Cracking Furnace Research Articles

Chemical Composition Analysis and Kinetic Analysis of Oxidation of Coke Deposits Recovered from Cracking Furnaces in Ethylene Plant

Chemical Composition Analysis and Kinetic Analysis of Oxidation of Coke Deposits Recovered from Cracking Furnaces in Ethylene Plant

Analysis of Flow-induced Noise Characteristics of Ethylene Cracking Furnace Tubes before and after Coking

Analysis of Flow-induced Noise Characteristics of Ethylene Cracking Furnace Tubes before and after Coking

Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition

A thermal cracking furnace is an important equipment in the petrochemical industry that is typically used for breaking long hydrocarbons into short chains and producing coke as a byproduct. Deposition of the generated coke increases the temperature at the outside coil wall, necessitating regular furnace maintenance to prevent coil failure. Therefore, this study proposed a machine learning approach with a posteriori-based feature to predict the service life of the furnace to runtime failure. The proposed approach consists of a two-level machine learning model, which aims to improve prediction accuracy and reduce feature sensitivity. The label is classified as a week-range label, which can be categorized by classification criteria into three classes: weekly, bi-weekly, and quarter-weekly. The first-level model is utilized to extract sensor features into the posterior probability class label score. These scores are then processed and sorted into moving windows to generate features for the second-level model. The results showed that the proposed model could extract process variation and identify service needs, which improved classification accuracy by 23.94 % and 17.67 % for the clean and coke-contaminated datasets compared to the conventional classification model, respectively. Additionally, the most general class range (quarter-weekly) provided the best performance compared to the bi-weekly and weekly classes. Therefore, the model has the potential for the prediction of the furnace service life under pseudo-steady state conditions, where coking evolves gradually over time.

Soft Sensor Modeling Method Considering Higher-Order Moments of Prediction Residuals

Traditional data-driven soft sensor methods can be regarded as an optimization process to minimize the predicted error. When applying the mean squared error as the objective function, the model tends to be trained to minimize the global errors of overall data samples. However, there are deviations in data from practical operation, in which the model performance in the estimation of the local variations in the target parameter worsens. This work presents a solution to this challenge by considering higher-order moments of prediction residuals, which enables the evaluation of deviations of the residual distribution from the normal distribution. By embedding constraints on the distribution of residuals into the objective function, the model tends to converge to the state where both stationary and deviation data can be accurately predicted. Data from the Tennessee Eastman process and an industrial cracking furnace are considered to validate the performance of the proposed modeling method.

Multiobjective Optimization of CO2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

The dominant role of surface chemistry on coking resistance of passivating coating

Passivation coatings with excellent coking inhibition performance play an important role in active cooling technology and ethylene cracking furnace. However, different coating types possess different coking resistance, and the dominant factor remains unclear. Herein, a novel strategy of isolative analysis for coke in coating underwent RP-3 supercritical cracking is proposed to account for the role of carburization and surface chemistry. The carburization amount of TiN coating after cracking experiment was obtained from the model constructed by artificial carburization of standard TiN-coated samples. Introduced TiN/SiO2 coating owned different surface chemistry contrast to the TiN coating, additive of density-functional-theory calculations to reveal the correlation between surface chemistry and coke amount. The results indicated that carburization contributed little to the total coke amount of TiN coating, and non-negligibly, the coke diffused rapidly along the grain boundaries, threatened the integrity of coating. More importantly, the main coke belonged to the part that adhered to the coating surface. The strongly adsorbed 3d-4σ*/2π*bonded coking precursors (C2H2), were presented on the TiN surface, while the great flexibility of amorphous SiO2 made it non-adsorbent to C2H2, leading to weak coke adhesion on the TiN/SiO2 surface and strongly adherent coke on TiN surface. These results reveal that surface chemistry influenced coke adhesion and dominated coking resistance of coating.

A data-driven strategy for industrial cracking furnace system scheduling under uncertainty

Cyclic scheduling of ethylene cracking furnace system (CSECFS) has a significant impact on raising economic performance of ethylene plants. However, in actual plants, optimal results of deterministic models may be suboptimal or ineffective because of various uncertainties. This paper proposes a novel data-driven adaptive robust optimization (DDARO) strategy that effectively bridges robust optimization and machine learning methods. A mixed-integer nonlinear programming (MINLP) model for CSECFS is developed initially. Second, data-driven uncertainty sets are generated using the historical data of processing flow rates: maximally correlated principal component analysis (MCPCA) is employed to partition the data into two subspaces, which are then delineated by support vector clustering (SVC) and generalized norm approaches, respectively. Third, a multi-stage DDARO model is established using the derived uncertainty sets and afterward transformed as a tractable single-stage model. Finally, a real-world case is performed to exemplify the effectiveness of the proposed framework.

Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition

A cracking furnace is the primary unit of a naphtha cracking center (NCC) to produce ethylene (EL) and propylene (PL); the yields of EL and PL depend on the naphtha composition of the NCC feedstock. However, the naphtha composition typically fluctuates depending on the feedstock suppliers, and the consequent uncertainties in the composition causes investment risks associated with the net profit. However, owing to the high dimensional uncertainties of the naphtha, conventional sampling (e.g. Monte Carlo method) based robust optimization is infeasible option due to high computational cost. In this study, we adopt the polynomial chaos expansion (PCE) to surrogate the system considering the uncertainty. Owing to the orthogonality of the PCE, the statistical moment of the PCE can be directly calculated without uncertainty sampling and iterative simulation. In this study, the PCE-based profit optimization model of the NCC furnace is developed as follows: Initially, we infer probability density functions (PDF) using industrial data to consider the uncertainty in the naphtha composition. Using the inferred PDF, we extract training dataset samples with coil outlet temperature (COT), product price, feed composition, and the net profit. Subsequently, using the training dataset, we develop a polynomial chaos expansion (PCE)-based surrogate model to predict the moments of the net profit, namely, the mean, variance, and skewness. Owing to the orthogonality of the model, the moments can be parameterized with only decision variables instead of computing the uncertainty. Finally, we incorporate the developed PCE-based model into a genetic algorithm to simultaneously optimize two conflicting objectives: maximizing the mean profit and minimizing the variance. The optimization results reveal the trade-off relationship between the mean profit and investment risk (variance and skewness) of the NCC process under feed uncertainty. Owing to the orthogonality, the optimal decision point can be provided with low computation time and high prediction accuracy compared with the sampling based optimization method. Considering the application of the proposed optimization model, we conduct case studies for two different scenarios of the product price. The optimal COTs for maximum mean profit with minimum variance of profit in the first and second scenarios range from 723 to 833 and 734 to 898 ℃, respectively. Therefore, the proposed model can quantitatively predict the mean profit with investment risk and help decision-makers select optimal operating conditions considering both the investment tendency and uncertainty in the naphtha composition.

Coke deposition and run length in industrial naphtha thermal cracking furnaces via a quasi‐steady state coupled CFD model

AbstractMathematical modelling of the thickness of the coke layer growing over months in millisecond cracking reactors is a dilemma in computational fluid dynamics (CFD) simulation. To address the time scale issue, a quasi‐steady state (QSS) approach was employed through a comprehensive coupled reactor/firebox CFD model in the current study. The model was applied to predict the time‐dependent behaviour of coke deposition and to determine the appropriate operating conditions for maximum olefin yields over an industrial furnace run length. A novel algorithm was designed to overcome the complexity of QSS simulation of the CFD model, which is a combination of reactive turbulence flow, combustion, and radiation models. The furnace parameters were studied as a function of two variables: the dilution steam‐to‐feed ratio and the liberated heat by the burners. The results indicated that the run length can be extended by up to 20% while retaining the main product yields. This study offers practical suggestions to maximize the run length in the operation.

Techno-economic evaluation of post-combustion carbon capture based on chemical absorption for the thermal cracking furnace in ethylene manufacturing

As the core unit of an ethylene production plant, thermal cracking furnace generates a large amount of CO2 emissions. To reduce the CO2 emissions, post-combustion carbon capture based on chemical absorption using monoethanolamine (MEA) solvent is used to capture CO2 from the thermal cracking furnace. The computational fluid dynamics (CFD) method was used to simulate the operation of a 60kt/a thermal cracking furnace to obtain the flow rate and composition of the flue gas. A carbon capture plant model was developed using Aspen Plus® and validated using the pilot plant test data from Technology Centre Mongstad (TCM) in Norway. Scale-up of the capture plant model was carried out to match the flue gas flow rate of the thermal cracking furnace. Two integration cases of the carbon capture plant and the industrial thermal cracking unit were carried out. The results show that the excess heat of the gasoline fractionator can be used to provide heat for the carbon capture plant without affecting ethylene production. The economic evaluation was conducted based on the two case studies. Results show that the cost of CO2 capture can be reduced from $80.03/tonne (without heat integration) to $70.41/tonne with heat integration. When considering the impact of carbon credits on capture costs, the cost will be further reduced to $50.41/tonne.

A Data-Driven Semi-Supervised Soft-Sensor Method: Application on an Industrial Cracking Furnace

The cracking furnace is the key equipment of the ethylene unit. Coking in furnace tubes results from the generation of coke during cracking, which will compromise the heat transfer efficiency and lead to shape change of tubes. In order to keep the cracking furnace operating economically and safely, the engineers need to decoke according to the surface temperature of the furnace tube. However, the surface temperature of the furnace tube is difficult to obtain in practice. Due to redundant instrumentation and the high level of process control in cracking furnaces, a large number of operation data have been collected, which makes it possible to predict the surface temperature of furnace tubes based on autocorrelation and cross correlation within and among variables. Traditional prediction methods rely on labeled data samples for training, ignoring the process information contained in a vast amount of unlabeled data. In this work, a data-driven semi-supervised soft-sensor method is proposed. Considering the nonlinear and dynamic relationship among variables, long short-term memory network (LSTM) autoencoder (AE), a deep neural network suitable for the feature extraction of long-term nonlinear series, is used for pre-training to extract process data features from unlabeled and labeled data. Then, principal component analysis (PCA) and mutual information (MI) are applied to remove feature correlation and select features related to target variables, respectively. Finally, the selected data features are utilized to establish a soft-sensor model based on artificial neural network (ANN). Data from an industrial cracking furnace of an ethylene unit is considered to validate the performance of the proposed method. The results show that the prediction error of furnace tube surface temperature is about 1% and successfully aid engineers in determining the optimal time for decoking.

TiN-SiO2 double layer composite coating with enhanced oxidation resistance and reusability in anti-coking applications

The passivation coating has been the main solution to the coking problem in the field of active cooling technology and ethylene cracking furnace. The need for repeated use and essential decoking process poses a great challenge to the single-layer coating. Herein, a new TiN/SiO2 double-layer coating suitable for internal channels has been prepared and characterized, whose results are compared with single-layer TiN coating. The oxidation resistance was revealed by HT-XRD and TGA. The reusability was analyzed from surface roughness, bonding force and coking inhibition performance in the repeated use of the coking-decoking cycle (RP-3 supercritical pyrolysis, on-line decoking technology). Results reveal the synergistic effect of the inner and outer layers of TiN/SiO2 coatings. The outer amorphous SiO2 with high crystallization temperature plays the role of isolating O and C atoms from penetrating, and the inner TiN acts to reduce the mismatch of thermal expansion coefficient between substrate and SiO2. Therefore, excellent oxidation resistance, with a bearable temperature of 1000 °C, can be obtained for the TiN/SiO2 coatings. In addition, during the coking-decoking cycles, the roughness of TiN/SiO2 coatings has been maintained at a low level; the coating has been tightly adhered to the substrate throughout the process; and the coking resistance has not been weakened with an average anti-coking ratio of 93%, higher than that of TiN (31–67%).

Simulation and Optimization of the Ethane Cracking Furnace Using ASPEN PLUS and MATLAB: A Case Study from Petrochemical Complexes

ABSTRACT Ethylene production is one of the most abundant activities in petrochemical industries. So, the main objective is to develop detailed models to describe a pyrolysis section in the real ethylene plant furnace located at the petrochemical complex (PC1), Basra, Iraq. Those models were utilized to evaluate the effects of the reaction temperature, steam to ethane ratio (DS/HC), and feedstock flowrate on the ethylene yield using different feedstock compositions. The simulation validity was tested by comparing the simulation results with the real data collected from the PC1 and showed an acceptable similarity and reliability depending on relative error values. A genetic algorithm coded by MATLAB and Aspen Plus optimizer was used to obtain the optimal operating conditions to give a maximum profit. The optimization results of Aspen Plus and GA showed an increase in the profit equal to 10.541 and 9.823% respectively when the reaction temperature, DS/HC and outlet flowrate of hydrocarbons were in the range of 850–920°C, 0.2–0.5, and 2421–1064 Kmole/hr respectively. Also, Increasing the ethane content in feedstock leads to an increase in the feedstock production of 16–19% ethane content.

Multi-Objective Optimization of Co2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Non-Dominated Sorting Genetic Algorithm

Multi-Objective Optimization of Co2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Non-Dominated Sorting Genetic Algorithm

Exploration on Cracking Process of Huanqiu Cracking Furnace

Exploration on Cracking Process of Huanqiu Cracking Furnace

Investigating the Potential of Recycling Flare-Source Hydrocarbon Gases in an Industrial Burner

Abstract Flare gas is considered a global environmental concern. Flaring contributes to wasting limited material and energy resources, economic loss, and greenhouse gas emissions. Utilizing flared gas as a fuel feed to industrial cracking furnaces grants advantages in terms of fuel economy and emissions reduction. This work presents the results obtained by ansysfluent simulation of a flare hydrocarbon gas utilized in a steam-cracking furnace of ethylene process when combusting hydrocarbons flare gas in a low-NOx burner. In addition, the study determined the suitability of different hydrocarbon fuel mixtures in satisfying the required adiabatic flame temperature. The flared stream is assumed to be inlet from both primary and secondary staged fuel burners. The simulation results illustrated the detailed temperature profiles along the furnace flue gas side. They also presented the influence of flare stream compositions and Wobbe Index (WI) on the temperature profile. It was found that having an alternative fuel with a heating value or WI similar to that of methane would not result in the same temperature profile of methane, as the current fuel source. In addition, using different excess air percentages has no linear effect on the burner’s temperature profile. However, the results showed that the best replacement of methane, as the main fuel source, is a flare mixture with the same WI of methane as well as a certain H2 content needs to be added to every flare mixture composition to reach the same temperature profile of methane.

Numerical Investigation of Short Elliptical Twisted Tube for Reduced Fouling Rate in Steam Cracking Furnaces

In the steam cracking industry of natural gas or naphtha, fouling of tubular heat exchangers by cokes is a major issue. This growing carboneous layer has two major negative effects: 1) it increases pressure drop and 2) reduces heat transfer from the tube wall to the processed fluid. Using the open-source Computational Fluid Dynamics software OpenFOAM, this study numerically investigates wall shear stress, pressure drop and heat transfer performances of swirl decaying flow generated by different elliptical cross-section twisted tube. One of the objectives is to determine if minor modifications of tube geometry can generate swirling flow which could enhance wall shear stress at a reduce pressure drop penalty. For a Reynolds number ranging from 10, 000 to 100, 000, it is shown that the investigated geometries could enhance heat transfer by 90% at an increased pressure drop of 128% which yields a Performance Evaluation Criterion (PEC) of 1.44. The comparison between the performances of the different geometries is carried out using a newly defined PEC based on the bulk temperature, along with the usual PEC.

A coupled CFD simulation approach for investigating the pyrolysis process in industrial naphtha thermal cracking furnaces

In the steam thermal cracking of naphtha, the hydrocarbon stream flows inside tubular reactors and is exposed to flames of a series of burners in the firebox. In this paper, a full three-dimensional computational fluid dynamics (CFD) model was developed to investigate the process variables in the firebox and reactor coil of an industrial naphtha furnace. This comprehensive CFD model consists of a standard k-ε turbulence model accompanied by a molecular kinetic reaction for cracking, detailed combustion model, and radiative properties. In order to improve the steam cracking performance, the model is solved using a proposed iterative algorithm. With respect to temperature, product yield and specially propylene-to-ethylene ratio (P/E), the simulation results agreed well with industrial data obtained from a mega olefin plant of a petrochemical complex. The deviation of P/E results from industrial data was less than 2%. The obtained velocity, temperature, and concentration profiles were used to investigate the residence time, coking rate, coke concentration, and some other findings. The coke concentration at coil exit was 1.9 × 10 −3 %(mass) and the residence time is calculated to be 0.29 s. The results can be used as a scientific guide for process engineers.

Embrittlement of HP40Nb Heat-Resistant Alloy at Intermediate Operating Temperatures

Sections of an ethane cracker furnace were constructed of a modified heat-resisting alloy, HP40Nb MA. The application included a section which was designed to operate at a maximum design temperature of only 740 °C. The selection of this alloy for this section is deemed conservative as the alloy is designed to operate at temperatures in excess of 1000 °C. An elbow from the colder section failed in a brittle manner in service after only 2 years in operation. A metallurgical investigation indicated the elbow material to possess significantly higher tensile strength than expected for a new material while tensile elongation indicated it to be severely embrittled. The embrittlement was proven to be the result of a combination of intradendritic precipitation of needle-like sigma phase and partial transformation of the primary Nb-carbides to G-phase. The sigma phase is expected to have resulted in strengthening of the matrix and embrittlement of the alloy. Strain localization of the interdendritic regions could not be accommodated by the brittle G-phase which resulted in embrittlement and interdendritic cracking. The precipitation of sigma phase is unexpected considering the high Ni-content of this alloy. Thermodynamic simulation, however, proved that the precipitation of sigma phase is indeed possible at temperatures between 680 and 725 °C when slightly elevated Si-content is present. From the perspective of elevated temperature strength and corrosion resistance, the use of HP40Nb MA may appear to be a conservative selection at the comparatively low operating temperature. Failure to recognize that the operating temperatures, while still very hot, are lower than recommended for this alloy therefore rendered the ostensible conservative selection as an incorrect selection.

Data-Driven Modeling and Cyclic Scheduling for Ethylene Cracking Furnace System with Inventory Constraints

The optimization of cyclic scheduling for an ethylene cracking furnace system (ECFS) is beneficial for ethylene plants. The scheduling problem involves multiple feeds, tanks, furnaces, and differen...