Production of high-purity hydrogen from paper recycling black liquor via sorption enhanced steam reforming

Environmentally friendly and energy saving treatment of black liquor(BL),a massively produced waste in Kraft papermaking process,still remains a big challenge.Here,by adopting a NieCaOeCa_(12)Al_(14)O_(33) bifunctional catalyst derived from hydrotalcite-like materials,we demonstrate the feasibility of producing high-purity H_(2)(~96%)with 0.9 mol H_(2) mol^(-1) C yield via the sorption enhanced steam reforming(SESR)of BL.The SESRBL performance in terms of H_(2) production maintained stable for 5 cycles,but declined from the 6th cycle.XRD,Raman spectroscopy,elemental analysis and energy dispersive techniques were employed to rationalize the deactivation of the catalyst.It was revealed that gradual sintering and agglomeration of Ni and CaO and associated coking played important roles in catalyst deactivation and performance degradation of SESRBL,while deposition of Na and K from the BL might also be responsible for the declined performance.On the other hand,it was demonstrated that the SESRBL process could effectively reduce the emission of sulfur species by storing it as CaSO_(3).Our results highlight a promising alternative for BL treatment and H_(2) production,thereby being beneficial for pollution control and environment governance in the context of mitigation of climate change.

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.

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.

Sorption enhanced catalytic CF4 hydrolysis with a three-stage catalyst-adsorbent reactor

In this study, we developed a three-stage catalyst-adsorbent reactor for the catalytic hydrolysis of CF4. Each stage is composed of a catalyst bed followed by an adsorbent bed using Ca（OH）2 to remove HF. The three stages are connected in series to enhance the hydrolysis of CF4 and eliminate a scrubber to dissolve HF in water at the same time. With a 10 wt-% Ce/AlaO3 catalyst prepared by the incipient wetness method using boehmite and a granular calcium hydroxide as an adsorbent, the CF4 conversion in our proposed reactor was 7%-23% higher than that in a conventional single-bed catalytic reactor in the temperature range of 923-1023 K. In addition, experimental and numerical simulation （Aspen HYSYS） results showed a reasonable trend of increased CF4 conversion with the adsorbent added and these results can be used as a useful design guideline for our newly proposed multistage reactor system.

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).

Removal of trivalent samarium from aqueous solutions by activated biochar derived from cactus fibres

The efficiency of activated biochar fibres obtained from Opuntia Ficus lndica regarding me sorpuon oi trlvalent samarium （Sm（Ⅲ）） from aqueous solutions was investigated by batch experiments. The effect of various physicochemical parameters （e.g. pH, initial metal concentration, ionic strength, temperature and contact time） on the Sm（III） adsorption was studied and the surface species were characterized by FTIR spectroscopy prior to and after the lanthanide sorption. The experimental results showed that the acti- vated biochar fibres possessed extraordinary sorption capacity for Sm（Ⅲ） in acidic solutions （qmax=90 g/kg, pH 3.0） and near neutral solutions （qmax=350 g/kg, pH 6.5）, This was attributed to the formation of samarium complexes with the surface carboxylic moieties, available in high density on the lamellar structures of the bio-sorbent.