Crystal shape monitoring and supersaturation measurement in cooling crystallization with quartz crystal oscillator

A crystallization monitoring system using a quartz crystal oscillator was utilized to predict different shapes of crystal formation by measuring crystal growth rate and to measure supersaturation. Applying different rates of cooling, crystal formation of different shapes was induced, and the frequency variation of the oscillator and the crystal shape observed with an SEM were compared to determine how the frequency variation can be interpreted for the prediction of produced crystal shape. The experimental results obtained from the crystallization of potassium nitrate and cupric sulfate solutions showed that the proposed frequency measurement technique could be applied in the prediction of crystal shape of cooling crystallization processes. In addition, supersaturation was determined from the measurements of solution and coolant temperatures.     

Three-way data analysis with time lagged window for on-line batch process monitoring

In this paper, on-line batch process monitoring is developed on the basis of the three-way data structure and the time-lagged window of process dynamic behavior. Two methods, DPARAFAC (dynamic parallel factor analysis) and DTri-PLS (dynamic trilinear partial least squares), are used here depending on the process variables only or on the process variables and quality indices, respectively. Although multivariate analysis using such PARAFAC (parallel factor analysis) and Tri-PLS (trilinear partial least squares) models has been reported elsewhere, they are not suited for practicing on-line batch monitoring owing to the constraints of their data structures. A simple modification of the data structure provides a framework wherein the moving window based model can be incorporated in the existing three-way data structure to enhance the detectability of the on-line batch monitoring. By a sequence of time window of each batch, the proposed methodology is geared toward giving meaningful results that can be easily connected to the current measurements without the extra computation for the estimation of unmeasured process variables. The proposed method is supported by using two sets of benchmark fault detection problems. Comparisons with the existing two-way and three-way multiway statistical process control methods are also included.     

Mineral scale monitoring for reverse osmosis desalination via real-time membrane surface image analysis

An approach to real-time analysis of mineral scale formation on reverse osmosis (RO) membranes was developed using an ex-situ direct observation membrane monitor (MeMo). The purpose of such monitoring is to signal the onset of mineral scaling and provide quantitative information in order to appropriately initiate system cleaning/scale dissolution. The above is enabled by setting the MeMo operating conditions (cross flow velocity and transmembrane pressure) to closely match the conditions in the monitored membrane plant (e.g., in the tail RO element) in order to mimic the surface scaling processes taking place inside the monitored RO plant element. Mineral scale in the MeMo system is monitored by comparison of consecutive images of the membrane surface for the purpose of determining the evolution of the fractional coverage by mineral salt crystals and the corresponding crystal count in the monitored region. Through online image analysis, once crystal growth is determined to be above a prescribed threshold, one can then initiate any number of cleaning protocols. Through early detection of membrane scaling (i.e., before permeate flux decline is observed), enabled by the present monitoring approach, the system operator can prevent irreversible membrane damage and loss of system productivity.     

An algorithm for analyzing noisy, in situ images of high-aspect-ratio crystals to monitor particle size distribution

An image analysis algorithm for extracting particle size distribution information from in situ images of suspended, high-aspect-ratio crystals is described. The accuracy of the algorithm is assessed using comparisons with results obtained by manually sizing the imaged crystals. The algorithm can accurately extract crystal size information from in situ images of suspended, high-aspect-ratio crystals for moderate solids concentrations. The algorithm's performance declines somewhat for high solids concentrations and high levels of particle attrition. The speed with which the algorithm analyzes the images is suitable for real-time monitoring and control of particle size distribution mean and variance.     

Determination of particle shape distribution of clay using an automated AFM image analysis method

We have developed a direct imaging method for measuring particle shape distributions. It consists of a series of advanced automated algorithms that analyze 3D Atomic Force Microscopy (AFM) images of diluted pigment suspensions deposited onto glass cover slips. The method was applied to determine particle shape distributions of 10 different clay pigments from Brazil and Georgia, US. We found that the aspect ratio varies significantly and monotonically with particle diameter. The method gives generally lower average aspect ratios than those mentioned in previously published data, but still ranks pigments similarly. It was determined that Georgia clays generally have higher aspect ratios and broader shape distributions than Brazilian clays. This new direct automated AFM image analysis approach will be useful in pigment shape characterization and will provide important data for predicting coated paper performance.     

Model-based object recognition to measure crystal size and shape distributions from in situ video images

To achieve robust control of industrial crystallization processes, it is necessary to measure the sizes, shapes, and polymorphic forms (i.e., internal structures) of the developing crystal population. This paper describes a model-based object recognition algorithm designed to extract crystal size and shape information from noisy, in situ crystallization images. The effectiveness of the algorithm is demonstrated using in situ images obtained at low, medium, and high solids concentrations during an α-glycine cooling crystallization in water. With respect to measurements obtained through manual image analysis by human operators, the algorithm gives reasonably accurate size and shape measurements. The algorithm is sufficiently fast to enable real-time monitoring for typical cooling crystallization processes.     

An investigation on dissolution kinetics of single sodium carbonate particle with image analysis method

Dissolution kinetics of sodium carbonate is investigated with the image analysis method at the approach of single particle. The dissolution experiments are carried out in an aqueous solution under a series of controlled temper-ature and pH. The selected sodium carbonate particles are al spherical with the same mass and diameter. The dissolution process is quantified with the measurement of particle diameter from dissolution images. The concentration of dissolved sodium carbonate in solvent is calculated with the measured diameter of particle. Both surface reaction model and mass transport model are implemented to determine the dissolution mecha-nism and quantify the dissolution rate constant at each experimental condition. According to the fitting results with both two models, it is clarified that the dissolution process at the increasing temperature is controlled by the mass transport of dissolved sodium carbonate travelling from particle surface into solvent. The dissolution process at the increasing pH is control ed by the chemical reaction on particle surface. Furthermore, the dissolution rate constant for each single spherical sodium carbonate particle is quantified and the results show that the disso-lution rate constant of single spherical sodium carbonate increases significantly with the rising of temperature, but decreases with the increasing of pH conversely.     

Automated image analysis for trajectory determination of single drop collisions

The fundamental analysis of drop coalescence probability in liquid/liquid systems is necessary to reliably predict drop size distributions in technical applications. For this crucial investigation two colliding oil drops in continuous water phase were recorded with different high speed camera set-ups under varying conditions. In order to analyze the huge amount of recorded image sequences with varying resolutions and qualities, a robust automated image analysis was developed. This analysis is able to determine the trajectories of two colliding drops as well as the important events of drop detachment from cannulas and their collision. With this information the drop velocity in each sequence is calculated and mean values of multiple drop collisions are determined for serial examinations of single drop collisions. Using the developed automated image analysis for drop trajectory and velocity calculation, approximately 1–2 recorded high speed image sequences can be evaluated per minute.     

A neural network-based soft sensor for particle size distribution using image analysis

Many industrial processes require on-line measurement of particle size and particle size distribution for process monitoring and control. The available techniques for reliable on-line measurement are, however, limited. In this paper, based on the captured surface images of randomly disarranged ore particles, the image uniformity was characterized. Particle size distribution was then investigated by applying a neural network-based modeling with the obtained image uniformity. The proposed soft sensor provides an improved prediction model and can be used for real time measurement of particle size distribution in the industrial operations.     

Measurement of size and shape distributions of particles through image analysis

The size and shape of particles can be described using a 2D particle size distribution (PSD) where two characteristic lengths define each particle in the population. The determination of 2D PSDs based on microscopic pictures of particles in suspension is studied. The experimental data are represented as an axis length distribution (ALD) that can be extracted from a series of microscopic pictures by a fully automated image analysis. The problem of finding the underlying bi-dimensional PSD is stated as an optimization problem. For the solution a genetic algorithm is used. The approach is tested on simulated ALDs, as well as on an experimentally measured ALD obtained from carbon fiber particles.     

A novel automated image analysis method for maceral analysis

A new image analysis technique has been developed which allows maceral analysis of coal to be carried out. The technique is able to separate the liptinite component from the background resin by using two separate images of the surface captured with different camera exposure times. At normal exposure, the liptinite has a similar grey scale value to the surrounding resin, but at a higher exposure time, the resin remains black whilst the liptinite grey scale increases significantly enough to distinguish it from the resin. The method has been tested for repeatability and reproducibility and found to be within the ISO Standard requirements.     

Recent advances in the monitoring,modelling and control of crystallization systems

Crystallization is one of the most important unit operations used for the separation and purification of crystalline solid products. Appropriate design and control of the crystallization process is paramount to produce crystalline products with tailor-made-properties. This paper provides an overview of selected recent developments in the modelling, monitoring and control of crystallization processes. We consider the topics discussed in this review to be enabling technologies for the development of the next generation of crystallization processes with significantly improved predictability, robustness and controllability.     

激光衍射法测试颗粒形状的模拟

对激光衍射法测试颗粒形状的原理、数学模型作了全面的介绍。在数值模拟的基础上论证了该法的可行性以及操作中应注意的问题,指出该法与现代激光粒度仪的结合有望诞生新一代激光颗粒仪。     

Measurement of coal particle size and shape with concentrated coal-water mixtures

The behavior of concentrated coal-water mixtures having narrow particle size ssfractions of coal was investigated. The pulverized coal was fractionated into six distinct particle size ranges, i.e. -70+80, -80+120, -120+140,-140+200, -200+400 and -400 mesh sizes by using a series of sieves. Settling rates were determined as functions of solids concentration for suspensions in water of coal particles to establish the measurement of particle size and shape factor and to assess concentration effect upon the observed hindered settling rates. The settling rates were modelled using the Richardson-Zaki model with the exponent n variable to account for the nonspherical shape of the coal particles. The data was also correlated with the Michaels-Bolger model which explicitly account for the excess water which is dragged down along with the particles undergoing sedimentation. In addition, coal particles and suspensions were characterized by coal analysis, heating value, solid heat capacity and thermal conductivity, densities, maximum packing concentrations and pore size distributions.     

A Wavelet Method for Analysis of Droplet and Particle Images for Monitoring Heterogeneous Processes

Image analysis has been used for many years in chemistry and chemical engineering laboratories for the study of size distributions and shapes of droplets/particles, and with the rapid progress in on-line digital imaging sensors, there is a great potential for applying the technique to on-line monitoring and automatic control of sizes and shapes of particulate products. One of the major challenges towards this goal is clearly the availability of methods for image analysis that need to be accurate, fast, robust, and tolerant of the quality of images and noises. This article describes a wavelet-based method for analysis of images obtained in heterogeneous polymerization. The method consists of four steps: image pre-processing using morphological operation, multi-scale wavelet analysis for edge detection, curvature-based circle recognition, and clustering. Real images from heterogeneous polymerization of varied qualities were used to illustrate the method.     

A Wavelet Method for Analysis of Droplet and Particle Images for Monitoring Heterogeneous Processes

Image analysis has been used for many years in chemistry and chemical engineering laboratories for the study of size distributions and shapes of droplets/particles, and with the rapid progress in on-line digital imaging sensors, there is a great potential for applying the technique to on-line monitoring and automatic control of sizes and shapes of particulate products. One of the major challenges towards this goal is clearly the availability of methods for image analysis that need to be accurate, fast, robust, and tolerant of the quality of images and noises. This article describes a wavelet-based method for analysis of images obtained in heterogeneous polymerization. The method consists of four steps: image pre-processing using morphological operation, multi-scale wavelet analysis for edge detection, curvature-based circle recognition, and clustering. Real images from heterogeneous polymerization of varied qualities were used to illustrate the method.     

Pore segmentation of cement-based materials from backscattered electron images

A technique to segment pores from a normal backscattered electron (BSE) image of cement-based materials is presented. The upper threshold grey level for porosity is determined from the inflection point of the cumulative brightness histogram of the BSE image. This represents a critical point where a small incremental grey value will cause a sudden increase in thresholded area, a condition termed as overflow. The proposed technique was found to be more consistent and reliable than existing methods. Significantly fewer images are required to achieve a satisfactory level of statistical confidence for quantifying porosity.     

Three-dimensional mathematical analysis of particle shape using X-ray tomography and spherical harmonics: Application to aggregates used in concrete

The properties of composites made by placing inclusions in a matrix are often controlled by the shape and size of the particles used. Mathematically, characterizing the shape of particles in three dimensions is not a particularly easy task, especially when the particle, for whatever reason, cannot be readily visualized. But, even when particles can be visualized, as in the case of aggregates used in concrete, three-dimensional (3-D) randomness of the particles can make mathematical characterization difficult. This paper describes a mathematical procedure using spherical harmonic functions that can completely characterize concrete aggregate particles and other particles of the same nature. The original 3-D particle images are acquired via X-ray tomography. Three main consequences of the availability of this procedure are mathematical classification of the shape of aggregates from different sources, comparison of composite performance properties to precise morphological aspects of particles, and incorporation of random particles into many-particle computational models.     

颗粒形状对全散射法测量颗粒物质量浓度的影响

燃煤电厂排放颗粒物作为大气颗粒物主要来源之一受到广泛关注,实现颗粒物质量浓度的精确在线监测意义重大。激光全散射法作为一种常用的颗粒物质量浓度在线监测方法,受颗粒特性影响较大,易造成较大的测量偏差。为探究颗粒形状对全散射法测量的影响,搭建了全散射法测试台架,并采用球形二氧化硅颗粒对测试系统进行标定,标定结果显示,球形二氧化硅颗粒质量浓度与消光程度高度线性相关,相关系数R2>0.99,测量浓度与真实浓度偏差仅为1.8%。进一步采用633 nm与532 nm波长激光分别对3种非球形(棱形、不规则和片状)二氧化硅颗粒进行测试试验。测试结果显示,3种形状颗粒物的质量浓度与消光程度均高度相关,R2均高于0.99;3种形状颗粒物质量浓度的测量值与真实值偏差与颗粒形状及激光波长相关,对比发现,入射波长为633 nm时,不规则、棱形和片状颗粒的质量浓度测量值与真实值的测量偏差分别为16.1%、27.4%和36.6%;入射波长为532nm时,3种颗粒质量浓度的测量偏差分别为4.8%、11.4%和17.4%;颗粒形状一致条件下,532 nm入射波长时的测量偏差明显较小,通过波长的选择可降低颗粒形状变化造成的测量偏差。