A composite absorption liquid for simultaneous desulfurization and denitrification in flue gas

A composite-liquid absorbent(CLA),NaClO/KMnO4,for simultaneous desulfurization and denitrification(SDD)was studied in a homemade bubbling reactor.The experimental results showed that the CLA configured by sodium hypochlorite(NaClO)and potassium permanganate(KMnO4)had a very good synergistic effect on SDD.The effects of NaClO concentration(CNa),KMnO4 concentration(CK),gas space velocity(Vg),initial p H value,and temperature of the absorption liquid(Ts)on efficiencies of the SDD were investigated.Under the optimal reaction conditions,the best removal efficiencies were 100%for sulfur dioxide(SO2)and above 94%for nitric oxide(NO).The ion chromatography and titration were used to analyze the changes of both the ion species and concentrations in the liquid before and after the reaction.According to the experiment results and related literature,the reaction mechanism of the SDD based on the CLA was proposed.

Application of machine learning to process simulation of n-pentane cracking to produce ethylene and propene

Modeling light olefin production was one of the main concerns in chemical engineering field.In this paper,machine learning model based on artificial neural networks(ANN)was established to describe the effects of temperature and catalyst on ethylene and propene formation in n-pentane cracking.The establishment procedure included data pretreatment,model design,training process and testing process,and the mean square error(MSE)and regression coefficient(R2)indexes were employed to evaluate model performance.It was found that the learning algorithm and ANN topology affected the calculation accuracy.GD24223,CGB2423,and LM24223 models were established by optimally matching the learning algorithm with ANN topology,and achieved excellent calculation accuracy.Furthermore,the stability of GD24223,CGB2423 and LM24223 models was investigated by gradually decreasing training data and simultaneously transforming data distribution.Compared with GD24223 and LM24223 models,CGB2423 model was more stable against the variations of training data,and the MSE values were always maintained at the magnitude of 10^-3-10^-4,confirming its applicability for simulating light olefin production in n-pentane cracking.

Fabrication and catalytic performance of meso-ZSM-5 zeolite encapsulated ferric oxide nanoparticles for phenol hydroxylation

An encapsulation-structured Fe_(2)O_(3)@mesoZSM-5(Fe@MZ5)was fabricated by confining Fe_(2)O_(3) nanoparticles(ca.4 nm)within the ordered mesopores of hierarchical ZSM-5 zeolite(meso-ZSM-5),with ferric oleate and amphiphilic organosilane as the iron source and meso-porogen,respectively.For comparison,catalysts with Fe_(2)O_(3)(ca.12 nm)encapsulated in intra-crystal holes of meso-ZSM-5 and with MCM-41 or ZSM-5 phase as the shell were also prepared via sequential desilication and recrystallization at different pH values and temperatures.Catalytic phenol hydroxylation performance of the as-prepared catalysts using H_(2)O_(2) as oxidant was compared.Among the encapsulation-structured catalysts,Fe@MZ5 showed the highest phenol conversion and hydroquinone selectivity,which were enhanced by two times compared to the Fe-oxide impregnated ZSM-5(Fe/Z5).Moreover,the Fe-leaching amount of Fe@MZ5 was only 3% of that for Fe/Z5.The influence of reaction parameters,reusability,and ·OH scavenging ability of the catalysts were also investigated.Based on the above results,the structure-performance relationship of these new catalysts was preliminarily described.