Sorption of methane and CO_2 for enhanced coalbed methane recovery and carbon dioxide sequestration

Sequestration of CO2 in deep and unmineable coal seams is one of the attractive alternatives to reduce its atmospheric concentration. Injection of CO2 in coal seams may help in enhancing the recovery of coalbed methane. An experimental study has been carried out using coal samples from three different coal seams, to evaluate the enhanced gas recovery and sequestration potential of these coals. The coals were first saturated with methane and then by depressurization some of the adsorbed methane was desorbed. After partial desorption, CO2 was injected into the coals and subsequently they were depressurized again. Desorption of methane after the injections was studied, to investigate the ability of CO2 to displace and enhance the recovery of methane from the coals. The coals exhibited varying behavior of adsorption of CO2 and release of methane. For one coal, the release of methane was enhanced by injection of CO2, suggesting preferential adsorption of CO2 and desorption of methane. For the other two coals, CO2 injection did not produce incremental methane initially, as there was initial resistance to methane release. However with continued CO2 injection, most of the remaining methane was produced. The study suggested that preferential sorption behavior of coal and enhanced gas recovery pattern could not be generalized for all coals.

Catalytic steam methane reforming enhanced by CO_2 capture on CaO based bi-functional compounds

Sorption enhanced steam methane reforming（SE-SMR） was performed to maximize hydrogen production and contemporary remove COfrom the product stream using bi-functional sorbent-catalyst compounds.Samples were tested at two different scales: micro and laboratory. The CaO amount varied in the CaO-CaAlOsorbent system synthesized by wet mixing(CaO content of 100 wt%, 56 wt%, 30 wt%, or 0 wt% and balance of CaAlO) which were upgraded to bi-functional compounds by impregnation of 3 wt% of Ni. Nitrogen adsorption（BET/BJH）, X-Ray Diffraction（XRD）, Temperature-Programmed Reduction（TPR） and Scanning and Transmission Electronic Microscopy（SEM and TEM, respectively） analyses were performed to characterize structural and textural properties and reducibility of the bi-functional materials and evaluate their catalytic behavior. A fixed sorbent composition CaO-CaAlO(56 wt% of CaO and CaAlObalance), was chosen to study the effect of different weight hourly space times（WHST） and CHstream compositions in SE-SMR activity. Impregnated mayenite at both micro and laboratory scales showed stable Hcontent of almost 74%, with CHconversion of 72% similarly to the values reported by the sample containing 30 wt% of CaO in the post-breakthrough.Sample with 30 wt% of CaO showed promisingly behavior, enhancing Hcontent up to almost 94.5%.When the sorption enhanced reaction is performed roughly 89% of CHconversion is achieved, and after the pre-breakthrough, the catalyst worked at the thermodynamic level. During cycling sorption/regeneration experiments, even if COremoval efficiency slightly decreases, CHconversion and Hyield remain stable.

Riemann problem for one-dimensional binary gas enhanced coalbed methane process

With an extended Langmuir isotherm, a Riemann problem for one-dimensional binary gas enhanced coalbed methane (ECBM) process is investigated. A new analytical solution to the Riemann problem, based on the method of characteristics, is developed by introducing a gas selectivity ratio representing the gas relative sorption affinity. The influence of gas selectivity ratio on the enhanced coalbed methane processes is identified.

Efficiency analysis of sorption-enhanced method in steam methane reforming process

The sorption-enhanced method can change the thermodynamic equilibrium by absorbing CO_(2).However,it also brings about the problems of high regeneration temperature of adsorbent and large regeneration energy consumption.In order to study the impact of enhanced adsorption methods on the overall energy cost of the system in the hydrogen production process,this paper analyzes and compares steam methane reforming and reactive adsorption-enhanced steam methane reforming with the energy consumption of hydrogen production products as the evaluation index.The results showed that the energy consumption per unit hydrogen production decreased from 276.21 MJ/kmol to 131.51 MJ/kmol,and the decomposition rate of H2O increased by more than 20%after the addition of adsorption enhancement method.It is proved that the advantage of sorption enhanced method on pre-separation of CO_(2)in the product makes up for the disadvantage of energy consumption of adsorbent regeneration.In addition,the ability of the process to obtain H element is improved by the high decomposition rate of H2O,which realizes a more rational distribution of the element.

Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models

Carbon dioxide-abated hydrogen can be synthesised via various processes,one of which is sorption enhanced steam methane reforming(SE-SMR),which produces separated streams of high purity H_(2) and CO_(2).Properties of hydrogen and the sorbent material hinder the ability to rapidly upscale SE-SMR,therefore the use of artificial intelligence models is useful in order to assist scale up.Advantages of a data driven soft-sensor model over ther-modynamic simulations,is the ability to obtain real time information dependent on actual process conditions.In this study,two soft sensor models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured.Both artificial neural networks and the random forest models were devel-oped as soft sensor prediction models.They were shown to provide good predictions for gas concentrations in the reformer and regenerator reactors of the SE-SMR process using temperature,pressure,steam to carbon ratio and sorbent to carbon ratio as input process features.Both models were very accurate with high R^(2) values,all above 98%.However,the random forest model was more precise in the predictions,with consistently higher R^(2) values and lower mean absolute error(0.002-0.014)compared to the neural network model(0.005-0.024).