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Shahnam, Mehrdad

Publications and source records attributed to Shahnam, Mehrdad.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Air Classification of Forestry Residues for Fast Pyrolysis

Understanding critical biomass attributes through efficient fractionation is crucial for advancing sustainable pyrolysis for renewable energy and chemical production. This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency.

09 - BIOMASS FUELS↗
DOE OSTI2024

Computational Modeling of CO2 Capture By Novel PIM-RU in a Fluidized Bed Riser

The interest in carbon capture, storage, and utilization (CCUS) has increased significantly in the past few decades as it can help mitigate the threat of global warming caused by substantial increase in CO2 emissions due to anthropogenic activities. Although several CO2 capture technologies have been developed, porous solid sorbents, which adsorb CO2 by physisorption, are considered promising candidates for post-combustion CO2 capture because of the easier recovery of adsorbed CO2 and high material stability. Some prominent types of porous solid sorbents are metal-organic frameworks (MOFs), Zeolite, mesoporous silica, and polymer-based sorbents (e.g., polymers with intrinsic microporosity or PIM). Oak Ridge National Laboratory (ORNL) recently developed a novel PIM-based sorbent, referred to here as PIM-RU. The main objective of this work is to investigate the CO2 capture performance of this sorbent using computational fluid dynamics (CFD). While process configuration and reactor design are open questions, a fluidized bed riser was selected as the contactor type for this study.

Aziz, Hossain↗
DOE OSTI2024

CFD Model Development of a Small-Scale Fixed-Bed Reactor for Direct Air Capture of CO2

Direct air capture (DAC) is a promising approach to significantly reduce the CO2 concentration in the ambient air. This approach requires the extraction of CO2 from air with ultra dilute CO2 concentration which makes this approach more expensive than point source CO2 capture. Several efforts are being made in academics and industry to develop cost-efficient sorbents and solvents suitable for DAC. One of them is polymer with intrinsic microporosity (PIM) which is often used for CO2 capture because of its higher porosity and rigidity. A novel, cost-efficient aminated PIM sorbent, PF-15-TAEA, was developed at the National Energy Technology Laboratory for DAC applications. The current work is focused on the computational investigation of the CO2 capture by this novel sorbent from a gas mixture with ultra dilute concentration of CO2 in dry and humid conditions in a laboratory-scale fixed-bed reactor.

Aziz, Hossain↗
DOE OSTI2024

Numerical Simulation of Biogenic Fluid Catalytic Cracking (BFCC) Regenerators at Different Scales with MFIX-Exa

Catalytic Fast Pyrolysis (CFP) is a process that converts biomass into liquid intermediates suitable for transportation fuels by rapidly heating it in the presence of a catalyst, aiming to produce stable oils with reduced oxygen content. During CFP, the catalyst can become deactivated by the accumulation of coke, a carbon-rich deposit formed from the decomposition of biomass components. Unlike in petroleum refining, regenerating coked catalysts from biomass pyrolysis requires specific approaches due to the different chemical nature of the coke formed. An experimental technique, Temperature Programmed Oxidation (TPO), was used to study the de-coking process by gradually increasing temperature while monitoring the production of CO and CO2, which provides data for kinetic modeling. Utilizing data from TPO experiments, coke combustion kinetic model was developed to describe the rate of coke removal at different temperatures, allowing for simulation of regeneration processes. Then kinetic model is integrated into MFIX-Exa for the simulation of Biogenic Fluid Catalytic Cracker (BFCC) regenerator at different scales, enabling analysis of catalyst flow, temperature distribution, and regeneration efficiency under various operating conditions.

biogenic fluid catalytic cracking↗
DOE OSTI2024

The Effect of Air Separations on Fast Pyrolysis Products for Forest Residue Feedstocks

This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency. In summary, a detailed understanding and strategic manipulation of critical material attributes in biomass through efficient fractionation techniques are imperative for advancing fast pyrolysis as a sustainable avenue for renewable energy and chemical production.

09 BIOMASS FUELS↗
DOE OSTI2024

Microwave-Assisted Heating for Gasification

Compared to traditional heating, microwave-assisted heating enhances catalyst activity, selectivity and stability. These features are essential in gasification to improve the process outcome while mitigating the generation undesirable by-products such as char. To study the effect of microwaves on fluidized bed reactors, the electromagnetics module of COMSOL and MFiX are coupled using file input/output wherein external calls are made to COMSOL from MFiX. The particle bed heating rates and temperature distributions are investigateed in the case of a monodisperse bed of magnetite particles subjected to single mode transverse electric and magnetic fields in reacting and non-reacting conditions. The effect of inlet gas velocity on the heating rate of particle is also explored in this work.

Koneru, Rahul Babu↗
DOE OSTI2024

Characterization of Solid Sorbent for Direct Air Capture of CO2 using a CFD-based Methodology

Computational Fluid Dynamics (CFD) was used to investigate the CO2 capture by a novel PIM sorbent developed in National Energy Technology Laboratory (NETL) for direct air capture (DAC) applications. The CO2 adsorption kinetics used in the CFD was developed using isotherm data and CO2 breakthrough data obtained from experiments. Effect on humidity on the CO2 adsorption was also investigated and compared with the CO2 adsorption in dry conditions.

Aziz, Hossain↗
DOE OSTI2024

Unraveling the Pyrolytic Behavior and Kinetics of Single Polymers and Plastic-Rich Municipal Solid Waste Using Thermal Analysis

Pyrolysis is a highly promising thermochemical recycling technology for converting heterogenous plastic waste into sustainable fuels in a single step. Therefore, understanding the pyrolysis mechanism is essential for enabling rational reactor design and enhancing efficient recycling techniques. In this study, the thermal degradation behaviors and corresponding kinetics of pure polymers (PE, PP, and PET) and plastic-rich MSW were examined using simultaneous thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Experiments were carried out in the temperature range of 30-800°C with variable heating rates from 5°C/min to 20°C/min in an ultra-high purity Argon atmosphere. Our results indicated that the plastic pyrolysis was an endothermic process, with varying decomposition temperature ranges depending on their structure and composition. Various iso-conversional model-free methods (Friedman, Flynn-Wall-Ozawa, Starink, and Kissinger-Akahira-Sunose) were utilized to determine the apparent activation energy of the plastic degradation, which increased in the following order: PET (214 kJ/mol), PP (218 kJ/mol), PE (245 kJ/mol), and MSW (249 kJ/mol). Finally, Criado’s master plots were employed to identify the best-fitting reaction model and the pre-exponential factor was subsequently determined.

Bashir, Muhammad Aamir↗
DOE OSTI2024

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
DOE OSTI2024

Development of a machine learning model for polyethylene pyrolysis using a detailed reaction mechanism

Waste plastics have recently received significant attention as the issue of waste generation continues to increase. Thermal conversion processes, such as pyrolysis and gasification, are attractive potential technologies for utilizing waste plastics and reducing overall waste generation. Efficient utilization of plastics requires a detailed understanding of the conversion process such as pyrolysis and gasification. However, a mechanistic understanding of these processes lead to large and complex kinetic schemes that are not suited for large-scale and long-time simulation methods. Currently, most modeling approaches for pyrolysis and gasification rely on globally lumped, simplified kinetic schemes that provide results that are classified by their product type and not individual species, which limit the level of fidelity achieved via modeling. A machine learning (ML) model has been developed for the primary reactions of high-density polyethylene (HDPE) in an attempt to increase computational efficiency while still maintaining a high level of detail and accuracy. The ML model is trained on a detailed reaction mechanism containing 42 total species and 737 chemical reactions. A DeepONet branch and trunk architecture was adopted to train the model using time-steps relevant to computational fluid dynamics simulations. The ML used physics-informed loss functions to ensure mass conservation. The surrogate model has been deployed in simple MFiX CFD simulations, single particle and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗