Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

THE STUDIES ON THE SEPARATION AND IDENTIFICATION OF EPHEDRINES IN URINE BY GC/MSD

The separation and identification of the ephedrines(cathine,norephe-drine,ephedrine,pseudoephedrine,methylephedrine and ethylephedrine)and their derivatives obtained from TFAA,MSTFA,MSTFA+MBTFA and MSTFA+Ethyl acetate deriva- tization were carried out by GC/MSD.

Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals

A novel separation identification strategy for the neural fuzzy Wiener–Hammerstein system using hybrid signals is developed in this study.The Wiener–Hammerstein system is described by a model consisting of two linear dynamic elements with a nonlinear static element in between.The static nonlinear element is modeled by a neural fuzzy network(NFN)and the two linear dynamic elements are modeled by an autoregressive exogenous(ARX)model and an autoregressive(AR)model,separately.When the system input is Gaussian signals,the correlation technique is used to decouple the identification of the two linear dynamic elements from the nonlinear element.First,based on the input and output of Gaussian signals,the correlation analysis technique is used to identify the input linear element and output linear element,which addresses the problem that the intermediate variable information cannot be measured in the identified Wiener–Hammerstein system.Then,a zero-pole match method is adopted to separate the parameters of the two linear elements.Furthermore,the recursive least-squares technique is used to identify the nonlinear element based on the input and output of random signals,which avoids the impact of output noise.The feasibility of the presented identification technique is demonstrated by an illustrative simulation example and a practical nonlinear process.Simulation results show that the proposed strategy can obtain higher identification precision than existing identification algorithms.

Isolation and Experimental Study on Bacteriostasis of Bacillus haynesii

This experiment aims to isolate and inhibit three bacteria strains to provide candidate strains for the development and application of probiotics.Using bacterial morphological identification,16S rDNA sequence alignment,and genetic evolution analysis,three strains were identified as Bacillus haynesii,named HP01,HD02,and HK03.Through biosurfactant activity tests,C-TAB tests,hemolysis tests,and antibacterial activity analyses,the results showed that all three strains of B.haynesii exhibited significant biosurfactant activity.Additionally,the solutions of the three strains demonstrated a pronounced antibacterial effect on Staphylococcus aureus.The resistance and safety of commonly used drugs were evaluated using the tablet diffusion method and a mouse feeding test.The results indicated that the three strains were not resistant to commonly used antibacterial drugs,and the oral bacterial solution was not pathogenic and had high safety in mice.The study concluded that all three B.haynesii strains met the basic conditions for use,with B.haynesii HP01 being the most promising candidate.

奶粉中脂肪酸的气相色谱-质谱分析

To determine the fatty acids in milk powders,the fatty oils of milk powders were extracted by Rose-Gettlieb method,the fatty acids in the fatty oils were esterified by potassium hydroxide-methanol,and then analyzed by gas chromatography-mass spectrometry (GC-MS).The results indicate that main fatty acids’ carbon chain were from 8 to 24,each fatty acid has different conents in different samples and the content was determined by area normalization.

An efficient equivariant adaptive separation via independence algorithm for acoustical source separation and identification

To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.