鲕状赤铁矿微波碳热还原-磁选提铁脱磷实验研究

采用微波碳热还原-磁选工艺对鲕状赤铁矿提铁脱磷进行了研究,考察了还原温度、碱度、添加剂用量和原矿粒度等因素对提铁脱磷效果的影响。结果表明,最佳还原条件为:还原温度1 150 ℃、碱度0.8、配碳量1.0、钠盐添加剂用量15%、原矿粒度0.15 mm;将还原所得球团磨至-0.15 mm,在65 mT磁场强度下磁选,可得到全铁含量87.98%、铁回收率95.48%、脱磷率69.42%的指标。     

Experimental Fuzzy/Split-Range Control: Novel Strategy for Biodiesel Batch Reactor Temperature Control

With respect to environmental concerns, biofuels have been used around the world, such as biodiesel, and any improvement in the biodiesel production process is desirable. This paper proposes three different control systems based on fuzzy logic and output control signal split-range. Regulating reaction temperature with low settling time and process variable oscillation are the process control aims, and smooth output control signal for actuators protection is also desired. An automated biodiesel production prototype was used and the experimental results demonstrate that a fuzzy/split-range structure with three input variables showed the best performance, resulting in process variable regulation in set-point value with small settling time and actuators premature wearing prevention.     

Methods for uncertainty propagation in life cycle assessment

Life cycle assessment (LCA) calculates the environmental impact of a product over its entire life cycle. Uncertainty analysis is an important aspect in LCA, and is usually performed using Monte Carlo sampling. In this study, Monte Carlo sampling, Latin hypercube sampling, quasi Monte Carlo sampling, analytical uncertainty propagation and fuzzy interval arithmetic were compared based on e.g. convergence rate and output statistics. Each method was tested on three LCA case studies, which differed in size and behaviour. Uncertainty propagation in LCA using a sampling method leads to more (directly) usable information compared to fuzzy interval arithmetic or analytical uncertainty propagation. Latin hypercube and quasi Monte Carlo sampling provide more accuracy in determining the sample mean than Monte Carlo sampling and can even converge faster than Monte Carlo sampling for some of the case studies discussed in this paper.     

Exploring stop-go decision zones at rural high-speed intersections with flashing green signal and insufficient yellow time in China

The objective of this study is to empirically analyze and model the stop-go decision behavior of drivers at rural high-speed intersections in China, where a flashing green signal of 3 s followed by a yellow signal of 3 s is commonly applied to end a green phase. 1, 186 high-resolution vehicle trajectories were collected at four typical high-speed intersection approaches in Shanghai and used for the identification of actual stop-go decision zones and the modeling of stop-go decision behavior. Results indicate that the presence of flashing green significantly changed the theoretical decision zones based on the conventional Dilemma Zone theory. The actual stop-go decision zones at the study intersections were thus formulated and identified based on the empirical data. Binary Logistic model and Fuzzy Logic model were then developed to further explore the impacts of flashing green on the stop-go behavior of drivers. It was found that the Fuzzy Logic model could produce comparably good estimation results as compared to the traditional Binary Logistic models. The findings of this study could contribute the development of effective dilemma zone protection strategies, the improvement of stop-go decision model embedded in the microscopic traffic simulation software and the proper design of signal change and clearance intervals at high-speed intersections in China.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.     

Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems

This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The system relies on multiple visual cues to characterize the level of alertness of the driver. The parameters used for detecting fatigue are: eye closure duration measured through eye state information and yawning analyzed through mouth state information. Initially, the face is located through Viola–Jones face detection method to ensure the presence of driver in video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. Simultaneously, the pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle. The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. The system has been tested using real data, with different sequences recorded in day and night driving conditions, and with users belonging to different race and gender. The system yielded an average accuracy of 100% on all the videos on which it was tested.     

Effect of bacterial inoculum ratio in mixed culture for decolourization and detoxification of pulp paper mill effluent

BACKGROUND: Effluent released from industry is a mixture of various pollutants. For the degradation of complex pollutants, mixed bacterial cultures can be more effective than a single culture. This study investigated the balance of bacterial populations in a mixed culture for maximum reduction of pollutants. RESULTS: This study deals with the degradation and detoxification of pulp paper mill effluent (PPME) by three bacterial strains, i.e. Serratia marcescens, Serratia liquefaciens and Bacillus cereus in different ratios, and found that two ratios, 4:1:1 and 1:4:1, were effective for the degradation of PPME. These ratios reduced the various pollution parameters. Enzyme bioassay revealed that more enzyme was produced during degradation for the ratio 4:1:1. High performance liquid chromatography (HPLC) analysis showed that the ratio 4:1:1 degraded 95% of lignin and related compounds, and chlorophenols up to 98%, whereas ratio 1:4:1 reduced lignin by 84% and chlorophenols by 58% after 7 days incubation. Degradation products were confirmed by gas chromatography–mass spectrometry (GC‐MS) analysis. A seed germination bioassay on Phaseolous mungo L. revealed that toxicity was reduced by the ratio 4:1:1. CONCLUSION: Due to variable potential of different bacteria show variation in their growth pattern at any contaminated site. This study shows that an appropriate ratio of mixed cultures is required for maximum degradation and detoxification of PPME. Copyright © 2012 Society of Chemical Industry     

whittle软件境界优化设计的理念与技术方法

介绍了国外设计咨询公司处理矿石损失贫化及构建采矿模型的理念与方法,并对whittle软件优化境界的概念及技术进行了详细论证。     

Application of fuzzy-analytic hierarchy process algorithm and fuzzy load profile for load shedding in power systems

In an electrical competitive market, load shedding decision support systems are needed to find the ways to process load shedding to satisfy both economic and technical conditions. This paper proposed the Fuzzy Analytic Hierarchy Process algorithm (Fuzzy-AHP) and fuzzy logic for load profile approach to determine the weight of the load nodes of the system and select the control strategy when the system operates at various load levels. Weights of the load nodes were calculated by triangular fuzzy numbers in the pair comparison. These results combined with the fuzzy logic for load profile to draw out the suitable control strategies for various load levels in case of emergency. The effectiveness of the algorithm was demonstrated through the experiment of load shedding of IEEE 37 bus compared with traditional AHP algorithm.