Predicting the amount of coke deposition on catalyst through image analysis and soft computing

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis

Early Detection of the Wear of Coriolis Flowmeters through In Situ Stiffness Diagnosis

Coriolis flowmeters have been widely employed in a variety of industrial applications. There is a potential that the measuring tube of a Coriolis flowmeter may be eroded when it is used to measure abrasive fluid such as slurry flow. However, it is challenging to verify the structural health of the flowmeter without process interruptions or using on-site calibration devices such as meter provers. This paper presents an in-situ structural health monitoring technique through stiffness diagnosis to identify the potential wear occurring on the measuring tube. To measure the frequency response of a Coriolis flowmeter which strongly depends on the structural characteristics of the tube, the tube is not only excited at a resonant frequency but also at two additional off-resonant frequencies. Through digital processing of the drive and sensor signals, the frequency response is obtained and a stiffness related diagnostic parameter (SRDP) is extracted from a Coriolis flowmeter. The proposed stiffness diagnosis technique was experimentally evaluated on a commercial bent-tube Coriolis flowmeter with dilute sand-water slurry flow. The results illustrate that the slight tube erosion is successfully identified when a relative change in SRDP reaches −1%, showing a good capability for an early detection of tube wear. In addition, the outcomes from recalibration with water suggest that, after the erosion occurs, the flowmeter overestimates the mass flowrate and underestimates the flow density

A Deep Learning-enhanced Digital Twin Framework for Improving Safety and Reliability in Human-Robot Collaborative Manufacturing

In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human–robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our framework, a fully-supervised detector based on a faster region-based convolutional neural network is firstly trained on synthetic data generated by the Digital Twin, and then tested on the physical system to demonstrate the effectiveness of the proposed Digital Twin-based framework. To ensure safety and reliability, a semi-supervised detector is further designed to bridge the gap between the twin system and the physical system, and improved performance is achieved by the semi-supervised detector compared to the fully-supervised detector that is simply trained on either synthetic data or real data. The evaluation of the framework in multiple scenarios in which human operators collaborate with a Universal Robot 10 shows that it can accurately detect the human and robot, and classify their actions under a variety of conditions. The data from this evaluation have been made publicly available, and can be widely used for research and operational purposes. Additionally, a semi-automated annotation tool from the Digital Twin framework is published to benefit the collaborative robotics community

Temperature Measurement of Stored Biomass Using Low-frequency Acoustic Waves and Correlation Signal Processing Techniques

As a substitute of traditional fossil fuels, biomass is widely used to generate electricity and heat. The temperature of stored biomass needs to be monitored continuously to prevent the biomass from self-ignition. This paper proposes a non-intrusive method for the temperature measurement of stored biomass based on acoustic sensing techniques. A characteristic factor is introduced to obtain the sound speed in free space from the measured time of flight of acoustic waves in stored biomass. After analysing the relationship between the defined characteristic factor and air temperature, an updating procedure on the characteristic factor is proposed to reduce the influence of air temperature. By measuring the sound speed in free space air temperature is determined which is the same as biomass temperature. The proposed methodology is examined using a single path acoustic system which consists of a source and two sensors. A linear chirp signal with a duration of 0.1 s and frequencies of 200-500 Hz is generated and transmitted through stored biomass pellets. The time of flight of sound waves between the two acoustic sensors is measured through correlation signal processing. The relative error of measurement results using the proposed method is no more than 4.5% over the temperature range from 22? to 48.9?. Factors that affect the temperature measurement are investigated and quantified. The experimental results indicate that the proposed technique is effective for the temperature measurement of stored biomass with a maximum error of 1.5? under all test conditions

Long-Term Exposure to Silica Dust and Risk of Total and Cause-Specific Mortality in Chinese Workers: A Cohort Study

A retro-prospective cohort study by Weihong Chen and colleagues provides new estimates for the risk of total and cause-specific mortality due to long-term silica dust exposure among Chinese workers

Slurry Flow Measurement Using Coriolis Flowmeters

This thesis describes a novel methodology for slurry flow measurement using Coriolis flowmeters incorporating error compensation and structural condition monitoring techniques. This work investigates the influence of entrained solid particles on Coriolis flow metering along with the potential wear problem of Coriolis flowmeters handling such abrasive medium. A review of slurry flow measurement techniques is given, together with the associated technical issues in slurry flow metering using Coriolis flowmeters. The negative impact of the presence of solid particles on Coriolis flow metering is identified through experimental work. A semi-empirical analytical model is proposed to compensate the effect of solid particles on Coriolis flow metering. An in-situ condition monitoring technique is presented for examining the structural health of Coriolis measuring tubes

Coke deposition detection through the analysis of catalyst images

Coke deposition on catalyst will not only reduce catalytic activity and selectivity, but also affect the product yield, the reaction residence time, the regenerator temperature and so on. As a result, it is necessary to measure the amount of coke deposition on catalyst. This paper proposes a new method based on image analysis. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and analysis, the gray layer is selected to be the most effective colour layer for colour features extraction based on a discriminability index, D. Eight colour features (mean, variance, skewness, entropy, energy, H, S, V) are extracted from images with a good ability to classify the catalysts with different coke amount. Furthermore, the results show that there is a significant linear correlation between the H value and amount of coke deposition on catalyst, which could reflect the coke deposited state and coking progress effectively

Mass Flow Measurement of Slurry Using Coriolis Flowmeters

Coriolis flowmeters have been proven to be effective while measuring single phase flows, however, the measurement accuracy degrades in case of multiphase flows. In this paper a Gaussian Process Regression (GPR) based soft-computing correction model is proposed for two-phase (sand-water) slurry mass flow measurement using Coriolis flowmeters. Experimental tests were conducted on a purpose-built slurry flow test rig for two different orientations of Coriolis measuring tubes i.e. upward and downward. Five different mass flowrates, 8200, 12000, 14300, 17000 and 20000 kg/h, were tested with Solid Volume Fraction (SVF) ranging between 0 – 1.6%. A number of features, including apparent mass flowrate, density, SVF, and solid weight concentration are used as inputs to GPR models. Two GPR models are trained and tested to estimate the measurement errors of slurry mass flow measurement for the upward and downward orientations of Coriolis flowmeters, respectively. The performances of the GPR models are assessed in comparison with the reference readings. The experimental results suggest that the proposed correction models have successfully limited the relative errors within ±0.2 % for all the five mass flowrates and SVFs from 0-1.6% for both upward and downward orientations of Coriolis flowmeters

Babesia divergens

ABSTRACTHuman babesiosis is an important tick-borne infectious disease. We investigated human babesiosis in the Gansu province and found that it is prevalent in this area with a prevalence of 1.3%. Results of gene sequencings indicate that 1.3% of patients were positive for Babesia divergens. This initial report of human B. divergens infections in Gansu Province should raise awareness of human babesiosis

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation