A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.

Decoupling Control of Fluid Catalytic Cracking Unit

This study presents a decoupling control scheme of fluid catalytic cracking unit to account for the high interaction between two temperature control loops. The feed flow rate load disturbance is introduced to test the proposed decoupling control scheme. Through simulation study shown that the decoupling is effective, stable and it presents advantage over controller without decoupler. Also, this scheme is able to offer good dynamic performance for most disturbances.

Failure analysis of corrosion cracking and simulated testing for a fluid catalytic cracking unit

The failure of a fluid catalysis and cracking unit (FCCU) in a Chinese refinery was investigated by using nondestructive detection methods, fracture surface examination, hardness measurement, chemical composition and corrosion products analysis. The results showed that the failure was caused by the dew point nitrate stress corrosion cracking. For a long operation period, the wall temperature of the regenerator in the FCCU was below the fume dew point. As a result, an acid fume NOx-SOx-H2O medium present- ed on the surface, resulting in stress corrosion cracking of the component with high residual stress. In order to confirm the relative conclusion, simulated testing was conducted in laboratory, and the results showed similar cracking characteristics. Finally, some sug- gestions have been made to prevent the stress corrosion cracking of an FCCU from re-occurring in the future.

Study on Commercial Application of FP-DSN Sulfur Transfer Additive in FCC Unit

The FCC unit with addition of various inventories of the FP-DSN type sulfur transfer additive was tested in a commercial scale. The effect of the sulfur transfer additive was analyzed by investigating the indicators related with the regenerator flue gas composition,the dry gas composition before desulfurization,the LPG composition before desulfurization,the acid gas,and the yield of gasoline and diesel. The test results indicated that the sulfur was trans ferred from the feed stream into the dry gas,LPG and acid gas,and the sulfur transfer effect was obvious only when the inventory of sulfur transfer additive exceeded over 2.0% of total FCC catalyst inventory.

Influence of Design Margin on Operation Optimization and Control Performance of Chemical Processes

Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected.

Effect of CO Combustion Promoters on Combustion Air Partition in FCC under Nearly Complete Combustion

With CO combustion promoters, the role of combustion air flow rate for concerns of economics and control is important. The combustion air is conceptually divided to three parts: the air consumed by coke burning,the air consumed by CO combustion and the air unreacted. A mathematical model of a fluid catalytic cracking(FCC)unit, which includes a quantitative correlation of CO heterogeneous combustion and the amount of CO combustion promoters, is introduced to investigate the effects of promoters on the three parts of combustion air. The results show that the air consumed by coke burning is almost linear to combustion air flow rate, while the air consumed by CO combustion promoters tends to saturate as combustion air flow rate increases, indicating that higher air flow rate can only be used as a manipulated variable to control the oxygen content for an economic concern.

Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes

Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.

Modeling and optimum operating conditions for FCCU using artificial neural network

A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.

Multivariate Statistical Process Monitoring Using Robust Nonlinear Principal Component Analysis

The principal component analysis （PCA） algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the nonlinear PCA （NLPCA） using autoassociative bottle-neck neural networks is so sensitive that the obtained model differs significantly from the underlying system. In this paper, a robust version of NLPCA is introduced by replacing the generally used error criterion mean squared error with a mean log squared error. This is followed by a concise analysis of the corresponding training method. A novel multivariate statistical process monitoring （MSPM） scheme incorporating the proposed robust NLPCA technique is then investigated and its efficiency is assessed through application to an industrial fluidized catalytic cracking plant. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms and is, hence, expected to better monitor real-world processes.