The Experts below are selected from a list of 2097 Experts worldwide ranked by ideXlab platform

Zhenlei Wang - One of the best experts on this subject based on the ideXlab platform.

  • multiobjective optimization of ethylene Cracking Furnace system using self adaptive multiobjective teaching learning based optimization
    Energy, 2018
    Co-Authors: Lyndon While, Liang Zhao, Mark Reynolds, Xin Wang, J J Liang, Zhenlei Wang
    Abstract:

    The ethylene Cracking Furnace system is crucial for an olefin plant. Multiple Cracking Furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these Furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial Cracking Furnace system that describes the operation of each Furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions.

  • Cyclic scheduling for an ethylene Cracking Furnace system using diversity learning teaching-learning-based optimization
    Computers & Chemical Engineering, 2017
    Co-Authors: Lyndon While, Mark Reynolds, Xin Wang, Zhenlei Wang
    Abstract:

    Abstract The ethylene Cracking Furnace system is central to an olefin plant. Multiple Cracking Furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a Cracking Furnace system, with consideration of different feeds, multiple Cracking Furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the Cracking Furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different Furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.

  • multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene Cracking Furnace optimization
    Knowledge Based Systems, 2016
    Co-Authors: Xin Wang, Zhenlei Wang
    Abstract:

    A new variant of PSO, abbreviated as MLPSO-STP, is proposed.A novel learning strategy is used to enhance the global search ability.Space transformation perturbation is used to obtain better solutions.MLPSO-STP outperforms its peers in terms of searching accuracy and reliability.MLPSO-STP is used to optimize the operating conditions of ethylene Cracking Furnace. This paper proposes a new variant of particle swarm optimization (PSO), namely, multiple learning PSO with space transformation perturbation (MLPSO-STP), to improve the performance of PSO. The proposed MLPSO-STP uses a novel learning strategy and STP. The novel learning strategy allows each particle to learn from the average information on the personal historical best position (pbest) of all particles and from the information on multiple best positions that are randomly chosen from the top 100p% of pbest. This learning strategy enables the preservation of swarm diversity to prevent premature convergence. Meanwhile, STP increases the chance to find optimal solutions. The performance of MLPSO-STP is comprehensively evaluated in 21 unimodal and multimodal benchmark functions with or without rotation. Compared with eight popular PSO variants and seven state-of-the-art metaheuristic search algorithms, MLPSO-STP performs more competitively on the majority of the benchmark functions. Finally, MLPSO-STP shows satisfactory performance in optimizing the operating conditions of an ethylene Cracking Furnace to improve the yields of ethylene and propylene.

  • self adaptive multi objective teaching learning based optimization and its application in ethylene Cracking Furnace operation optimization
    Chemometrics and Intelligent Laboratory Systems, 2015
    Co-Authors: Xin Wang, Zhenlei Wang
    Abstract:

    Abstract A self-adaptive multi-objective teaching-learning-based optimization (SA-MTLBO) is proposed in this paper. In SA-MTLBO, the learners can self-adaptively select the modes of learning according to their levels of knowledge in classroom. The excellent learners are more likely to choose the learner phase to enhance population diversity, and the common learners are tend to choose the teacher phase to improve the convergence ability of the algorithm. So learners at different levels choose appropriate modes of learning and carry out corresponding search function to efficiently enhance the performance of algorithm. To evaluate the effectiveness of the proposed algorithm, SA-MTLBO is firstly compared with other algorithms in twelve test problems. The results demonstrate that SA-MTLBO can generate Pareto optimal fronts with good convergence and distribution. Finally, SA-MTLBO is used to maximize the yields of ethylene, propylene, and butadiene of the naphtha pyrolysis process. The computational results of SA-MTLBO indicate that the operation of ethylene Cracking Furnace can be improved by increasing the yields of ethylene, propylene, and butadiene.

  • Strategy of changing Cracking Furnace feedstock based on improved group search optimization
    Chinese Journal of Chemical Engineering, 2015
    Co-Authors: Xiaoyu Nian, Zhenlei Wang, Feng Qian
    Abstract:

    Abstract The scheduling process of Cracking Furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is proposed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the “excellent” infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Finally, the proposed algorithm is used to optimize the scheduling process of Cracking Furnace feedstock. And the optimizing result is obtained.

Feng Qian - One of the best experts on this subject based on the ideXlab platform.

  • Data-driven Scheduling Optimization of Ethylene Cracking Furnace System
    2020 Chinese Control And Decision Conference (CCDC), 2020
    Co-Authors: Liang Zhao, Wenli Du, Feng Qian
    Abstract:

    The ethylene Cracking Furnace system convert a variety of hydrocarbon feeds into products such as ethylene and propylene in parallel. During the Cracking process, the coke produced by the Cracking will degrade the performance of the Cracking Furnace, so the Furnace should be cleaned periodically. Scheduling of the Cracking Furnace system involves various raw materials, multiple Furnaces, and multiple constraints, so it is a difficult mathematic programming problem. Previous studies have simplified the optimization model to obtain a corresponding mixed integer nonlinear programming (MINLP) model. In order to make the scheduling more suitable for the actual process, this paper develops the transfer-line exchanger (TLE) model, fuel consumption model, and super-high pressure steam (SS) model by using the actual plant data. A case study is put forward to demonstrate the effectiveness of the proposed method.

  • Analytical models for heat transfer in the tube bundle of convection section in a steam Cracking Furnace
    Applied Thermal Engineering, 2019
    Co-Authors: Benfeng Yuan, Guihua Hu, Weimin Zhong, Feng Qian
    Abstract:

    Abstract In steam Cracking Furnaces, about 45% of the heat released from fuel combustion is recovered by preheating Cracking feedstock and high pressure steam. This is achieved mainly by indirect convective heat transfer from flue gas to feedstock and steam in a series of tube bundles located in the so-called convection section. Radiation also plays in an important role in lower bundles where flue gas temperature is high. Thus, accurate heat transfer simulation of the convection section is crucial for operation optimization and control of steam Cracking Furnace. This paper aims to develop a heat transfer analysis (HTA) model for combined convective and radiative heat transfer calculation in tube bundles with both inline and staggered layouts. Computational fluid dynamics (CFD) was employed to validate the HTA model in the first stage. Based on the HTA model, coupled steady-state simulations of the convection section were performed for an industrial steam Cracking Furnace with naphtha feed capacity of 30 t/h. The predictions of the HTA model are in good agreement with the design data. The study shows that convective heat transfer is significantly enhance by fins in the tube bundles where the flue gas temperatures are too low to effectively heat up the feedstock. Radiative heat transfer at the bottom of the convection chamber is much larger than the convective heat transfer and thus cannot be ignored in heat transfer calculation.

  • coupled simulation of convection section with dual stage steam feed mixing of an industrial ethylene Cracking Furnace
    Chemical Engineering Journal, 2016
    Co-Authors: Guihua Hu, Benfeng Yuan, Wenlin Du, Jinlong Li, Liang Zhang, Feng Qian
    Abstract:

    Abstract A complete coupled simulation of the convection chamber and tubes with dual stage steam feed mixing of an industrial ethylene Cracking Furnace has been carried out with the computational fluid dynamics (CFD) method for the first time. In the convection chamber, the standard k – e model and discrete ordinates (DO) radiation model were respectively used in the descriptions of turbulence characteristics and radiative heat transfer. In the tubes, renormalization group (RNG) k – e model and volume of fluid (VOF) model were respectively applied to the turbulence flow and the liquid–vapor two phases flow. Simulation results agree well with the industrial data. Based on the coupled result, a dynamic simulation was calculated in the feedstock preheater (FPH). Simulation results show that the velocity and temperature fields are inhomogeneous distributions along the width direction due to the asymmetrical structure of convection chamber. Two recirculation zones occur at the corner both near and away from the entrance to the convection chamber, which will cause a longer residence time of flue gas and local overheating in Furnace wall of convection chamber. The process gas temperature, tube skin temperature and heat flux profiles are respectively different along the axial and radial direction of the high temperature coil (HTC-I). The changes of flow pattern from bubble flow to spray flow are effected by gravity and centrifugal force during evaporation. The results will be helpful for the design and operation in Cracking Furnace.

  • Strategy of changing Cracking Furnace feedstock based on improved group search optimization
    Chinese Journal of Chemical Engineering, 2015
    Co-Authors: Xiaoyu Nian, Zhenlei Wang, Feng Qian
    Abstract:

    Abstract The scheduling process of Cracking Furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is proposed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the “excellent” infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Finally, the proposed algorithm is used to optimize the scheduling process of Cracking Furnace feedstock. And the optimizing result is obtained.

  • a hybrid algorithm based on differential evolution and group search optimization and its application on ethylene Cracking Furnace
    Chinese Journal of Chemical Engineering, 2013
    Co-Authors: Xiaoyu Nian, Zhenlei Wang, Feng Qian
    Abstract:

    Abstract To find the optimal operational condition when the properties of feedstock changes in the Cracking Furnace online, a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed, which is based on the differential evolution (DE) and the group search optimization (GSO). The DEGSO combines the advantages of the two algorithms: the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum. A cooperative method is also proposed for switching between these two algorithms. If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold, it is considered to fall into the local optimization and the other algorithm is chosen. Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy, global searching ability and efficiency. The optimization of ethylene and propylene yields is illustrated as a case by DEGSO. After optimization, the yield of ethylene and propylene is increased remarkably, which provides the proper operational condition of the ethylene Cracking Furnace.

Xin Wang - One of the best experts on this subject based on the ideXlab platform.

  • multiobjective optimization of ethylene Cracking Furnace system using self adaptive multiobjective teaching learning based optimization
    Energy, 2018
    Co-Authors: Lyndon While, Liang Zhao, Mark Reynolds, Xin Wang, J J Liang, Zhenlei Wang
    Abstract:

    The ethylene Cracking Furnace system is crucial for an olefin plant. Multiple Cracking Furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these Furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial Cracking Furnace system that describes the operation of each Furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions.

  • Cyclic scheduling for an ethylene Cracking Furnace system using diversity learning teaching-learning-based optimization
    Computers & Chemical Engineering, 2017
    Co-Authors: Lyndon While, Mark Reynolds, Xin Wang, Zhenlei Wang
    Abstract:

    Abstract The ethylene Cracking Furnace system is central to an olefin plant. Multiple Cracking Furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a Cracking Furnace system, with consideration of different feeds, multiple Cracking Furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the Cracking Furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different Furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.

  • multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene Cracking Furnace optimization
    Knowledge Based Systems, 2016
    Co-Authors: Xin Wang, Zhenlei Wang
    Abstract:

    A new variant of PSO, abbreviated as MLPSO-STP, is proposed.A novel learning strategy is used to enhance the global search ability.Space transformation perturbation is used to obtain better solutions.MLPSO-STP outperforms its peers in terms of searching accuracy and reliability.MLPSO-STP is used to optimize the operating conditions of ethylene Cracking Furnace. This paper proposes a new variant of particle swarm optimization (PSO), namely, multiple learning PSO with space transformation perturbation (MLPSO-STP), to improve the performance of PSO. The proposed MLPSO-STP uses a novel learning strategy and STP. The novel learning strategy allows each particle to learn from the average information on the personal historical best position (pbest) of all particles and from the information on multiple best positions that are randomly chosen from the top 100p% of pbest. This learning strategy enables the preservation of swarm diversity to prevent premature convergence. Meanwhile, STP increases the chance to find optimal solutions. The performance of MLPSO-STP is comprehensively evaluated in 21 unimodal and multimodal benchmark functions with or without rotation. Compared with eight popular PSO variants and seven state-of-the-art metaheuristic search algorithms, MLPSO-STP performs more competitively on the majority of the benchmark functions. Finally, MLPSO-STP shows satisfactory performance in optimizing the operating conditions of an ethylene Cracking Furnace to improve the yields of ethylene and propylene.

  • self adaptive multi objective teaching learning based optimization and its application in ethylene Cracking Furnace operation optimization
    Chemometrics and Intelligent Laboratory Systems, 2015
    Co-Authors: Xin Wang, Zhenlei Wang
    Abstract:

    Abstract A self-adaptive multi-objective teaching-learning-based optimization (SA-MTLBO) is proposed in this paper. In SA-MTLBO, the learners can self-adaptively select the modes of learning according to their levels of knowledge in classroom. The excellent learners are more likely to choose the learner phase to enhance population diversity, and the common learners are tend to choose the teacher phase to improve the convergence ability of the algorithm. So learners at different levels choose appropriate modes of learning and carry out corresponding search function to efficiently enhance the performance of algorithm. To evaluate the effectiveness of the proposed algorithm, SA-MTLBO is firstly compared with other algorithms in twelve test problems. The results demonstrate that SA-MTLBO can generate Pareto optimal fronts with good convergence and distribution. Finally, SA-MTLBO is used to maximize the yields of ethylene, propylene, and butadiene of the naphtha pyrolysis process. The computational results of SA-MTLBO indicate that the operation of ethylene Cracking Furnace can be improved by increasing the yields of ethylene, propylene, and butadiene.

Sinopec Shanghai - One of the best experts on this subject based on the ideXlab platform.

  • Quality Control for Super-high-pressure Steam in Cracking Furnace
    Technology-Economics in Petrochemicals, 2010
    Co-Authors: Sinopec Shanghai
    Abstract:

    The quality of super - high - pressure steam and boiler water in Cracking Furnace is of great importance for the stable operation of steam turbine.After the capacity - expansion reconstruction of the SRT-Ⅲtype Cracking Furnace,as long as the liquid level of steam manifold and the related indexes were well controlled in daily production,the quality of steam and boiler liquid could be controlled well even though the steam manifold was not reconstructed.The controlling indexes of steam and commonly used controlling methods for quality of steam and boiler water were elaborated.

  • Effects of Internal Structure of Steam Manifold in Cracking Furnace on the Quality of Steam
    Technology-Economics in Petrochemicals, 2010
    Co-Authors: Sinopec Shanghai
    Abstract:

    After capacity expanding transformation of SRT-III Cracking Furnace in the ethylene plant of SINOPEC Shanghai Petrochemical Co.,Ltd.,the driving segment of blading for key compressors was forced to stop for overhaul due to serious salt deposits.This paper analyzed the accident in details,and pointed out that after transformation of Cracking Furnace,the steam generated in single Cracking Furnace was increased by 50%,but the modification of local structure in steam and water separating system in the steam manifold fell flat,which caused steam entrained with liquid,and affected the quality of steam produced by the Cracking Furnace.Measures for improvement were raised in aspects of process and equipment respectively.

  • Industrial Application of Air Preheater Set Additionally for Inflamer of Ethylene Cracking Furnace
    Technology-Economics in Petrochemicals, 2009
    Co-Authors: Sinopec Shanghai
    Abstract:

    Ethylene Cracking Furnace is an important energy consuming facility in ethylene plant,with the fuel consumption accounting for about 70%of the ethylene plant's comprehensive energy consumption.The air preheater set additionally for the inflamer at bottom of Cracking Furnace provides space for thermal exchange of the condensate liquid and the normal temperature air blowing in Furnace chamber,so as to improve the temperature of combustion supporting air entering in the Furnace chamber with the residue heat of medium-pressure and low - pressure condensate liquid,which not only reduces the fuel consumption for heating the combustion supporting air in Cracking Furnace,but also decreases the dose of circulating cooling water so that fulfill the aim of energy conservation and consumption reduction.

  • Development Ideas and Primary Scheme of Large Capacity and Green Chemistry Cracking Furnace
    2008
    Co-Authors: Sinopec Shanghai
    Abstract:

    Study on the history course of Concorde-jet exited business flight and largest Airbus A380 airplanes flied first business flight.The development course of ethylene Cracking Furnace is mostly similar as above airplane history course.The history course and development trend of large capacity and green chemistry(LCGC) Cracking technology is enhanced ethylene selectivity,increased energy efficiency and improved health,safety and environment protection.Apply multiple dimensions model of Monte Carlo Simulation Method to study Cracking Furnace and use this calculation result to develop and design LCGC Cracking Furnace.The LCGC Cracking Furnace is better than traditional Cracking Furnace in general ideas,design scheme,process parameter,technology special feature,equipment structure,technology and economy analysis,operation maneuverability etc.It is more progressive technology and more reasonable economy that integration replacing method instead of conventionality revamping method by using LCGC Cracking Furnace.The integration replacing method of LCGC Cracking Furnace is better than the conventionality revamping method that instead of domestic Cracking Furnace which had been run between 20 and 30 years,equipment aging severity,smaller scale capacity.

  • INDUSTRIAL APPLICATION OF HYDROCRACKED TAIL OIL IN INVERTED TRAPEZOIDAL Cracking Furnace
    Ethylene Industry, 2007
    Co-Authors: Sinopec Shanghai
    Abstract:

    After the Ethylene Plant of Jinshan Petro- chemical Company was revamped and put into opera- tion,the ethylene Cracking feedstock gap of supply and demand was accordingly increased.In order to meet the demand of material balance and optimize the Cracking feedstock for No.1 Ethylene Plant,an experiment was carried out on the F-101 inverted trapezoidal Cracking Furnace to process hydrocracked tail oil.Because the hydrocracked tail oil consists of heavy component and has high dry point,the main purpose of the experiment was to validate the feasibility analysis of Cracking hydro- cracked tail oil in inverted trapezoidal Cracking Furnace, including the Cracking performance of hydrocracked tail oil and the operation cycle of Cracking Furnace.

Guihua Hu - One of the best experts on this subject based on the ideXlab platform.

  • Analytical models for heat transfer in the tube bundle of convection section in a steam Cracking Furnace
    Applied Thermal Engineering, 2019
    Co-Authors: Benfeng Yuan, Guihua Hu, Weimin Zhong, Feng Qian
    Abstract:

    Abstract In steam Cracking Furnaces, about 45% of the heat released from fuel combustion is recovered by preheating Cracking feedstock and high pressure steam. This is achieved mainly by indirect convective heat transfer from flue gas to feedstock and steam in a series of tube bundles located in the so-called convection section. Radiation also plays in an important role in lower bundles where flue gas temperature is high. Thus, accurate heat transfer simulation of the convection section is crucial for operation optimization and control of steam Cracking Furnace. This paper aims to develop a heat transfer analysis (HTA) model for combined convective and radiative heat transfer calculation in tube bundles with both inline and staggered layouts. Computational fluid dynamics (CFD) was employed to validate the HTA model in the first stage. Based on the HTA model, coupled steady-state simulations of the convection section were performed for an industrial steam Cracking Furnace with naphtha feed capacity of 30 t/h. The predictions of the HTA model are in good agreement with the design data. The study shows that convective heat transfer is significantly enhance by fins in the tube bundles where the flue gas temperatures are too low to effectively heat up the feedstock. Radiative heat transfer at the bottom of the convection chamber is much larger than the convective heat transfer and thus cannot be ignored in heat transfer calculation.

  • coupled simulation of convection section with dual stage steam feed mixing of an industrial ethylene Cracking Furnace
    Chemical Engineering Journal, 2016
    Co-Authors: Guihua Hu, Benfeng Yuan, Wenlin Du, Jinlong Li, Liang Zhang, Feng Qian
    Abstract:

    Abstract A complete coupled simulation of the convection chamber and tubes with dual stage steam feed mixing of an industrial ethylene Cracking Furnace has been carried out with the computational fluid dynamics (CFD) method for the first time. In the convection chamber, the standard k – e model and discrete ordinates (DO) radiation model were respectively used in the descriptions of turbulence characteristics and radiative heat transfer. In the tubes, renormalization group (RNG) k – e model and volume of fluid (VOF) model were respectively applied to the turbulence flow and the liquid–vapor two phases flow. Simulation results agree well with the industrial data. Based on the coupled result, a dynamic simulation was calculated in the feedstock preheater (FPH). Simulation results show that the velocity and temperature fields are inhomogeneous distributions along the width direction due to the asymmetrical structure of convection chamber. Two recirculation zones occur at the corner both near and away from the entrance to the convection chamber, which will cause a longer residence time of flue gas and local overheating in Furnace wall of convection chamber. The process gas temperature, tube skin temperature and heat flux profiles are respectively different along the axial and radial direction of the high temperature coil (HTC-I). The changes of flow pattern from bubble flow to spray flow are effected by gravity and centrifugal force during evaporation. The results will be helpful for the design and operation in Cracking Furnace.

  • Comprehensive CFD simulation of product yields and coking rates for a Floor- and wall-fired naphtha Cracking Furnace
    Industrial & Engineering Chemistry Research, 2011
    Co-Authors: Guihua Hu, Feng Qian, Jinlong Li, Honggang Wang, Yu Zhang, Kevin Van Geem, Guy Marin
    Abstract:

    A coupled Furnace/reactor simulation was conducted to determine product yields and coking rates for an industrial SL-II naphtha Cracking Furnace fired by both floor and wall burners. The process gas side, as well as the fire side, is simulated using the computational fluid dynamics (CFD) approach. The molecular kinetic model of Kumar and co-workers was used to simulate the naphtha Cracking reactions in the reactor. The results show that the asymmetrical design of the Furnace results in asymmetrical profiles of flue gas velocity, temperature, and concentration, and leads to poor heat supply of the wall burners on the front wall as well as a high-temperature zone in the crossover section. The recirculation of flue gas caused by the positioning of burners makes the temperature more uniform in the middle of the Furnace. Good agreement between simulation and industrial product yields has been obtained without any tuning of the kinetics, indicating that the proposed approach can be used as a guide for further o...