Continuous Catalytic Reforming Research Articles

Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

Causality-embedded reconstruction network for high-resolution fault identification in chemical process

Fault identification is essential for analyzing the root causes and propagation of faults. Traditional identification based on contribution plots often suffer from the smearing effect, a phenomenon where the residuals of fault variables smearing over other non-fault process variables due to the complex interconnections within the processes, potentially leading to ambiguous or inaccurate diagnosis. To address this, we propose the Causality-Embedded Reconstruction Network (CERN) based high-resolution fault identification for chemical process. CERN is an advanced graph network model that embeds causality, featuring a dual-decoding scheme comprising a causal decoder and an attribute decoder. This design ensures that feature reconstruction is guided by variables with established causal relationships, effectively isolating the feature representations of normal variables from the influence of fault variables that lack a causal connection. By leveraging this causality-embedded reconstruction, the smearing effect can be alleviated. Experiments conducted on a Continuous Stirred-Tank Reactor system and a real-world Continuous Catalytic Reforming process to demonstrate that the proposed CERN-based fault identification method not only improves the accuracy in identifying fault variables but also offers explanatory insights and traceability for fault diagnosis, showing prospects for industrial process safety.

Fluid dynamics and erosion analysis in industrial naphtha reforming: A CFD-DPM simulation approach

This paper explores using Computational Fluid Dynamics-Discrete Phase Model simulation in an industrial naphtha reforming unit. It analyzes the reformer process, including the reformer, hopper, and three elbow types. Objectives encompass catalyst residence time, fluid flow, and erosion due to the catalyst in these geometries. Simulation results unveil insights, showing varying erosion rates among the elbows. The 90-degree elbow has the highest erosion rate 4.42×10−8kgm2s, followed by the 60-degree elbow3.49×10−8kgm2s, and the 45-degree elbow with lower erosion rate 2.65×10−8kgm2s. Results align with literature, emphasizing flow dynamics' role in optimization and maintenance. The study validates simulation with only 1.58% deviation from plant data, highlighting model's precision. Catalyst residence time is 1.28 s. Erosion patterns in the hopper pinpoint high-density zones as erosion-prone areas, guiding preventive measures. This research enhances continuous catalytic reformer systems' efficiency and integrity, aiding decisions for design and maintenance, ultimately improving overall process efficiency and stability.

Modeling and Parameter Tuning for Continuous Catalytic Reforming of Naphtha in an Industrial Reactor System

A two-dimensional mathematical model was developed to simulate naphtha reforming in a series of three industrial continuous catalytic regeneration (CCR) reactors. Discretization of the resulting partial differential equations (PDEs) in the vertical direction and a coordinate transformation in the radial direction were performed to make the model solvable using Aspen Custom Modeler. A sensitivity-based parameter subset selection method was employed to identify the most influential parameters within the model. Tuning of 8 out of 180 parameters was used to ensure that model predictions match experimental data from one steady-state run. The updated parameter values improved the model fit to the data, reducing the weighted least-squares objective function for parameter estimation by 73%. The proposed model was used to predict reactor temperatures, catalyst coke weight fraction at the exit of the third reactor, and benzene flowrate from the outlet of the third reactor. The simulation results demonstrated a good agreement between the simulated values and the industrial measurements. Finally, the reactor model was utilized to explore the effects of changes in inlet temperatures and inlet level of catalyst deactivation, providing valuable insights for identifying desirable operational conditions that will improve the overall efficiency of the CCR process.

Influencing Factors of Carbon Emission from Typical Refining Units: Identification, Analysis, and Mitigation Potential

As the global third-largest stationary source of carbon emissions, petroleum refineries have attracted much attention. Many investigations and methodologies have been used for the quantification of carbon emissions of refineries at the industry or enterprise scale. The granularity of current carbon emissions data impairs the reliability of precise mitigation, so analysis and identification of influencing factors for carbon emissions at a more micro-level, such as unit level, is essential. In this paper, four typical units, including fluid catalytic cracking, Continuous Catalytic Reforming, delayed coking, and hydrogen production, were chosen as objects. A typical 5-million-ton scale Chinese petroleum refinery was selected as an investigating object. The Redundancy analysis and multiple regression analysis were utilized to explore the relationship between the process parameters and carbon emissions. Three types of influencing factors include reaction conditions, processing scale, and materials property. The most important mitigation of carbon emission, in this case, can be summarized as measures of improving energy efficiency via optimizing equipment parameters or prompting mass efficiency by upgrading the scale for material and energy flow.

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery.

Risk analysis and prediction is a primary monitoring strategy to identify abnormal events occurring in chemical processes. The accidental release of toxic gases may result in severe problems for people and the environment. Risk analysis of hazardous chemicals using consequence modeling is essential to improve the process reliability and safety of the refineries. In petroleum refineries: toluene, hydrogen, isooctane, kerosene, methanol, and naphtha are key process plants with toxic and flammable chemicals. The major process plants considered for risk assessment in the refinery are the gasoline hydrotreatment unit, crude distillation, aromatic recovery, continuous catalytic reformer, methyl-tert-butyl-ether, and kerosene merox units. Additionally, we propose a threat and risk analysis neural network for the chemical explosion (TRANCE) model for refinery incident scenarios. Significantly, 160 attributes were collected for the modeling on the basis of the significance of failure and hazardous chemical leaks in the refinery. Hazard analysis shows that the leakages of hydrogen and gasoline at the gasoline hydrotreatment unit, kerosene at the kerosene merox plant, and crude oil at crude-distillation units were areas of profound concern. The developed TRANCE model predicted the chemical explosion distance with an R2 accuracy value of 0.9994 and MSE of 679.5343.

Optimization and assessment method for total energy system retrofit in the petrochemical industry considering clean energy substitution for fossil fuel

Carbon emission peak and neutrality prospect has expedited the revolution of conventional energy utilization mode, particularly in highly energy-intensive industries. The petrochemical industry bears great responsibility for energy conservation and carbon emission reduction. TES (Total Energy System) retrofit schemes in the petrochemical industry classified as REST (Retrofitting through Energy-Saving Techniques) and RSFC (Retrofitting through Substituting Fossil fuel with Clean energy) have emerged in large numbers recently. It is challenging for energy managers to optimize and assess TES retrofit schemes simultaneously and globally, once clean energy supply is involved. State-of-the-art assessment and optimization method for TES retrofitting is urgently needed. This work proposes a novel optimization and assessment method based on the exergy conversion and consumption processes to figure out the most cost-effective retrofit schemes in view of exergy analysis. The method features with the simulation of TES, the clean energy substitution for fossil fuel, the optimization of the subsystems, and the exergy efficiency assessment of retrofit schemes. The double-parallel-loop application framework is then presented, which deals with the optimization and assessment of RSFC and REST simultaneously. Combined plants of continuous catalytic reforming and aromatic extraction plants along with their matched TES are investigated. A MINLP optimization model is formulated and programmed with GAMS software. The optimal results show that the total exergy consumption decreased by 3200 kW with clean energy substitution. Sensitivity analysis explores the energy conservation benefits and exergy efficiency improvement when clean energy with different qualities and quantities substituting for fossil fuel, which provides valuable information for feasibly implementing retrofit schemes. Comparison of scenarios reveals the interacting influence between the implementation of RSFC and REST, which is significant for decision-making when multiple retrofit schemes are alternatives. Real-world case investigations demonstrate the validation and efficacy of the proposed method. The main findings of this study are significant and quantitative references for petrochemical industrial practice, which would contribute to energy conservation and carbon emission reduction in the future.

Multiple linear regression and GRU model for the online prediction of catalyst activity and lifetime in counter-current continuous catalytic reforming

Multiple linear regression and GRU model for the online prediction of catalyst activity and lifetime in counter-current continuous catalytic reforming

Large-scale chemical process causal discovery from big data with transformer-based deep learning

Fault diagnosis is critical for ensuring safe and stable chemical production. Correct identification of causal relationships among variables in large-scale chemical processes is a prerequisite for analyzing the root causes and propagation paths of faults. However, chemical process big data often exhibit nonlinearity and nonstationarity, and contain various forms of noise, rendering conventional causal discovery methods vulnerable. In this paper, a novel causal discovery method based on the causality-gated time series Transformer (CGTST) is proposed to address this challenge. By performing time series prediction using the Transformer-based model on the target variable, CGTST measures the causal strength by assessing the contribution of each variable to the prediction through the causality gate structure. Furthermore, a causal validation method based on permutation feature importance is proposed to eliminate spurious causal relationships and ensure robust results. To enhance the performance of causal discovery on nonlinear and nonstationary chemical process data, ensemble empirical mode decomposition is employed to reduce noise. The CGTST-based method is validated on three case studies: a continuous stirred-tank reactor, the Tennessee Eastman process, and a real-world continuous catalytic reforming process. Our findings demonstrate that the proposed method outperforms conventional causal discovery methods and holds promising prospects for industrial applications.

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.

Multi-Scale Process Monitoring Based on Time-Frequency Analysis and Feature Fusion

Data-driven process monitoring is an important tool to ensure safe production and smooth operation. Generally, implicit information can be mined through data processing and analysis algorithms to detect process disturbances on the basis of historical production data. In industrial practice, signals with different sources of disturbance show different distribution patterns along with the time domain and frequency domain, that is, noise and pulse-type changes are usually contained in the high-frequency portion while most process dynamic is contained in the low-frequency portion. However, feature extraction is usually implemented at a single scale in traditional multivariate statistical algorithms. With this concern, a novel multi-scale process monitoring method is proposed in this work, by which wavelet packet decomposition is first employed for time-frequency analysis. After decomposition, multivariate statistical models are established for each scale to construct process statistics. For the high-frequency part, the classical principal component analysis (PCA) algorithm is adopted to construct squared prediction error (SPE) and Hotelling T2(T2) statistics. While for the low-frequency part, the slow feature analysis (SFA) algorithm is adopted to construct T2, Te2, S2 and Se2 statistics for the extraction of the long-term slowly changing trend. Then the monitoring statistics, obtained from each method at different scales, are integrated by a support vector data description (SVDD) method to give a final fault detection decision. The performance of the proposed method is verified on the benchmark Tennessee Eastman Process (TEP) and an industrial continuous catalytic reforming heat exchange unit by comparing with related multivariate statistical methods, which only focus on a single scale.

Statistical method based on dissimilarity of variable correlations for multimode chemical process monitoring with transitions

Chemical industrial processes are always accompanied by multiple operating conditions, which brings great challenges for multivariate statistical process monitoring methods to extract general characteristics from multimode data, especially for time-varying characteristics in transitions between two modes. In this work, a novel statistical process monitoring method based on the dissimilarity of process variable correlation (DISS-PVC) is proposed. The proposed method aims to monitor multiple stable modes and between-mode transitions simultaneously with no prior knowledge of the number of operating modes. Unlike traditional methods oriented to monitoring process variables, the proposed method is applied to monitor the correlation of process variables based on the idea that variable correlation should always conform to a certain process internal mechanism, no matter in which stable or transition mode. Mutual information is first employed to quantitate variable correlation with a moving-window approach. Cosine similarity between eigenvalues of mutual information matrices is selected as a dissimilarity index to evaluate the difference in variable correlation between two data sets and perform fault detection. The effectiveness of the proposed method is verified on the benchmark Tennessee Eastman (TE) process and an industrial continuous catalytic reforming heat exchange unit.

Mesoporous silica-encaged ultrafine ceria–nickel hydroxide nanocatalysts for solar thermochemical dry methane reforming

Reforming of methane to produce synthesis gas for the Fischer–Tropsch process provides an alternative to fossil fuels. Silica-encaged ceria–nickel hydroxide catalysts were produced by an in situ synthesis method to obtain ultrafine bimetallic species dispersed evenly within the mesoporous silica matrix. Dry reforming and reduction-oxidation cycling was undertaken with the materials. Catalysts with high content of nickel showed good activity during dry reforming, with conversions rates close to equilibrium in equimolar conditions. Insignificant deactivation of the catalysts was observed over 5 h and 50 h of reaction at 900 °C. Syngas production via reduction–oxidation cycling was shown to be insignificant as compared to continuous catalytic reforming.

TECHNOECONOMIC ANALYSIS OF REFINING NIGERIAN LIQUEFIED NATURAL GAS CONDENSATE

Condensate refining is among the strategies proposed to solve the light oil glut around the globe. The Nigerian Liquefied Natural Gas (NLNG), which is the Nigerian government’s best performing investment in the natural gas value chain, produces plant condensate as a by-product. In this paper, the economics of a refinery designed to use NLNG plant condensate is evaluated under an optimistic oil price forecast and a pessimistic oil price trend. A gasoline producing refinery configuration was chosen for this study, and it comprises of a naphtha splitter, a Penex isomerisation unit and a Continuous Catalytic Reforming (CCR) unit. The product yields and plant costs were determined by established correlations and industry estimates. The proposed refinery will convert 40,000 bpd plant condensate into 96% gasoline, 3% LPG and 1% hydrogen, and economic indicators such as Net Present Value (NPV), Internal Rate of Return (IRR) and Profitability Index (PI) were used to assess the economic viability of the refinery. The optimistic scenario of oil price forecast resulted in an NPV of $ 531.90 million, an IRR of 20.09% and a PI of 3.16, while the pessimistic scenario gave an NPV of $16.26 million, an IRR of 11.16% and a PI of 1.07. These results prove that a condensate refinery with the proposed configuration is economically feasible and interested investors in Nigeria’s refining space should explore this possibility.

Dynamic Simulation of Continuous Catalytic Reforming Process Based on Simultaneous Solution

The dynamic simulation of the continuous catalytic reforming process is of great significance to the performance prediction and optimization of the entire process. In this study, a 34-lumped mechanism model described by differential algebra was established based on the actual process conditions of the continuous catalytic reforming process in China, and an efficient dynamic simulation solution method based on simultaneous equations was proposed. First, a 34-lumped differential–algebraic mechanism model was established based on the basic principles of reforming kinetics, thermodynamics, material balance, and energy balance. Secondly, in order to solve and simulate the mechanism model composed of 144 differential equations and several algebraic equations, the method of finite-element collocation is used to discretize the differential equations and convert them into large-scale, nonlinear programming problems, and the interior point algorithm is used to estimate its parameters and verify the model. In addition, in order to avoid the problem of too long derivative solution time and too large memory in the solution process, methods such as sparse derivative and Broyden–Fletcher–Goldfarb–Shanno (BFGS) with limited storage are used to solve the problem. Finally, on the basis of model verification, dynamic simulation and sensitivity analysis of the whole process are carried out by modifying different input parameters. The results show that the mechanism model and solution method presented in this paper can quickly and accurately simulate the continuous catalytic reforming process dynamically.

Simulation and optimization of continuous catalytic reforming: Reducing energy cost and coke formation

Continuous catalytic naphtha reformers are one of the main refinery units producing hydrogen. Reliable and comprehensive modeling of such a complex and integrated refinery unit plays a pivotal role in optimization to increase the efficiency of the process. A reaction network with 26 pseudo components and 58 reactions, considering coke formation and catalyst activity, was proposed to predict the outlet compositions of reactors in more detail. The whole unit, including reaction and purification sections, was simulated to cover all products. NOMAD optimizer, with the mesh adaptive direct search algorithm, is used to minimize energy costs. Non-linear constraints were defined to guarantee the quality of the final products. The optimized decision variables reduced the energy cost by 13.5%, mainly caused by a significant decline in natural gas consumption. The simulation also indicated an increase of 4800 ton/y in reformate production, which the product is rich in aromatics. By contrast, LPG, hydrogen-rich gas, and coke production dropped by nearly 4355, 321, and 127 ton/y, respectively. The coke production decline can significantly increase the catalyst lifetime and decrease the operating cost of the catalyst regeneration unit.

Metal carbide as a potential non noble metal catalyst for naphtha reforming

In petroleum refining, continuous catalytic reforming of heavy naphtha is an important process to produce high value products such as hydrogen and aromatics from the low-value feedstock. Pt-Sn supported on chlorinated alumina is an accepted catalyst over last seven decades. However, use of expensive noble metals and corrosion due to halogen leaching are major issues associated with this catalyst. Such problems can be addressed by replacing the precious Pt metal with equally active and a stable non-precious metal catalytic material over suitable non-chlorinated solid acidic supports. In the present study Fe5C2, WC, Fe2W2C, Mo2C carbide materials have been explored as alternatives to Pt and these materials have been synthesized by solid-state temperature-programmed reaction and supported on different zeolites for imparting acidic function to the catalyst. Screening of these catalyst combinations for heavy naphtha reforming under suitable reaction conditions shows that, Mo2C supported on HY zeolite is found to be an active catalyst compared to any other carbide and zeolite combinations, with product aromatics in the range of 50 wt%, RON of 96, while maintaining reformate yields around 91 wt%.

Emission characteristics and associated assessment of volatile organic compounds from process units in a refinery

The accuracy and reliability of volatile organic compound (VOC) emission data are essential for assessing emission characteristics and their potential impact on air quality and human health. This paper describes a new method for determining VOC emission data by multipoint sampling from various process units inside a large-scale refinery. We found that the emission characteristics of various production units were related to the raw materials, products, and production processes. Saturated alkanes accounted for the largest fraction in the continuous catalytic reforming and wastewater treatment units (48.0% and 59.2%, respectively). In the propene recovery unit and catalytic cracking unit, alkenes were the most dominant compounds, and propene provided the largest contributions (57.8% and 23.0%, respectively). In addition, n-decane (12.6%), m,p-xylene (12.4%), and n-nonane (8.9%) were the main species in the normal production process of the delayed coking unit. Assessments of photochemical reactivity and carcinogenic risk were carried out, and the results indicate that VOC emissions from the propene recovery unit and catalytic cracking unit should be controlled to reduce the ozone formation potential; in addition, alkenes are precedent-controlled pollutants. The cancer risk assessments reveal that 1,2-dibromoethane, benzene, 1,2-dichloroethane, and chloroform were the dominant risk contributors, and their values were much higher than the standard threshold value of 1.0 × 10−6 but lower than the significant risk value defined by the US Supreme Court. Based on the VOC composition and a classification algorithm, the samples were classified into eight main groups that corresponded to different process units in the petroleum refinery. In conclusion, this work provides valuable data for investigating process-specific emission characteristics of VOCs and performing associated assessments of photochemical reactivity and carcinogenic risk in petrochemical refineries.

Real-Time Semisupervised Predictive Modeling Strategy for Industrial Continuous Catalytic Reforming Process with Incomplete Data Using Slow Feature Analysis

The catalytic naphtha reforming process is one of the most significant processes in the petrochemical industry. This process is notable for its function of transforming petroleum refinery naphtha f...

Molecular-Level Kinetic Model for C12 Continuous Catalytic Reforming

A detailed kinetic model for a continuous catalytic reforming (CCR) process was developed. The model included 447 naphtha molecules (C1–C12) that underwent 1469 reactions. Paraffin and naphthenic isomers up to C9 components were fully depicted, whereas aromatic isomers were fully described up to C10. Coking kinetics and the corresponding deactivation of the catalyst were integrated into the model. The steady state kinetic parameters were tuned using pilot plant data for a widely used industrial catalyst. To enable the use of commercial plant data, the energy balance and catalyst moving mechanism of typical CCR reactors were also formulated. The model was then used to simulate an industrial unit loaded with the same catalyst after deactivation calibration by adjusting a few deactivation parameters. The results showed that calculated PONA fractions, individual aromatic species, and the temperature drops of each reactor were in good accord with industrial data.