Developing next-generation solid sorbents to improve the economy of pre- and post-combustion carbon capture processes has been challenging for many researchers. Magnesium oxide (MgO) is a promising sorbent because of its moderate sorption-desorption temperature and low heat of sorption. However, its low sorption capacity and thermal instability need to be improved. Various metal-promoted MgO sorbents have been experimentally developed to enhance the CO2 sorption capacities. Nevertheless, rigorous computational studies to screen an optimal metal promoter have been limited to date. We conducted first-principles calculations to select metal promoters of MgO sorbents. Five alkali (Li-, Na-, K-, Rb-, and Cs-) and 4 alkaline earth metals (Be-, Ca-, Sr-, and Ba-) were chosen as a set of promoters. Compared with the CO2 adsorption energy on pure MgO, the adsorption energy on the metal-promoted MgO sorbents is higher, except for the Na-promoter, which indicates that metal promotion on MgO is an efficient approach to enhance the sorption capacities. Based on the stabilized binding of promoters on the MgO surface and the regenerability of sorbents, Li, Ca, and Sr were identified as adequate promoters among the 9 metals on the basis of PW91/GGA augmented with DFT+D2. The adsorption energies of CO2 on metal-promoted MgO sorbents for Li, Ca, and Sr atoms are -1.13, -1.68, and -1.48 eV, respectively.
Comprehensive study on control system design for a rapid thermal processing (RTP) equipment has been conducted with the purpose to obtain maximum temperature uniformity across the wafer surface, while precisely tracking a given reference trajectory. The study covers from model development, identification, optimum multivariable iterative learning control (ILC), to reduced-order controller design. The highlight of the study is the ILC technique on the basis of a semi-empirical dynamic radiation model named as 4 -model. It was shown that the 4 -model-based ILC technique can remarkably improve the performance of RTP control compared with the ordinary linear model-based ILC. In addition, reduced-order control methods and the associated optimum sensor location have been addressed. The proposed techniques have been evaluated in an RTP equipment fabricating 8-in wafers.Index Terms-Iterative learning control (ILC), linear quadratic Gaussian (LQG), rapid thermal processing (RTP) control, RTP modeling.
To reduce the energy required for CO 2 desorption, an energy exchangeable three-stage dry sorbent CO 2 capture process was designed and in the step of efficiency evaluation. The process is composed of three stages working at different sorptiondesorption temperatures to utilize the heat released at higher temperature absorption cycles for the regeneration of sorbent working at lower temperature cycles; low-, medium-and high-temperature stages. For this process three kinds of sorbents having different sorption-desorption temperatures were developed; amines supported on silica (low temperature), alkali-promoted MgO (medium temperature) and Li 4 SiO 4 (high temperature). Based on the kinetic properties of these three types of sorbents, several process models were simulated and it was found that dilute-dilute sorption-desorption process is the most efficient. According to the simulation, the thermal energy demand for the three-stage CO 2 capture process was 1.68 GJ/ton-CO 2 , which means about 60% of the thermal energy required for a single-stage dry sorbent process can be saved. To evaluate this concept, real facility which can treat 60 Nm 3 /hr exhaust gas was constructed and in the step of operation.
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