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.

Reform in the Property Right System to Enhance Competitiveness—On the China Yangtze Group Corp.

The China Yangtze Group Corp. was founded in 1980. Now it has developed into a large state-owned consortium, with its business scope covering electrical appliances, automobiles, light industry and other industrial sectors. It has gross assets of RMB 1.737 billion, and net assets of RMB359 million. The Yangtze Group has been developing, with reliance on bank loans in recent years, incurring heavy debts; and its leading product-refrigerator,

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).