The fluidized catalytic cracking (FCC) unit is the primary production unit for gasoline and olefins in refineries. Improving product yield and processing capacity is closely related to the economic benefits of refineries. In this paper, we use the Aspen HYSYS software to model the mechanism of an 800 kt/a FCC unit in China. The simulation of the FCC unit based on the 21-lump models and the simulation results are in good agreement with the actual factory production data. Then we find out the optimization method to improve the product yield and processing capacity through the model research. The results of the study indicate the following optimization plans: when keeping the riser outlet temperature (ROT) in the range of 535~545 °C, it can increase the yield of gasoline and liquefied petroleum gas (LPG) by up to 5%; when keeping the riser outlet temperature (ROT) in the range of 510~520 °C, it can increase the yield of gasoline and diesel by up to 0.5%; the optimal ROT of control temperature should be 530 °C when ensuring the same gasoline yield and increasing processing capacity, it can increase the feed rate by 14.3% which in turn increases gasoline production. Through the above plans, the refinery can achieve the production goals of yield and capacity. Therefore, the refinery can get more profits.

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers.