Modelling and simulation of naphtha cracker

Parmar, Kanubhai K.; Padmavathi, G.; Dash, Sukanta Kumar

doi:10.1080/00194506.2018.1529633

Cited by 5 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The kinetic models for cracking C 5 + hydrocarbons or complex feeds like naphtha ,,− usually consist of an overall primary reaction of the feed cracking into C 2 –C 4 hydrocarbons and secondary reactions among the products of the primary reaction. The model proposed by Kumar and Kunzru is popular in modeling the decomposition of heavy hydrocarbon feeds where the secondary pyrolytic reactions are almost independent of feedstocks.…”

Section: Introductionmentioning

confidence: 99%

Revisiting Reaction Network Modeling of Thermal Cracking of Hydrocarbons

Mehta,

Gaikar

2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Thermal cracking of hydrocarbons is an industrially important process to produce products with better economic value. Although substantial literature exists on the thermal cracking of hydrocarbons, there is considerable disparity in the proposed mechanisms and kinetic parameters. A better understanding of the complex cracking reactions network becomes important to maximize the returns by producing the products in demand, mostly C 2 −C 4 olefins. In this work, we propose an improved molecular reaction scheme(s) for the thermal cracking of C 2 −C 7 linear hydrocarbons and establish a set of improved and unified kinetic parameters. Starting with a reaction network scheme for ethane cracking, the kinetic model and parameters are extended up to heptane cracking. The model accuracy was improved in a stepwise manner by using multiple data sets of ethane, propane, and butane cracking. The improved reaction scheme and unified kinetic parameters fit the major product profiles of cracking of individual light alkanes, from ethane to heptane, as well as mixtures of these hydrocarbons within a 10% relative error, and have the potential of representing kinetics of reactions involving a complex feed like naphtha. The molecular reaction mechanism is validated against industrial data available in the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

Revisiting Reaction Network Modeling of Thermal Cracking of Hydrocarbons

Mehta,

Gaikar

2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…The modeling of hydrocarbon pyrolysis could be satisfactorily completed by rigorous calculation of kinetic and thermodynamic parameters, which characterizes many possible reactions. Parmar et al (2019) established a complete model including mass balance, energy balance, and momentum balance to simulate a naphtha cracker. The kinetic model reaction network used in this work was an extension of the classic Kumar model (Kumar and Kunzru, 1985), which involved one primary reaction and 21 secondary reactions.…”

Section: Introductionmentioning

confidence: 99%

Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN

Ren

Liao²,

Yang³

et al. 2022

Front. Chem. Eng.

View full text Add to dashboard Cite

Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model.

show abstract