impact on biomass fast pyrolysis process by affecting the heat transfer dynamics in the granular flow. The DEM was first employed to investigate the granular flow and particle mixing in a double screw reactor. Visual observations suggest the simulation captures the particle mixing trends observed in the experiments. Results indicate that the mixing index profile in the axial direction shows a mixing-demixing-mixing oscillation pattern. Increasing screw pitch length is detrimental to mixing performance; decreasing the solid particle feed rate reduces the mixing degree; and increasing the biomass to glass bead size ratio decreases mixing performance. A comparison of a binary, single-sized biomass and glass particle mixture to a multicomponent mixture indicates that the binary system has similar mixing pattern as a multicomponent system. The developed particle-scale heat transfer model was validated by modeling heat transfer in packed beds and comparing simulation predictions with experimental measurements. The simulation results of the heat transfer in the double screw reactor indicate an existence of both spatial and temporal temperature oscillations in the granular flow. The effects of the operating conditions on the average temperature profile, biomass particle temperature probability distribution, heat flux and heat transfer coefficient are analyzed. The results show that the particle-fluid-particle conductive heat transfer pathways are the dominant contributors to the total heat flux, which accounts for approximately 70%-80% in the total heat flux. Radiative heat transfer contributes 14%-26% to the total heat flux. The heat transfer coefficient in the double screw reactor varies in a range of 70 to 110 W/(m2K) depending on the operating conditions. The proposed approach was applied to simulating biomass fast pyrolysis process in the double screw reactors. Results show that the heat of pyrolysis needs to be considered for accurate prediction of biomass pyrolysis process in the reactor. The hemicellulose and cellulose decompositions are predicted to start around 480 K and 600 K, separately, and the predictions are in agreement with experimental studies. The product yield predictions also have a good agreement with experimental studies. Results indicate that both decreasing particle size and reducing feedstock volumetric fill level in the reactor are favorable to the biomass pyrolysis process. A multi-objective kinetic parameter regression model was proposed for estimating parameters in kinetic models in the last part of this research. The proposed regression model integrated a multi-objective particle swarm optimization algorithm with ODE solver from CVODE. A case study indicates that this regression model has a better performance comparing to traditional deterministic optimization solvers.</p

A DEM modeling of biomass fast pyrolysis in a double auger reactor

Thermochemical conversion of biomass via fast pyrolysis is a proven pathway to product low-carbon crude bio-oils. In this research, an extended discrete element method (DEM) is proposed for simulating biomass fast pyrolysis reacting granular flows in a double auger reactor, in which particle hydrodynamics and interparticle heat transfer processes are involved and coupled with chemical reactions in solid particles. An adaptive time step algorithm is proposed to achieve a stable coupling between the integration of reaction ordinary differential equations and the DEM solver, and the algorithm is proven computationally efficient. A multi-component fast pyrolysis kinetics is adopted and its modeling accuracy is assessed by carrying out simulations of benchmark biomass pyrolysis experiments and comparing the prediction results with experimental data. The predicted product yields of bio-oil, char and non-condensable gas from the simulation of the biomass fast pyrolysis in the auger reactor are in satisfactory agreement with experimental measurements. The decomposition rates of biomass components in the reactor are revealed from the simulation and the pyrolysis number Py is calculated from the decomposition rate of biomass and the heat transfer coefficient. The Py number illustrates that the biomass fast pyrolysis process is limited by the heat transfer process at particle size of 2 mm

Research on Embedded Real-time Processing Technology of ARINC429 Bus Dynamic Logic Block

ARINC429 bus is widely used. In a new type of logic block communication method, the logic layer of the application layer is composed of a plurality of ARINC429 messages, and the message length and content dynamically change. The telemetry monitoring needs real-time analysis of the application layer communication protocol to correctly interpret the user-defined message content. This paper proposes an embedded real-time processing scheme, which integrates real-time processing hardware and software in data acquisition unit. It can dynamic analysis the application layer protocol of the logic block, extract user-defined information according to the telemetry for downloading, the problems encountered in telemetry monitoring of the type of communication are solved. At the same time, this solution is also applicable to real-time analysis of other avionics bus in the application layer protocols.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection