Adsorption characteristics of supercritical CO2/CH4 on different types of coal and a machine learning approach
ویژگی های جذب CO2 / CH4 فوق بحرانی در انواع مختلف ذغال سنگ و رویکرد یادگیری ماشین-2019
The injection of CO2 into deep coal beds can not only improve the recovery of CH4, but also contribute to the geological sequestration of CO2. The adsorption characteristics of coal determine the amount of the greenhouse gas that deep coal seams can store in place. Using self-developed adsorption facility of supercritical fluids, this paper studied the adsorption behavior of supercritical CO2 and CH4 on three types of coal (anthracite, bituminous coal A, bituminous coal B) under different temperatures of 35 °C, 45 °C and 55 °C. The influence of temperature, pressure, and coal rank on the Gibbs excess and absolute/real adsorption amount of supercritical CO2/CH4 on coal samples has been analyzed. Several traditional isotherm models are applied to interpret the experimental data and Langmuir related models are verified to provide good performances. However, these models are limited to isothermal conditions and are highly depended on extensive experiments. To overcome these deficiencies, one innovative adsorption model is proposed based on machine learning methods. This model is applied to the adsorption data of both this paper and four early publications. It was proved to be highly effective in predicting adsorption behavior of a certain type of coal. To further break the limit of coal type, the second optimization model is provided based on published data. Using the second model, one can predict the adsorption behavior of coal based on the fundamental physicochemical parameters of coal. Overall, working directly with the real data, the machine learning technique makes the unified adsorption model become possible, avoiding tedious theoretical assumptions, derivations and strong limitations of the traditional model.
Keywords: Supercritical CO2 | Supercritical CH4 | Coal | Adsorption model | Machine learning
Experimental and numerical investigation of supercritical CO2 test loop transient behavior near the critical point operation
بررسی تجربی و عددی فوق بحرانی رفتار گذرای تست حلقه CO2 در نزدیکی نقطه بحرانی عملیات-2016
Despite the growing interest in the Supercritical CO2 (S-CO2) Brayton cycle, research on the cycle transient behavior, especially in case of CO2 compressor inlet condition variation near the critical point, is still in its early stage. Controlling CO2 compressor operation near the critical point is one of the most important issues to operate a S-CO2 Brayton cycle with a high efficiency. This is because the compressor should operate near the critical point to reduce the compression work. Therefore, CO2 compressor operation and performance data from the S-CO2 compressor test facility called SCO2PE (Supercritical CO2 Pressurizing Experiment) were accumulated. The data are obtained under various compressor inlet conditions. Furthermore, in this study, the validation of the gas system transient analysis code GAMMA was carried out by utilizing the experimental data of SCO2PE. To simulate the data by the GAMMA code, the code was revised to model the compressor performance. A transient case for reduction in cooling event was simulated with the facility and the experimentaldata were compared to the revised GAMMA code. The revised GAMMA code showed a reasonable performance and demonstrated the potential of the code for being used in a larger scale S-CO2 power system.
KEYWORDS: supercritical carbon dioxide Brayton cycle | transient analysis | turbomachinery modeling | heat exchanger modeling | compact power conversion system