A novel soft-sensing method for quality parameters of aviation kerosene in atmospheric distillation column based on least absolute shrinkage and selection operator and particle swarm optimization deep belief network (LASSO-PSO-DBN) is proposed. First, to reduce the dimension of the input variables, the least absolute shrinkage and selection operator (LASSO) algorithm is used to select the input variables that are irrelevant to the soft sensor of aviation kerosene quality parameters. Then, to improve the generalization of soft sensor model, a deep learning algorithm, deep belief network (DBN), is proposed for soft sensing of aviation kerosene quality parameters. Considering that the structure characteristics and parameters of DBN algorithm have a great impact on the learning and prediction results, the parameters of DBN are optimized based on particle swarm optimization (PSO) algorithm. The benchmark data sets and the industrial atmospheric distillation column data are used for simulation analysis and evaluation of the soft-sensing performance. The simulation results show that the novel proposed algorithm can effectively reduce the dimension of the input variables and simplify the structure of the soft sensor model. It also has good generalization ability and the predicted value is in good agreement with the actual measured value.

A novel internal model control (IMC) method for wave rotor refrigeration process (WRRP) based on Harris hawks optimization (HHO) is presented in this paper. Firstly, an identification model is established by the test data of the WRRP. In order to reduce the complexity of the model, the Routh order reduction method was used to reduce the order of the identification model. This procedure yields an approximate second order model with time delay for the WRRP. Secondly, an IMC method based on HHO is proposed after analyzing the time delay characteristics of the WRRP model. This method improves the traditional IMC structure, and introduces a three degree of freedom IMC structure. The parameters of the controller are optimized by HHO to make the comprehensive performance index of the system reach the best. Simulation results demonstrate that the proposed method can achieve a good performance of tracking and disturbance rejection.

Aiming at the difficulty of temperature control and disturbance rejection control in wave rotor refrigeration process (WRRP), a novel whale optimization two‐degree‐of‐freedom (2DOF) Smith predictor based on feedforward compensation method (WO‐2DOFSP‐FC) is proposed. Firstly, the obtained test data of the WRRP are used for system identification and the approximate second‐order model with time‐delay is obtained by using the suboptimal reduction algorithm. This procedure also yields a first‐order model of the disturbance. Then, the whale optimization 2DOF Smith predictor based on feedforward compensation (2DOFSP‐FC) method is proposed. This proposed method improves the 2DOFSP and introduces feedforward compensation control to reduce the influence of the measurable disturbance on the temperature control system. In addition, the Whale Optimization Algorithm (WOA) is proposed to optimally tune the 2DOFSP‐FC controller parameters. Finally, the simulation results show that the proposed WO‐2DOFSP‐FC method can make the controlled system have good characteristics of both set value tracking and disturbance rejection.