化学链合成氨技术研究进展及展望

氨气不仅是重要的化工原料和良好的氢载体,其可以作为无碳燃料的属性也引起了广泛关注。低能耗高效率的合成氨工艺是实现氨作为燃料应用的关键。阐述了合成氨工艺的发展历程,概述了以Haber-Bosch工艺为基础的多相催化和光、电等外场力驱动的合成氨工艺的新发展,重点介绍了化学链合成氨的最新研究成果,并对其研究方向进行展望。传统Haber-Bosch工艺苛刻的反应条件以及热力学和动力学之间的矛盾,促使科研工作者一直努力探索可持续的环境友好型合成氨技术。随着催化科学和表面科学的进步,人们对合成氨的反应机理和催化剂的物化性质有了更深入的认识,这为开发“绿色”合成氨工艺提供了有价值的参考信息,如要提高过渡金属催化合成氨性能,须尽量规避表面物种吸附能间的线性关系。另外,以可再生能源为能量来源的光、电催化合成氨,借助外场作用可以有效影响反应速率和机理。化学链技术的发展为合成氨工艺提供了新思路,将合成氨过程解耦为吸氮和释氮产氨2个或多个分步反应,可较好地缓解合成氨热力学和动力学矛盾,规避反应物竞争吸附。同时,各分步反应可分别优化,使整个化学链合成氨工艺达到最佳反应效果。未来采用太阳能聚热供能以及以生物质炭为碳源,并对化学链合成氨工艺进行经济性分析反馈指导工艺流程的优化,可降低碳基化学链制氨工艺的成本和能耗。

Effects of dimethyl ether and ethanol additions on soot transition in ethylene counterflow diffusion flames

This paper investigates the effect of blending dimethyl ether(DME)and ethanol on the soot transition periods in ethylene counterflow diffusion flames by using a novel optical diagnostic method.The soot critical transition point in different conditions is identified experimentally and numerically.Two kinds of flames are carried out to gain the soot critical transition point in counterflow diffusion flames by changing oxygen fraction(Xo)and changing volume flow rates of fuel and oxidizer(Qv).The red-green-blue(RGB)ratio method is used to precisely identify the soot critical transition point,and chemical kinetic simulations are performed to analyze the detailed reaction paths.The results show that compared to the ethylene flame,the soot critical transition point occurs at a higher Xoand a lower Qvwhen DME or ethanol is blended.The addition of DME and ethanol can inhibit soot formation,due to the degree of soot formation reaction being lower than the degree of the oxidation reaction in the blending flames.

Coupled interactive effects of ammonia and hydrogen additions on ethylene diffusion flames: A detailed kinetic study

In order to create effective combustion technologies and fuels with low or no carbon emissions,the research was conducted to assess the coupled interactive effects of NH_(3) and H_(2) additions on ethylene counterflow diffusion flames from a kinetic perspective.The effects of the NH_(3)/H_(2) combination on flame temperatures,major species,key radicals,important intermediate species,representative oxygenated species and NO_xwere examined.The results of the study utilizing fictitious inert NH_(3) and/or H_(2) revealed the chemical effects of the two components.It was found that the NH_(3)/H_(2) coupled effects had a more effective inhibitory effect on soot precursors than the effects of corresponding sum of single NH_(3) or H_(2) addition.The production of soot precursors was promoted by the coupled chemical effects of NH_(3) and H_(2),but the coupled dilution and thermal effects were observed to have a greater impact,resulting in a decrease of the mole fractions of soot precursors.As for the interaction of NH_(3) and H_(2) effects,the presence of H_(2) decreased the chemical effects of NH_(3) on the augmentation of C_(2)H_(2),A1,A2,and CH_(3)CHO mole fractions.The NH_(3) addition alleviated the H_(2) chemical effects on increasing C_(2)H_(2),C_(3)H_(3),A1 and A2 concentrations.Conversely,the NH_(3)chemical effects on C_(3)H_(3),OH and CH_(3)CHO were enhanced when H_(2) was added.The presence of NH_(3) augmented the chemical effects of H_(2) on the growth of OH mole fraction.Moreover,the H_(2) chemical effects hindered the production of NO and NO_(2) in the presence of NH_(3).

Kinetic Analysis on Pyrolysis Characteristics of Butene Isomer Fuels with CO_(2) Additions

This article compared the pyrolysis characteristics of butene isomer fuels(n-butene,trans-2-butene,isobutene)under CO_(2) atmosphere.The focus of the study was on the impacts of fuels isomerization and CO_(2) chemistry on the formation of important pyrolysis intermediate products under two temperature conditions(at 1273 K and 1573 K).The results demonstrated that at 1273 K,the concentrations of all products in isobutene pyrolysis were significantly lower than those of the corresponding products formed by the pyrolysis of n-butene and trans-2-butene in the process of fuels pyrolysis.The generation or consumption rate of each species was arranged from high to low as n-butene>trans-2-butene>isobutene.At 1573 K,the chemical effect of CO_(2) could inhibit the consumption of ethylene(C_(2)H_(4)),promote the formation of diacetylene(C_(4)H_(2)),and inhibit the formation of acetylene(C_(2)H_(2))and benzene(A_(1)).During the pyrolysis of n-butene at 1573 K,the chemical effect of CO_(2) was mainly reflected in changes of the consumption paths of C_(2)H_(2) and benzyl(A_(1)CH_(2)).For trans-2-butene pyrolysis at 1573 K,CO_(2) addition could change the main consumption paths of methyl(CH_(3)),which then inhibited C_(2)H_(2) formation.In addition,CO_(2) chemical action significantly changed the consumption paths of C_(4)H_(2) and A_(1) in trans-2-butene pyrolysis at 1573 K.As for isobutene pyrolysis at 1573 K,the chemical effect of CO_(2) significantly inhibited the production of A_(1) by changing the main consumption paths of C_(2)H_(2) and propargyl(C_(3)H_(3)).

Fuel type recognition of ester isomer additives in flames by optical diagnostics coupled with machine learning method

Based on machine learning models,an approach for the type recognition of oxygenated additives(ester isomers,i.e.,methyl butyrate,methyl crotonate,ethyl acrylate,and ethyl acrylate)via optical diagnostics was proposed.By utilizing optical diagnostic methods flame features were extracted,and three models including random forest(RF),artificial neural network(ANN),and support vector machine(SVM),were employed to establish the relationship between flame images and oxygenated additives.Moreover,the impact of multiple factors on model performance,including image compression,dataset size,and feature number was also investigated.The images of flame obtained from inverse diffusion flame under four different oxygenated additives and various combustion conditions were used as examples to examine the effectiveness of the proposed approach.Results indicated that the accuracy of the recognition of ester isomers by the proposed approach exceeded 90%.Furthermore,it is observed that image compression had minimal impact on prediction accuracy but significantly reduced processing time.Different types of features contributed to predicting the type of ester isomers variously,and all models exhibited improved accuracy with an increased number of features.The number of samples significantly affected model accuracy.The investigation of feature missing and insufficient training samples suggested that ANN and RF models were more suitable for cases with many missing features,while SVM was more suitable for dealing with small samples.