Continuous Catalytic Reforming Process Research Articles

Modeling and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model

To address the modeling and optimization challenges of the complex reaction system in the continuous catalytic reforming process, a new integrated simulation and optimization framework is presented. First, a detailed mechanism model is established based on a reaction network involving 32 components and 50 reactions, coupled with mass transfer, heat transfer, pressure drop, and catalyst deactivation equations. Then, to solve the differential-algebraic equations in the mechanism model, a multi-objective hybrid optimization method with the adaptive infill strategy is introduced. GAMS and MATLAB are integrated to perform a joint iterative solution. Finally, two cases are conducted with the proposed algorithm. Results show that the mechanism model calculation deviations are below 4 % of reactor temperature, pressure, and composition distribution, and the Pareto front of various production plans is obtained. The accurate simulation and rapid trade-off optimization among the key goals can be achieved to provide scientific decision support for enterprise production.

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.

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.

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.

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...

Modeling a Reaction Section of a Commercial Continuous Catalytic Reformer

Continuous catalytic reforming (CCR) is known to convert refinery naphtha to a high-octane liquid product, also known as the reformate. In this paper, a First Principle Reaction Section Model for a CCR process is presented. Even though CCR is a well-established technology, the application of advanced, real-time optimization techniques that are able to quickly respond to any imposed changes onto the process, are necessary in the refinery business. This becomes particularly important as a result of changes in profit margin, changes in operating cost, and the introduction of new environmental legislations. Hence, we present a kinetic model for the CCR process using the so-called “lumped” concept. The reactors have been modeled using a quasi-steady-state approach. The unknown model parameters have been estimated by bench marking the First Principle Reaction Section results with a commercial CCR process owned by the Hungarian Oil and Gas Public Limited Company (INA-MOL). The proposed model has been tested and ...

Multi-zone parallel-series plug flow reactor model with catalyst deactivation effect for continuous catalytic reforming process

Accurate and reliable modeling of continuous catalytic regenerative (CCR) reforming process plays a significant role not only in product and temperature distributions prediction, but also in real-time optimization and control. In this study, a new multi-zone parallel-series plug flow reactor (PFR) model is proposed for the CCR reformer with four-stage stacked radial flow moving bed reactors (RFMBRs). A 27-lumped kinetic model is used to describe the aromatics aimed CCR reforming process. An empirical catalyst deactivation model is designed to describe the axial catalyst activity distribution information and integrated into the multi-zone parallel-series PFR model. The effectiveness of the proposed model is validated by the industrial plant datasets from a commercial reforming process. The zone number of 4 is reasonably determined by balancing the trade-off between the prediction accuracy and computational cost. Moreover, the detailed distributions of component mass fraction, temperature and catalyst activity are thoroughly investigated by means of the simulation results of the 4-zone parallel-series PFR model. The prediction results illustrate that the proposed model could provide accurate predictions of product and temperature distributions, which indicates that the proposed modeling approach could be greatly helpful for assisting in real-time optimization and control of CCR reforming process.

Multiobjective Optimization of the Industrial Naphtha Catalytic Reforming Process

In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reforming process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics reaction network and has been proved to be quite effective in terms of industrial application. The primary objectives include maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor index temperatures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.

Modeling, Simulation and Optimization of a Whole Industrial Catalytic Naphtha Reforming Process on Aspen Plus Platform

A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus platform. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is optimized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h −1 averagely, which is close to the calculated result.