Three-dimensional cfd simulation of the regeneration of mgo-based sorbent in a carbon capture process

Carbon dioxide is the primary greenhouse gas emitted through human activities; therefore, efficient reduction of CO2 is regarded as one of the key environmental challenges of the current century. Different processes have been introduced in the literature for CO2 capture; among these solid sorbent processes have shown potential advantages. In order to achieve steady CO2 capture using solid sorbents, a circulating fluidized bed (CFB) is used that consists mainly of a carbonator reactor (where the CO2 is adsorbed by solid sorbents) and a regenerator (where carbonated sorbents release CO2 and a concentrated CO2-steam mixture is produced). Different solid sorbents have been developed to be utilized in carbon capture units such as MgO-based sorbets and CaO-based sorbents. In this study, an MgO-based solid sorbent was used due to its capability to capture CO2 at high temperature (300-550 ᵒC), which is in the vicinity of the operating conditions of advanced power plants (e.g. integrated gasification combined cycles [IGCC]). Use of Mgo based sorbent results in a lower energy penalty in the carbonation/regeneration cycle of MgO-based sorbents. In this study, three dimensional CFD simulations of the regeneration unit of the carbon capture process using MgO-based solid sorbents were investigated and the performance of the fluidized bed regenerator unit, operating at different conditions, was studied

CFD Simulation of CO2 Sorption in a Circulating Fluidized Bed Using Deactivation Kinetic Model

The Computational Fluid Dynamics (CFD) approach was used to simulate sorption of CO2 using solid sorbents in the riser section of a circulating fluidized bed. The simulation results were compared with the experimental data of Korea Institute for Energy Research (KIER) for continuous CO2 sorption using potassium carbonate in a circulating fluidized bed system

Simulation of a Pulsating Bed Using Eulerian Approach

A numerical study of the effect of gas pulsation on the flow pattern of solid particles in a two-dimensional gas-solid fluidized bed was conducted using the Eulerian granular kinetic theory. Our simulated bed dynamics agreed well with the experimental work of Koksal and Vural, and with the Discrete Element Method (DEM) model simulations of Tsuji et al

CFD SIMULATION OF PHARMACEUTICAL PARTICLE DRYING IN A BUBBLING FLUIDIZED BED REACTOR

The bubbling fluidized bed flow regime is characterized by high heat and mass transfer rate and leads to a relatively short drying time. In this study, the gas-solid mixing and drying of pharmaceutical particles in a bubbling fluidized bed reactor was simulated using Computational Fluid Dynamics approach

NUMERICAL AND EXPERIMENTAL STEADY STATE FLOW AND HEAT TRANSFER IN A CONTINUOUS SPOUTED BED

Experimental measurements on heat transfer and flow properties and CFD simulations based on the two-fluid model (Eulerian-Eulerian approach) were performed in a draft tube continuous spouted bed system. The simulated results on the gas-particle flow patterns and the temperature distribution in the bed agreed well with our experimental data

NUMERICAL AND EXPERIMENTAL STEADY STATE FLOW AND HEAT TRANSFER IN A CONTINUOUS SPOUTED BED

Experimental measurements on heat transfer and flow properties and CFD simulations based on the two-fluid model (Eulerian-Eulerian approach) were performed in a draft tube continuous spouted bed system. The simulated results on the gas-particle flow patterns and the temperature distribution in the bed agreed well with our experimental data

Data‐enabled cognitive modeling: Validating student engineers’ fuzzy design‐based decision‐making in a virtual design problem

The ability of future engineering professionals to solve complex real‐world problems depends on their design education and training. Because engineers engage with open‐ended problems in which there are unknown parameters and multiple competing objectives, they engage in fuzzy decision‐making, a method of making decisions that takes into account inherent imprecisions and uncertainties in the real world. In the design‐based decision‐making field, few studies have applied fuzzy decision‐making models to actual decision‐making process data. Thus, in this study, we use datasets on student decision‐making processes to validate approximate fuzzy models of student decision‐making, which we call data‐enabled cognitive modeling. The results of this study (1) show that simulated design problems provide rich datasets that enable analysis of student design decision‐making and (2) validate models of student design cognition that can inform future design curricula and help educators understand how students think about design problems

Designing with and for Youth: A Participatory Design Research Approach for Critical Machine Learning Education

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9 - 13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children’s interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment

Teaching and Assessing Engineering Design Thinking with Virtual Internships and Epistemic Network Analysis

An engineering workforce of sufficient size and quality is essential for addressing significant global challenges such as climate change, world hunger, and energy demand. Future generations of engineers will need to identify challenging issues and design innovative solutions. To prepare young people to solve big and increasingly global problems, researchers and educators need to understand how we can best educate young people to use engineering design thinking. In this paper, we explore virtual internships, online simulations of 21st-century engineering design practice, as one method for teaching engineering design thinking. To assess the engineering design thinking, we use epistemic network analysis (ENA), a tool for measuring complex thinking as it develops over time based on discourse analysis. The combination of virtual internships and ENA provides opportunities for students to engage in authentic engineering design, potentially receive concurrent feedback on their engineering design thinking, and develop the identity, values, and ways of thinking of professional engineers