Sorption capacity evaluation of industrial flue gas mixture using South African coal seams: Conventional and ANN modelling

Kasturie Premlall, Lawrence Koech, Douw Faurie
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Abstract

This study investigates the sorption efficiency of synthetic flue gas compared to pure CO2 in two South African coal seams. The samples, designated AN and TD, represent distinct coal ranks, with AN being a high-rank coal (2.91 % vitrinite reflectance) and TD a medium-rank coal (1.26 % vitrinite reflectance). The experiments involved a high-pressure volumetric adsorption method to evaluate the adsorption capacities of pure CO2 and a synthetic flue gas mixture (21 % CO2, 4 % O2, 0.03 %SO2 and 74.97 % N2) at temperatures of 35 °C and 65 °C, and pressures up to 87 bar. The results indicate a significant effect of temperature on CO2 adsorption in flue gas, with a substantial drop of 49 % and 37 % observed for AN and TD coals samples, respectively, at 65 °C compared to 35 °C. Both coal samples exhibit a high adsorption preference for CO2 in flue gas, with AN showing greater affinity across all operating conditions. The presence of flue gas components significantly impacted CO2 adsorption, causing reductions of 94 % for AN and 91 % for TD at 35 °C. AN coal (high rank) showed superior adsorption capacity for all flue gas components, attributed to its favourable properties including high inertinite content, low moisture content and low ash content offering minimal adsorption hindrance. This study evaluated multi-component adsorption using the Extended Langmuir and Modified Competitive Langmuir isotherm models. Both models effectively captured the experimental data, demonstrating preferential CO2 adsorption as reflected by higher maximum adsorption capacity across all scenarios. Additionally, artificial neural network modelling of the adsorption data demonstrated a strong fit with the experimental data, yielding low MSE values and R2 values above 0.99 for training, validation and testing. This study aims to evaluate CO2 selectivity in a multi-component flue gas adsorption system in South African coals and explore the feasibility of direct flue gas injection into coal seams.

Abstract Image

利用南非煤层的工业烟气混合物的吸附能力评价:传统和人工神经网络模型
本研究考察了南非两种煤层中合成烟气与纯CO2的吸附效率。AN和TD代表不同的煤阶,AN为高煤阶煤(镜质组反射率为2.91%),TD为中煤阶煤阶煤(镜质组反射率为1.26%)。实验采用高压体积吸附法,在35°C和65°C的温度和高达87 bar的压力下,评估纯CO2和合成烟气混合物(21% CO2、4% O2、0.03% SO2和74.97% N2)的吸附能力。结果表明,温度对烟气中CO2的吸附有显著影响,在65°C时与35°C相比,在AN和TD煤样品中分别观察到49%和37%的大幅下降。两种煤样品都对烟气中的二氧化碳表现出高度的吸附偏好,AN在所有操作条件下都表现出更大的亲和力。烟气成分的存在极大地影响了二氧化碳的吸附,在35°C时,使AN减少94%,TD减少91%。AN煤(高煤级)对所有烟气组分的吸附能力都很好,这是由于其惰性元素含量高、水分含量低、灰分含量低,吸附障碍最小。本研究使用扩展的Langmuir和改进的竞争Langmuir等温线模型对多组分吸附进行了评价。这两种模型都有效地捕获了实验数据,表明在所有情景下,更高的最大吸附容量都反映了对二氧化碳的优先吸附。此外,对吸附数据的人工神经网络建模与实验数据拟合良好,获得了较低的MSE值和0.99以上的R2值,可用于训练、验证和测试。本研究旨在评价南非煤中多组分烟气吸附系统的CO2选择性,探索煤层直接注烟的可行性。
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