Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydro...Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation (EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.展开更多
In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped ki...In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics re-action network and has been proved to be quite effective in terms of industrial application. The primary objectives in-clude maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet tem-peratures, 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.展开更多
This research work developed a model for the MIP riser reactor using the data collected from an industrial MIP unit.Based on analysis of flow patterns in the reactor,three models were established and a comparison was ...This research work developed a model for the MIP riser reactor using the data collected from an industrial MIP unit.Based on analysis of flow patterns in the reactor,three models were established and a comparison was made on each other.The results indicated that Model Ⅲ,which was assumed a plug flow in the first reaction zone and a gas plug flow and a continuously stirred catalyst flow in the second reaction zone,was the best.The results of this research could offer an information and guidance for optimization and development of MIP unit.展开更多
Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning a...Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in FCC.This paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up research.Compared with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large delay.AI methods applied in product yield analysis build models based on massive data.By fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model accuracy.AI methods applied in flue gas desulfurization can be usually divided into two stages:modeling and optimization.In the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base.AI methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition warning.Knowledge-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction recall.While the data-driven methods are just the opposite.Thus,some studies combine these two methods to obtain better results.展开更多
Hydrogenation technology is an indispensable chemical upgrading process for converting the heavy feedstock into favorable lighter products.In this work,a new kinetic model containing four hydrocarbon lumps(feedstock,d...Hydrogenation technology is an indispensable chemical upgrading process for converting the heavy feedstock into favorable lighter products.In this work,a new kinetic model containing four hydrocarbon lumps(feedstock,diesel,gasoline,cracking gas)was developed to describe the coal tar hydrogenation process,the Levenberg–Marquardt’s optimization algorithm was used to determine the kinetic parameters by minimizing the sum of square errors between experimental and calculated data,the predictions from model validation showed a good agreement with experimental values.Subsequently,an adiabatic reactor model based on proposed lumped kinetic model was constructed to further investigate the performance of hydrogenation fixed-bed units,the mass balance and energy balance within the phases in the reactor were taken into accounts in the form of ordinary differential equation.An application of the reactor model was performed for simulating the actual bench-scale plant of coal tar hydrogenation,the simulated results on the products yields and temperatures distribution along with the reactor are shown to be good consistent with the experimental data.展开更多
A mathematical model has been developed for the simulation of gas-particle flow and fluid catalytic cracking in downer reactors. The model takes into account both cracking reaction and flow behavior through a four-lum...A mathematical model has been developed for the simulation of gas-particle flow and fluid catalytic cracking in downer reactors. The model takes into account both cracking reaction and flow behavior through a four-lump reaction kinetics coupled with two-phase turbulent flow. The prediction results show that the relatively large change of gas velocity affects directly the axial distribution of solids velocity and void fraction, which significantly interact with the chemical reaction. Furthermore, model simulations are carried out to determine the effects of such parameters on product yields, as bed diameter, reaction temperature and the ratio of catalyst to oil, which are helpful for optimizing the yields of desired products. The model equations are coded and solved on CFX4.4.展开更多
基金supported by National Natural Science Foundation of China(Basic Science Center Program:61988101)National Natural Science Foundation of China(62394345,62373155,62173147)the Major Science and Technology Project of Xinjiang(No.2022A01006-4).
文摘Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation (EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.
基金Supported by the National Natural Science Foundation of China (No.60421002).
文摘In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics re-action network and has been proved to be quite effective in terms of industrial application. The primary objectives in-clude maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet tem-peratures, 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.
文摘This research work developed a model for the MIP riser reactor using the data collected from an industrial MIP unit.Based on analysis of flow patterns in the reactor,three models were established and a comparison was made on each other.The results indicated that Model Ⅲ,which was assumed a plug flow in the first reaction zone and a gas plug flow and a continuously stirred catalyst flow in the second reaction zone,was the best.The results of this research could offer an information and guidance for optimization and development of MIP unit.
基金the State Key Program of National Science Foundation of China(No.61836006)the National Natural Science Fund for Distinguished Young Scholar(No.61625204)+1 种基金the National Natural Science Foundation of China(Nos.62106161 and 61602328)the Key Research and Development Project of Sichuan(No.2019YFG0494).
文摘Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in FCC.This paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up research.Compared with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large delay.AI methods applied in product yield analysis build models based on massive data.By fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model accuracy.AI methods applied in flue gas desulfurization can be usually divided into two stages:modeling and optimization.In the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base.AI methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition warning.Knowledge-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction recall.While the data-driven methods are just the opposite.Thus,some studies combine these two methods to obtain better results.
基金the Joint Funds of the National Natural Science Foundation of China(PRIKY15013 and 2015D-5006-0406)the National Natural Science Foundation of China(No.21808223).
文摘Hydrogenation technology is an indispensable chemical upgrading process for converting the heavy feedstock into favorable lighter products.In this work,a new kinetic model containing four hydrocarbon lumps(feedstock,diesel,gasoline,cracking gas)was developed to describe the coal tar hydrogenation process,the Levenberg–Marquardt’s optimization algorithm was used to determine the kinetic parameters by minimizing the sum of square errors between experimental and calculated data,the predictions from model validation showed a good agreement with experimental values.Subsequently,an adiabatic reactor model based on proposed lumped kinetic model was constructed to further investigate the performance of hydrogenation fixed-bed units,the mass balance and energy balance within the phases in the reactor were taken into accounts in the form of ordinary differential equation.An application of the reactor model was performed for simulating the actual bench-scale plant of coal tar hydrogenation,the simulated results on the products yields and temperatures distribution along with the reactor are shown to be good consistent with the experimental data.
基金the Natura Science Foundation of China under contract number:20176024
文摘A mathematical model has been developed for the simulation of gas-particle flow and fluid catalytic cracking in downer reactors. The model takes into account both cracking reaction and flow behavior through a four-lump reaction kinetics coupled with two-phase turbulent flow. The prediction results show that the relatively large change of gas velocity affects directly the axial distribution of solids velocity and void fraction, which significantly interact with the chemical reaction. Furthermore, model simulations are carried out to determine the effects of such parameters on product yields, as bed diameter, reaction temperature and the ratio of catalyst to oil, which are helpful for optimizing the yields of desired products. The model equations are coded and solved on CFX4.4.
