Simulation And Optimization Of Molecular-Level Naphtha Steam Cracking Reaction Process

With the concept of Industry 4.0,intelligent manufacturing technology has gradually penetrated into various industrial fields.Advanced digital technologies,such as big data,internet of things,and artificial intelligence,help the transformation and upgrading of traditional petrochemical industries.Light olefins are important chemical raw materials.Steam cracking of naphtha is an important chemical process for the production of light olefins,and it is also an operating unit with huge energy consumption.Traditional extensive and empirical cracking process simulation can no longer meet the needs of the current high-quality economic development.It is urgent to establish accurate and molecular-level process simulation and optimization model for naphtha steam cracking process,in order to fit the global development direction of "molecular refining",and conform to the trend of localization of process modeling software.At the same time,in recent years,the market of petrochemical vehicle fuel has been impacted by new energy such as electricity and hydrogen.The phenomenon of excess refining capacity in China is obvious,and traditional refineries with ten million tons have gradually transformed into "refining and chemical integration".Therefore,it is of great significance to develop new pyrolysis process with better atomic economy and higher yield of light olefins.The new technology of low temperature initiated cracking of hydrocarbons has significant development potential.Based on the pyrolysis mechanism of hydrocarbons,the chain reaction can be initiated in advance by adding initiator which is easier to generate free radicals at low temperature.However,the existing common small molecule additives have the disadvantages of large amount and high cost,while the new hyperbranched polymer additives only need a small amount to achieve high-quality initiation effect At present,the laws of additives in low-temperature initiated cracking to increase light olefins are not yet completely clear,especially the initiation mechanism of polymer additives.This paper focuses on the simulation and optimization of molecular-level naphtha steam cracking reaction process.The molecular reconstruction modeling of naphtha composition,steam cracking kinetic modeling and artificial neural network modeling were carried out,and the role of additives in low-temperature initiation of hydrocarbon cracking to olefins was further explored.The main conclusions are as follows:(1)A molecular reconstruction model of naphtha feedstock based on common bulk properties is established.The model uses a deterministic molecular library in which 258 hydrocarbon molecules in the range of C4-C12 are aggregated into 35 lumped components according to the carbon number and homologues series.Gamma distribution against both the carbon number and the boiling point have been implemented in the model.The differences of carbon number and boiling point used in gamma distribution and the combination of bulk physical properties as constraints or objective functions were compared.50 groups of naphtha samples in literature were used to test the modeling strategies.The results show that the most accurate modeling method is to follow gamma distribution against carbon number,place the PIONA data in constraints,and place other bulk properties in the objective function.Validation results show that the optimal modeling strategy can accurately predict the detailed composition of naphtha only depending on the common bulk properties.(2)A detailed mechanistic model of naphtha steam cracking is constructed with the help of an open-source automatic reaction network generator RMG.In order to obtain the complete reaction network of naphtha mixture,a step-by-step construction methodology to start with automatic reaction network generation for a single hydrocarbon and then merge reaction networks of all considered hydrocarbons according to certain merging rules is proposed.A total of 60 hydrocarbons are selected as reactants to describe naphtha feedstock.The final merged model contains 1947 species and 82130 reactions.The pyrolysis of n-decane in the literature is used to verify the accuracy of the generated reaction network for single hydrocarbon.The simulated results of n-decane and major products are in overall good agreement with the experimental values.The detailed mechanistic model of the naphtha mixture is verified by a set of naphtha steam cracking experiments.The results show that the generated reaction networks predict the major product yields and their changing trends under the normal cracking temperature with accuracy,and the deviations of minor products are within acceptable ranges.(3)Based on the reactor simulation data obtained by the above mechanism model,an artificial neural network model of the naphtha steam cracking process is constructed.The model is composed of two sub-networks:the feed composition ANN and the reactor ANN.The whole model could directly predict the yields of cracking products from the bulk properties of the naphtha feedstock.In the first part of the feed composition ANN,two different ANN construction methods were compared:directly predicting the detailed composition from bulk properties of naphtha;indirectly predicting the respective content distribution of PIONA first,and then obtain the total detailed composition.The results show that the indirect method has higher prediction accuracy.In the second part of the reactor ANN,the MAE of 11 cracking products is 0.24wt%.Combining the two parts of the network,i.e.,directly predicts the product distribution from the bulk properties,for the direct method and the indirect method,the MAE of the product is 0.53 wt%and 1.02wt%,respectively.The verification shows that the fundamental reason of lower product prediction MAE of the indirect method is that the indirect ANN ensures that PIONA is equal to the actual value,not just the reduction of the total error of the feed composition.Subsequently,based on the ANN surrogate model,the process optimization of naphtha steam cracking coupled with feed ratio of naphtha is carried out with ethylene,propylene and methane as the optimization objectives.The simultaneous optimization of feed composition and operating conditions was realized.(4)The representative molecules of three types of common small molecule additives are selected,and the co-cracking reaction network of each additive with n-hexane is obtained through automatic reaction network generation.The initiating cracking performance of the additives is compared and analyzed through reactor simulation.The results show that additives can obviously initiate the cracking of hydrocarbons at low temperature,and the initiation temperature is related to the cracking temperature of the additive itself.The lower the cracking temperature of the additive,the more obvious the decrease in the cracking temperature of n-hexane.Both triethylamine and nitromethane can increase the selectivity of n-hexane to ethylene and propylene.Nitromethane is more significant,while DTBP is almost ineffective.By investigating the factors that affect the initial cracking rate,it is shown that the performance of additives is closely related to the reaction temperature and the concentration of free radicals.DTBP is almost invalid because it has been completely cracked at too low temperature.Nitromethane is fully cracked near the outlet of the preheating section.At this temperature,the rate constants of the hydrogen abstraction reaction of n-hexane and the reaction of ethylene generation have reached a high level,and the released free radicals can be effective.Due to the excessively high cracking temperature of triethylamine,the thermal cracking of the hydrocarbon itself has occurred,and the low temperature initiation effect is weakened.By calculating the free radical conversion ratio of the additives,it is found that nitromethane has a higher free radical conversion ratio than triethylamine,indicating that nitromethane releases a higher concentration of free radicals,which is one of the reasons why nitromethane shows a better promoting effect.Finally,we propose three criteria for the selection and design of additives:appropriate cracking temperature,efficient free radical conversion,and large hydrogen ion reaction rate constant of free radical.(5)The co-cracking reaction network of hyperbranched polymer additives and hydrocarbon is constructed through automatic reaction network generation technology.The representative fragments and model compounds that can reflect the connection mode and end group characteristics of the original polymer central structure are selected as the reactive species.The potential reaction paths of the fragments of polymer additives are obtained from automatic reaction mechanism generation analysis.The fragments pyrolyze continuously,releasing·H,·NH2 and·CH3 free radicals,and finally forming small molecular products.These substances were also observed in the PY-GC/MS thermal cracking experiment of the additives.Though analyzing the reaction process of the additives attacking n-hexane,the mechanism of hyperbranched polymer additives can be explained as follows:after polymer additive is added,it releases active free radicals·H,·CH3,and also introduces new·NH2;three active free radicals attack n-hexane,and the hydrogen abstraction reaction overtakes the C-C scission reaction to become the main initiation reaction,thus changing the reaction network of n-hexane;·NH2 has a strong ability to abstract secondary hydrogen atoms to generate secondary carbon radicals,which have high propylene selectivity.