Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

Highly efficient recovery of waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials using a process involving pyrometallurgy and hydrometallurgy

Substantial environmental and economic benefits can be achieved by recycling used lithium-ion batteries. Hydrometallurgy is often employed to recover waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. As Ni, Co and Mn are transition metals, they exhibit similar properties;therefore, separating them is difficult. Thus, most researchers have focused on leaching processes, while minimal attention has been devoted to the separation of valuable metals from waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. Herein, we propose an environment-friendly, gentle process involving the usage of pyrometallurgy and hydrometallurgy to gradually leach valuable metals and effectively separate them. Interestingly, Li is recovered through a reduction roasting and water leaching process using natural graphite powder, Ni and Co are recovered through ammonia leaching and extraction processes and Mn is recovered through acid leaching and evaporation–crystallization processes. Results show that ~87% Li, 97.01% Co, 97.08% Ni and 99% Mn can be leached using water, ammonia and acid leaching processes. The result obtained using the response surface methodology shows that the concentration of (NH4)2SO3 is a notable factor affecting the leaching of transition metals. Under optimal conditions, ~97.01% Co, 97.08% Ni and 0.64% Mn can be leached out. The decomposition of LiNi_(x)Co_(y)Mn_(z)O_(2) is a two-step process. This study provides valuable insights to develop an environment-friendly, gentle leaching process for efficiently recycling valuable metals, which is vital for the lithium-ion battery recycling industry.

Multifractal modeling of the production of concentrated sugar syrup crystal

High quality, concentrated sugar syrup crystal is produced in a critical step in cane sugar production： the clarification process. It is characterized by two variables： the color of the produced sugar and its clarity degree. We show that the temporal variations of these variables follow power-law distributions and can be well modeled by multiplicative cascade multifractal processes. These interesting properties suggest that the degradation in color and clarity degree has a systemwide cause. In particular, the cascade multifractal model suggests that the degradation in color and clarity degree can be equivalently accounted for by the initial ＂impurities＂ in the sugarcane. Hence, more effective cleaning of the sugarcane before the clarification stage may lead to substantial improvement in the effect of clarification.

Opinion: Chemical--Mesoscopics..for the Mesoparticles Reactivity Explanation

The estimation of chemical particles reactivity and the determination of chemical reactions direction are the actual theme in new scientific trend-Chemical Mesoscopics.Paper includes the proposal about the using the theory of free energy linear dependence from physical organic chemistry and their applications for prognosis of reactions flowing.The semi-empiric constants is given according to mesoscopic physics definitions as well as the transformed Kolmogorov-Avrami equation is discussed.It is the development of Chemical Mesoscopics for organic reactivity estimation including nanostructures reactivity.