The Kinetics And Coke-resistant Catalysts Design Of Steam Methane Reforming

Steam-methane reforming(SMR)is a mature traditional chemical process,which plays an important role in the field of energy and chemical synthesis.In recent decades,the energy crisis and environmental pollution problems have become increasingly se-rious.Especially,as industrial development has brought about an increase in CO2,a greenhouse effect that has led to global warming and rising temperatures,making peo-ple pay more attention to the use of clean energy.As an important technology of clean utilization of natural gas,SMR’s industrial innovation will have a profound impact on the way energy is used in China and the world.The understanding of SMR dynamics is the basis for promoting its technological development.And since the 1930s,a large number of research results have been contra-dictory,which limited the development of SMR.Based on the density functional theory,here,establishes universal SMR kinetics,and on this basis,assisted by deep learning for the design of carbon-resistant SMR catalysts.Besides,a similar method was used to study methanol steam reforming(MeSR),the dominant technology of onboard fuel cell.This thesis is divided into 6 chapters:The first chapter introduces SMR’s wide range of industrial applications and re-search background.Chapter 2,based on the density functional theory(DFT),the SMR elementary re-action network is established.According to this,SMR micro-kinetics research is carried out.Under the wide-range conditions which cross solid oxide fuel cells and industrial hydrogen production,the modeling is highly consistent with the experiments.And a highly universal SMR micro-kinetic model is obtained.Chapter 3,based on micro-kinetics,the kinetic behavior of SMR under a large number of experimental conditions is studied.And after strict induction and deriva-tion,the analytical SMR macro rate equation is obtained.Through the analysis of the properties of the equation,the contradictions of the reaction orders of CH4,H2O and activation energy in the current experiments are explained.And the overall physical image of SMR kinetics changing with conditions is given,the experimental results that seem to contradict each other are unified.In chapter 4,using a similar method to studying SMR,the kinetics of MeSR on Ni is studied,and the rate equation of MeSR is also derived.Besides,it is found in the study that the dehydrogenation activation energy of CHxO-H is significantly reduced by the co-adsorbed OH*,so at the end of this chapter,the catalytic effect of hydrogen bonds of OH*on CHxO-H dehydrogenation is discussed.Chapter 5,on the basis of in-depth understanding of SMR dynamics,a complete catalyst design method assisted by deep learning is proposed.Firstly,based on the un-derstanding of SMR kinetics,the verified SMR activity descriptor is found according to the linear relationship in heterogeneous catalysis.Then,the active descriptors are pre-dicted by deep learning method,and some highly active catalyst are obtained.Finally,the catalyst is screened according to the requirements.This method is applied to the search of carbon-resistant SMR catalysts.The adsorption energies of C*and O*as the descriptor of the catalytic activity of SMR and is predicted by the deep neuron network after training.As results,after several steps of screening,11 kinds of high-activity、low-price、environment friendless and stable experimental alloys were obtained.And in the last chapter,the summary and prospect was carried out.