Comparative study on pyrolysis characteristics and kinetics of oleaginous yeast and algae

The pyrolysis processes of oleaginous yeast and algae were studied and compared using a non-isothermal thermogravimetric analyzer at heating rates of 10–50 °C/min, and the most probable mechanism function and kinetic analyses of the main stage of pyrolysis were carried out by the Popuse method, Starink method, and Fridemen method. The main pyrolysis stage of the samples could be described by the Jander equation and Z–L–T equation and the activation energy of the three biomass was 108–117, 107–121 and 93–108 kJ/mol, respectively. For the three kinds of biomass, the DTG curves were divided based on the four pseudo-components by performing Gaussian fitting which are carbohydrates, proteins, lipids, others, and the weight coefficients of them could be identified. The activation energy of each pseudo-component was obtained in the range of 58.36–140.44 kJ/mol by the Kissinger method. The four-pseudo-component model based on Gaussian fitting provides effective data for the design of oleaginous yeast and algae thermal decomposition systems and the kinetic analysis of the pyrolysis process.     

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.     

高斯混合模型结合加权似然的目标跟踪算法

鉴于高斯混合模型对背景变化快时无法精确检测出目标和目标跟踪的适应性差等瑕疵，提出了基于加权似然跟踪器来改进高斯混合模型实现运动目标跟踪算法。主要引入了自适应高斯混合模型来实时检测运动目标，然后空间加权似然来进行视频中的目标定位，引入加权似然期望值来改进高斯混合模型处理视频中的多尺度、多角度变化的目标跟踪不精准问题。通过VOT 2014 dataset对比实验结果表明提出的基于加权似然跟踪（Weighted Likelihood Tracking，WLT）和改进高斯混合模型（Improved Gaussian Mixture Model，IGMM）的目标跟踪算法较传统高斯混合模型跟踪算法在跟踪的精度有较大提高。在应对多尺度、多角度变化的目标跟踪表现出了较大的优势。     

System identification with measurement noise compensation based on polynomial modulating function for fractional-order systems with a known time-delay

This study presents a system identification method based on polynomial modulating function for fractional-order systems with a known time-delay involving input and output noises in the time domain. Based on the polynomial modulating function and fractional-order integration by parts, the identified fractional-order differential equation is transformed into an algebraic equation. By using the numerical integral formula, the least squares form for the system identification is obtained. In order to reduce the effect of noises existing in the input and output measurements, the compensation method for the input and output noises is also studied by introducing an auxiliary high-order fractional-order system in the revised identification algorithm. Finally, the effectiveness of the proposed algorithm is verified by the simulation result of an illustrative example and the experimental result of temperature identification for a thermal system.     

Toward a continuous synthesis of porous carbon xerogel beads

A continuous process for producing porous carbon xerogel beads has been developed. It consists in injecting a pre‐cured aqueous solution of resorcinol and formaldehyde on top of a column filled with hot oleic acid. The latter is pumped on the top of the column and fed at the bottom, generating an upward flow that can be adjusted to match the terminal velocity of the settling beads. Thus, the bead residence time in the column can be adjusted to match the gelation time, allowing the beads to solidify before reaching the bottom of the vessel. The obtained beads are subsequently dried and pyrolyzed. The developed experimental setup proved the continuous synthesis of porous carbon beads is possible. Nevertheless, the shaping process caused various texture changes of the porous carbon, which mainly yields macropores instead of micro and mesopores. This process also leads to the build‐up of a denser skin around the beads. © 2018 American Institute of Chemical Engineers AIChE J, 64: 1049–1058, 2018     

An enhanced state estimation for linear parabolic PDE systems with mobile sensors

This paper studies an enhanced state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing an performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make the performance bound as small as possible, a suboptimal enhanced state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.     

An enhancement algorithm based on adaptive updating template with Gaussian model for Si3N4 ceramic bearing roller surface defects detection

The method based on machine vision image processing is used to detect the surface defects of Si₃N₄ bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si₃N₄ bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

Generation-recombination in disordered organic semiconductor: Application to the characterization of traps

The presence of traps in organic semiconductor based electronic devices affects considerably their performances and their stability. The Shockley-Read-Hall (SRH) model is generally used to extract the trap parameters from the experimental results. In this paper, we propose to adapt the SRH formalism to disordered organic semiconductors by considering a hopping transport process and Gaussian distributions for both mobile and trapped carriers. The model is used to extract multiple trap parameters from charge based Deep Level Transient Spectroscopy (Q-DLTS) spectrum. Calculation of the charge transients are given in detail. The model predicts that the activation energy of the trap should not follow an Arrhenius plot on large temperature ranges. Also, the charge transients are no longer exponential when considering Gaussian trap distributions, enlarging the Q-DLTS peaks. The model fits the Q-DLTS spectra measured on organic diodes with a limited number of trap contributions with a good agreement. It is found that an increase of the material rate of disorder reduces the extracted trap energy distances to the LUMO but has no influence on the extracted trap distribution widths. This work shows the importance of considering the specific properties of organic materials to study their properties and their trap distributions.