Strategy of changing cracking furnace feedstock based on improved group search optimization

Nian, Xiaoyu; Wang, Zhenlei; Qian, Feng

doi:10.1016/j.cjche.2014.09.027

Cited by 8 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to refs ,, and there are more works that propose new optimization algorithms to improve the operation of cracking severity or product yields based on static and dynamic models. Nian et al , focused on the changes in cracking furnace feedstock in an ethylene plant. They used an improved group search optimization (GSO) algorithm to obtain optimal operational conditions of the furnace.…”

Section: Optimal Designmentioning

confidence: 99%

Thermal Cracking of Hydrocarbons for the Production of Light Olefins; A Review on Optimal Process Design, Operation, and Control

Fakhroleslam

Sadrameli

2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Olefins production plants are large-scale plants, in most of which gaseous and liquid hydrocarbons are cracked to produce light olefins. The complex and large-scale nature of these plants makes it an utmost necessity to design and operate them with the use of computer-aided optimization and control methods. This review paper provides an overview of the reported research works on the optimization and control of different parts of olefin plants. The main research studies are discussed in two main sections: Optimal Design and Process Operation and Control. In the Optimal Design section, the state of the optimal design of cracking furnace systems, cold-end separation systems, and separation columns have been studied. Then in the Process Operation and Control section, the control of cracking furnaces, the control of cold-end separation systems, real-time optimization of olefin plants, cyclic scheduling of cracking furnace systems, production planning of olefin plants, and finally, start-up and shutdown operations in these plants have been extensively reviewed. This paper is a continuation of the three review articles published previously entitled Thermal and Catalytic Cracking of Hydrocarbons for the Production of Light Olefins, the last of which focused on process modeling and simulation.

show abstract

Section: Optimal Designmentioning

confidence: 99%

Thermal Cracking of Hydrocarbons for the Production of Light Olefins; A Review on Optimal Process Design, Operation, and Control

Fakhroleslam

Sadrameli

2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Various studies have been conducted from simulation, lab experiments and pilot scale plants that looking at various process parameters to optimize the olefins yield such as ethylene from the furnace. The specific control variables such as Coil Outlet Temperature (COT) (Song & Tang, 2018;Yu et al, 2018), Dilution Steam ratio (Berreni & Wang, 2011;Geng et al, 2016), naphtha feedstock (Geng et al, 2016;Nian et al, 2015) and fuel gas consumption (Yu et al, 2018) were normally studied to optimize the olefins yield. However, there is also an opportunity to explore other variables in the steam cracker furnace which are not widely discussed such as Arch O 2 , Steam Drum, and Draft Pressure to see their relations towards the olefins yield.…”

Section: Introductionmentioning

confidence: 99%

Ethylene Yield from a Large Scale Naphtha Pyrolysis Cracking Utilizing Response Surface Methodology

Zakria¹,

Nawawi²,

Rahman³

2021

JST

View full text Add to dashboard Cite

Statistical software is a robust application that has proven reliable worldwide. However, it is not normally used in the actual large scale olefin plant as it relies on the simulation software by Olefin Licensor should any issue rises. The study was conducted in a newly commissioned large scale olefin plant to see the impact of various operating variables on the ethylene yield from Short Residence Time (SRT) VII Furnace. The analysis was conducted utilizing statistical analysis, Response Surface Methodology (RSM) in Minitab Software Version 18 to develop a reliable statistical model with a 95% confidence level. The historical data was taken from the Process Information Management System (PIMS) Software, PI Process Book Version 2015, and underwent both residuals and outliers removal prior to RSM analysis. 10 variables were shortlisted from the initial 15 identified variables in the studied SRT VII via Regression analysis due to RSM limitation to conduct the larger analysis in Minitab Software Version 18. The Response Optimizer tool showed that the ethylene yield from naphtha pyrolysis cracking in the studied plant could be maximized at 34.1% with control setting at 600.39 kg/ hr of Integral Burner Flow, 6.81% of Arch O2, 113.42 Barg of Steam Drum Pressure, 496.96°C of Super High Pressure (SHP) Temperature, 109.11 t/hr of SHP Boiler Feed Water (BFW) Flow, 92.78 t/hr of SHP Flow, 63.50 t/hr of Naphtha Feed Flow, and -13.38 mmHg of Draft Pressure.

show abstract

Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization

While

Reynolds

et al. 2018

Energy

View full text Add to dashboard Cite

Strategy of changing cracking furnace feedstock based on improved group search optimization

Cited by 8 publications

References 17 publications

Thermal Cracking of Hydrocarbons for the Production of Light Olefins; A Review on Optimal Process Design, Operation, and Control

Thermal Cracking of Hydrocarbons for the Production of Light Olefins; A Review on Optimal Process Design, Operation, and Control

Ethylene Yield from a Large Scale Naphtha Pyrolysis Cracking Utilizing Response Surface Methodology

Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization

Contact Info

Product

Resources

About