Effects of on-line solution and off-line heat treatment on microstructure and hardness of die-cast AZ91D alloy

The effects of on-line solution, off-line solution and aging heat treatment on the microstructure and hardness of the die-cast AZ91D alloys were investigated. Brinell hardness of die-cast AZ91D alloy increases through on-line solution and off-line aging treatment but decreases after off-line solution treatment. By X-ray diffractometry, optical microscopy, differential thermal analysis, scanning electron microscopy and X-ray energy dispersive spectroscopy, it is found that the microstructures of the die-cast AZ91D magnesium alloy before and after on-line solution and off-line aging are similar, consisting of α-Mg and β-Al12Mg17. The precipitation of Al element is prevented by on-line solution so that the effect of solid solution strengthening is enhanced. The β-Al12Mg17 phases precipitate from supersaturated Mg solid solution after off-line aging treatment, and lead to microstructure refinement of AZ91D alloy, so the effect of precipitation hardening is enhanced. The β-Al12Mg17 phases dissolve in the substructure after off-line solution treatment, which leads to that the grain boundary strengthening phase is reduced significantly and the hardness of die cast AZ91D is reduced.

Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

Measurement methods of particle size distribution in emulsion polymerization

The particle size distribution of polymer always develops in emulsion polymerization systems,and certain key phenomena/mechanisms as well as properties of the final product are significantly affected by this distribution.This review mainly focuses on the measurement methods of particle size distribution rather than average particle size during the emulsion polymerization process,including the existing off-line,on-line,and in-line measurement methods.Moreover,the principle,resolution,performance,advantages,and drawbacks of various methods for evaluating particle size distribution are contrasted and illustrated.Besides,several possible development directions or solutions of the in-line measurement technology are explored.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

Real-time retrieval of submicron aerosol size distributions from multi-wavelength spectral extinction measurements

The real-time retrieval of submicron aerosol size distributions is of major interest for applications. Based on the Mie theory,the spectral extinction method offers a simple measurement principle and a convenient optical arrangement. In contrast to the relative simplicity of the experimental measurement the retrieval of the particles size distribution and particle concentration from the spectral extinction method is difficult. Mie scattering Equation is a Fredholm Integral Equation of the First Kind. This paper develops a hybrid iterative model-dependent algorithm for on-line particle sizing from extinction spectra which is both computationally efficient and accurate. Applying the refined Mie diagnostic iterative procedures within some candidate solutions can identify the unique result accurately and rapidly enough for real time measurement. With the addition of added Gaussian noise,an average tolerance up to 5% of noise level is kept for particle size from submicron to micrometer under moderate polydispersity.

Recent Progress of Commercially Available Biosensors in China and Their Applications in Fermentation Processes

Biosensors, which are the products of the biotechnology industry, are among the key projects of the 7th, 8th, and 9th Fiveyear Plans of China Science ＆ Technology Developing Programs, respectively, and they play an important role in developing and reforming traditional biotechnology. SBA series biosensor analyzer, as the only one commercial biosensor in China, has attracted lots of attention in the process of information gathering and measurement for biological industry with the development of technology and society. In this paper, we presented an overview of the most important contributions dealing with the monitoring of the biochemical analytes in fermentation processes using SBA series biosensor analyzers in China. Future trends of the biosensor analyzer in China were also mentioned in the last section.

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches

Traditional Chinese medicines(TCMs)possess a rich historical background,unique theoretical framework,remarkable therapeutic efficacy,and abundant resources.However,the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms.To gain deeper insights into the phytochemicals and ensure the quality control of TCMs,there is an urgent need to enhance analytical techniques.Currently,two-dimensional(2D)chromatography,which incorporates two independent separation mechanisms,demonstrates superior separation capabilities compared to the traditional one-dimensional(1D)separation system when analyzing TCMs samples.Over the past decade,new techniques have been continuously developed to gain actionable insights from complex samples.This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs,encompassing 2D-gas chromatography(GC),2D-liquid chromatography(LC),as well as emerging three-dimensional(3D)-GC,3D-LC,and their associated data-processing approaches.These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis.Nevertheless,the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools.Considering that multidimensional chromatography is still a relatively nascent research field,further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.

Towards online optimisation of solid oxide fuel cell performance: Combining deep learning with multi-physics simulation

The use of solid oxide fuel cells(SOFCs)is a promising approach towards achieving sustainable electricity pro-duction from fuel.The utilisation of the hydrocarbons and biomass in SOFCs is particularly attractive owing to their wide distribution,high energy density,and low price.The long-term operation of SOFCs using such fuels remains difficult owing to a lack of an effective diagnosis and optimisation system,which requires not only a precise analysis but also a fast response.In this study,we developed a hybrid model for an on-line analysis of SOFCs at the cell level.The model combines a multi-physics simulation(MPS)and deep learning,overcoming the complexity of MPS for a model-based control system,and reducing the cost of building a database(compared with the experiments)for the training of a deep neural network.The maximum temperature gradient and heat generation are two target parameters for an efficient operation of SOFCs.The results show that a precise predic-tion can be achieved from a trained AI algorithm,in which the relative error between the MPS and AI models is less than 1%.Moreover,an online optimisation is realised using a genetic algorithm,achieving the maximum power density within the limitations of the temperature gradient and operating conditions.This method can also be applied to the prediction and optimisation of other non-liner,dynamic systems.