Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

Numerical Investigation into the Effect of Air Voids on the Anisotropy of Asphalt Mixtures

The aim of this study is to investigate the asphalt mixture anisotropy of both the modulus and Poisson＇s ratio due to air voids using a discrete element modeling simulation method. Three three-dimensional cubic digital samples of asphalt mixture with different shapes of single air void were built using discrete element software PFC^（3D）. The aggregate gradation, air voids and mastic included in the digital samples were modeled using different contact models, with due consideration of the volumetric fractions of the different phases. Laboratory uniaxial complex modulus test and indirect tensile strength test were conducted to obtain material input parameters for numerical modeling. Simulation of the uniaxial cyclic compressive tests was performed on the three cubic samples loaded in three different directions. Dynamic modulus in three directions and Poisson＇s ratio in six directions were calculated from the compression stress-strain responses. Results show that both the modulus and Poisson＇s ratio are dependent on the preferential orientation of air voids. The anisotropy of the modulus and Poisson＇s ratio increases as the pressure loading on the asphalt mixture increases. Compared to the modulus, Poisson＇s ratio due to air voids has been shown to be more anisotropic. The maximum of Poisson＇s ratio and modulus is shown to be up to 80% and 11% higher than the minimum, respectively.

Structural relaxation and glass transition behavior of binary hard-ellipse mixtures

Structural relaxation and glass transition in binary hard-spherical particle mixtures have been reported to exhibit unusual features depending on the size disparity and composition. However, the mechanism by which the mixing effects lead to these features and whether these features are universal for particles with anisotropic geometries remains unclear. Here, we employ event-driven molecular dynamics simulation to investigate the dynamical and structural properties of binary two-dimensional hard-ellipse mixtures. We find that the relaxation dynamics for translational degrees of freedom exhibit equivalent trends as those observed in binary hard-spherical mixtures. However, the glass transition densities for translational and rotational degrees of freedom present different dependencies on size disparity and composition. Furthermore,we propose a mechanism based on structural properties that explain the observed mixing effects and decoupling behavior between translational and rotational motions in binary hard-ellipse systems.