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In order to meet the requirements of variable transmission ratios and improve the bearing capacity of gear teeth, a new type of double-circular-arc tooth profile elliptical gear is proposed based on the meshing theory and the structural parameter characteristics of the double-circular-arc tooth circular profile curve. The design method of its pitch curve is described. A three-dimensional model of the double-circular-arc tooth circular profile elliptical gear is established using SolidWorks software. Adams software is used to simulate the kinematics of the elliptical gear pair model with double-circular-arc tooth profiles, and the influence of different eccentricities on the variation rule of the double-circular-arc gear pair transmission ratio is analyzed; compared with the theoretical transmission ratio curve, the influencing factors of the fluctuation error of the simulation transmission ratio curve are analyzed. In order to reduce the vibration pulse of the gear pair, the appropriate value range of eccentricities is given. The design method and analysis results in this study have theoretical reference value for parametric design of non-circular gears with double-circular-arc tooth profiles, and can provide a basis for NC manufacturing and application of elliptic gear pairs with double-circular-arc tooth profiles.

To address the significant soft measurement errors in traditional static models for evaporation process parameters, which are characterized by continuity and cumulativity, this paper proposes a dynamic correction method for soft measurement models of evaporation process parameters based on the autoregressive moving-average model (ARMA). Initially, the Powell's directional evolution (Powell-DE) algorithm is utilized to identify the autoregressive order and moving average order of the ARMA model. Subsequently, the prediction error of a mechanism-reduced robust least squares support vector machine ensemble model is utilized as input. An error time series prediction model, which compensates for the errors in the autoregressive moving average model, is then applied for dynamic estimation of the prediction error. Finally, an integration strategy using the entropy method is employed to combine the static soft measurement model, based on the mechanism-reduced robust least squares support vector machine, with the dynamic correction soft measurement model, which is based on the error time series compensation of the ARMA model. The new model is analyzed and validated using production data from an alumina plant's evaporation process. Compared to traditional models, the new model demonstrates significantly improved prediction accuracy and is capable of dynamic prediction of evaporation process parameters.