Particle Size Distribution Shape Research Articles

Particle Size Inversion from Spectrally Resolved Full-Field Forward Scattering.

Ensemble particle sizing has traditionally relied on inversion of extinction measurements for the characterization of the particle size distribution (PSD) in particulate media. However, particulate media induce complex phase changes that contain valuable information about their structure. Here, we propose the use of coherent detection to derive particle size distributions in inhomogeneous samples from light scattering. This is achieved by exploiting THz waves, which allow for both extinction and refractive index information to be directly retrieved. A modified version of the iterative Twomey method is presented in order to take into account this information. Additionally, by using a forward model based on the Waterman-Truell formula for the complex refractive index, samples with absorption in both the matrix medium and the particulate phase can be measured. The inversion needs neither a priori assumptions nor constraints regarding the PSD shape. Numerical simulations show that this full-field approach reduces the error of the inversion process potentially up to 65% compared with inversion using only extinction data. Experimental validation of the technique is provided by measuring calibrated spherical glass particles inside a PTFE matrix and retrieving the PSD in the case of monodisperse and polydisperse samples showing an enhancement of up to 32% in comparison to inversion from extinction data.

Surface area and size distribution of cement particles in hydrating paste as indicators for the conceptualization of a cement paste representative volume element

The conceptualization of a representative volume element (RVE) of hardened cement paste for numerical homogenization of mechanical problems rests on identifying the largest discernible microstructural feature, i.e. unreacted cement grains. While the particle size distribution (PSD) of anhydrous cement is a well-controlled production parameter, the size evolution of a representative cement grain throughout hydration remained unresolved. This study analyzes digitized 3D cement paste microstructures obtained from X-ray micro-computed tomography, coupled with CEMHYD3D hydration model, and segmented by image-processing tools, to obtain the full PSD and specific surface area evolutions of unreacted grains throughout hydration. Results provided indicate a representative grain size in the range of 30−40μm regardless of hydration elapsed, implying a cement paste RVE should amount to 150−200μm to realistically represent cement grains. The PSD shape remained self-similar and two distinctive hydration regimes were identified, differing in dissolution rate and specific surface area decrease, correlating with calcium sulfate reactivity peak. Both measures provide easily accessible microstructural features that may be used for constructing artificial RVEs of hardened cement paste in micromechanical models and related simulations, resting on experimental data.

Variability in Oceanic Particle Size Distributions and Estimation of Size Class Contributions Using a Non-parametric Approach.

A dataset of nearly 400 measurements of the particle size distribution (PSD) compiled from the Pacific, Atlantic, and Arctic Oceans is used to examine variability in the magnitude and shape of the PSD, and to characterize the partitioning of particle number, cross‐sectional area, and volume concentration among defined size intervals. The results indicate that the relative contributions of three size classes based upon the pico‐, nano‐, and microplankton size range exhibit substantial changes among measures of particle size and between oceanic environments. The single‐slope power law model commonly employed to characterize the PSD in aquatic studies is demonstrated to have significant limitations in capturing the complexity of PSD shapes observed for natural particle assemblages, and in consequence poorly predicts the relative contributions of these different size intervals. We show that specific percentile diameters derived from the cumulative distributions of particle size are strongly correlated with the contributions of these three size classes, and that these non‐parametric descriptors of the cumulative distribution provide superior performance for estimating their contributions while requiring no assumption of underlying PSD shape. A comparison of these predictive relationships with independent field measurements suggests that this approach is generally robust for particle assemblages representing a wide diversity of marine environments.

Effect of hydrodynamic heterogeneity on particle dispersion in a Taylor-Couette flow reactor with variable configurations of inner cylinder

BackgroundEffect of hydrodynamic heterogeneity on particle dispersion in a Taylor-Couette flow (TC) reactor with variable configurations of inner cylinder has been investigated using CFD modelling. MethodsParticle dispersion was tracked based on the Eulerian-Lagrangian approach, where the reactant solution phase was solved in the Eulerian reference frame, while the particle dispersion was calculated by tracking a large number of particles with consideration of the hydrodynamic forces acting on particles and adopting actual particle properties measured from the particle synthesis experiments. Significant FindingsThe simulation reveals that particle dispersion is significantly enhanced by increasing the inner cylinder rotational speed, characterized by particle distribution for both circular inner cylinder Taylor-Couette flow reactor (CTC) and lobed cross-section inner cylinder Taylor-Couette flow reactor (LTC). Particle trajectories or dispersion are influenced by the turbulent Taylor vortices. Particle radial dispersion affects the particle classification by presenting different particle axial velocities in radial direction, while particle axial dispersion can be seen as an indicator for global mixing occurring in the TC reactor, which is enhanced at high rotational speed, especially in the LTC. The calculated dispersion coefficient is found to be similar to the shape of particle size distribution found in the experiments.

Analysis of individual and interaction effects of processing parameters on wet grinding performance in ball milling of alumina ceramics using statistical methods

It is of great interest and necessary to closely investigate and understand how ball milling parameters can affect the overall quality of milled ceramic powders. In this paper, we analyzed the individual and interaction effects of the key processing parameters on the wet grinding performance in a ball mill for alumina powder using statistical methods. The grinding performance was evaluated based on three quality characteristics of milled alumina powder: the median particle size (d50), width and skewness reflecting the shape of particle size distributions (PSDs). For this determination, the volume percentage of slurry, solid content, milling speed, milling time, and ball size are regarded as the key parameters affecting these characteristics. Milling experiments were designed by central composite design, and an experimental dataset was collected systematically. The analysis results show that it is worthwhile to consider not only the d50 but also the shape of the PSDs when assessing the ball milling performance. These results are useful for the production of high-quality alumina powders in ceramic industries.

Urban aerosol size distributions: a global perspective

Abstract. Urban aerosol measurements are necessary to establish associations between air pollution and human health outcomes and to evaluate the efficacy of air quality legislation and emissions standards. The measurement of urban aerosol particle size distributions (PSDs) is of particular importance as they enable characterization of size-dependent processes that govern a particle's transport, transformation, and fate in the urban atmosphere. PSDs also improve our ability to link air pollution to health effects through evaluation of particle deposition in the respiratory system and inhalation toxicity. To inform future measurements of urban aerosol observations, this paper reviews and critically analyzes the current state of knowledge on urban aerosol PSD measurements by synthesizing 737 PSD observations made between 1998 to 2017 in 114 cities in 43 countries around the globe. Significant variations in the shape and magnitude of urban aerosol number and mass PSDs were identified among different geographical regions. In general, number PSDs in Europe (EU) and North America, Australia, and New Zealand (NAAN) are dominated by nucleation- and Aitken-mode particles. PSDs in Central, South, and Southeast Asia (CSSA) and East Asia (EA) are shifted to larger sizes, with a meaningful contribution from the accumulation mode. Urban mass PSDs are typically bimodal, presenting a dominant mode in the accumulation mode and a secondary mode in the coarse mode. Most PSD observations published in the literature are short-term, with only 14 % providing data for longer than 6 months. There is a paucity of PSDs measured in Africa (AF), CSSA, Latin America (LA), and West Asia (WA), demonstrating the need for long-term aerosol measurements across wide size ranges in many cities around the globe. Geographical variations in urban aerosol effective densities were also reviewed. Size-resolved urban aerosol effective density functions from 3 to 10 000 nm were established for different geographical regions and intra-city sampling locations in order to accurately translate number PSDs to mass PSDs, with significant variations observed between near-road and urban background sites. The results of this study demonstrate that global initiatives are urgently needed to develop infrastructure for routine and long-term monitoring of urban aerosol PSDs spanning the nucleation to coarse mode. Doing so will advance our understanding of spatiotemporal trends in urban PSDs throughout the world and provide a foundation to more reliably elucidate the impact of urban aerosols on atmospheric processes, human health, and climate.

Solving the population balance equation for non-inertial particles dynamics using probability density function and neural networks: Application to a sooting flame

Numerical modeling of non-inertial particles dynamics is usually addressed by solving a population balance equation (PBE). In addition to space and time, a discretization is required also in the particle-size space, covering a large range of variation controlled by strongly nonlinear phenomena. A novel approach is presented in which a hybrid stochastic/fixed-sectional method solving the PBE is used to train a combination of an artificial neural network (ANN) with a convolutional neural network (CNN) and recurrent long short-term memory artificial neural layers. The hybrid stochastic/fixed-sectional method decomposes the problem into the total number density and the probability density function of sizes, allowing for an accurate treatment of surface growth/loss. After solving for the transport of species and temperature, the input of the ANN is composed of the thermochemical parameters controlling the particle physics and of the increment in time. The input of the CNN is the shape of the particle size distribution (PSD) discretized in sections of size. From these inputs, in a flow simulation, the ANN–CNN returns the PSD shape for the subsequent time step or a source term for the Eulerian transport of the particle size density. The method is evaluated in a canonical laminar premixed sooting flame of the literature, and for a given level of accuracy (i.e., a given discretization of the size space), a significant computing cost reduction is achieved (six times faster compared to a sectional method with ten sections and 30 times faster for 100 sections).

Hydroponic Growth Media (Substrate): A Review

From the reviewed literature, it is observed that scientist’s worked on the various substrate medias used for hydroponic system. They also studied about different combinations of the media and their effect on crop growth. The reviewed literature shows that scientists have studied different organic and inorganic media viz. cocopit, saw dust, rice husk vermiculite, perlite, hydroton, pumice, sand etc. It was also found that these media are not easily, cheaply and locally available for users. Beside this, few scientists studied about physical and chemical properties of media such as particle size, porosity, bulk density, wettability, water holding capacity (WHC), Air filled porosity (AFP), Electrical conductivity, pH of these media. The crop specific suitability of combinations of the different media mixtures were also tested and reported by many scientists. It was revealed from reviewed literature that salinity of media caused linear reduction in the water consumption. The size and shape of particle size distribution are useful for estimating the hydraulic properties of the media The substrate under long cultivation period causes increase of organic matter content and microorganism activity which leads to an increased competition for oxygen in the root environment. The optimal EC levels range from 1.5 to 4.0 dSm-1 according to crop and its sensitivity to different salinity levels. It was also observed that level of growing medium temperature close to that of the surrounding air seems more suitable. The ideal substrate should have a total porosity of over 85 percent. Particles of smaller-sized individual grains have a larger specific surface area.

Comprehensive soot particle size distribution modelling of a model Rich-Quench-Lean burner

Soot particle size distribution (PSD) evolution in an ethylene lab-scale swirl Rich-Quench-Lean (RQL) combustor is investigated using a detailed physicochemical sectional soot model coupled with the Conditional Moment Closure turbulent combustion model and Large Eddy Simulation. The aim is to develop predictive capability for the local soot PSD and to explore differences in soot PSDs with different conditions of a burner configuration widely used in practice for emissions control. Two such conditions are studied by varying the airflow provided in the burner primary and dilution regions, which has a drastic effect on soot emission as shown by previous experiments. The results show a reasonably good agreement with experiments for the mean reaction zone and soot locations and their variations with dilution. The predicted PSDs at the burner exit are fairly well captured for the high-dilution condition but show too few and too small particles for the dilution-free condition, which may be due to an over-prediction of the oxidation rates or the unity Lewis number assumption used here that enhances penetration of soot towards the oxidiser stream. The results are analysed to reveal the hierarchy of reaction pathways during soot evolution and indicate how dilution air modifies the soot PSD within the primary zone. In particular, the presence of dilution leads to a broadly sustained uni-modal soot PSD shape and a decrease in particle size. The results demonstrate a framework for capturing soot PSD in realistic combustion devices, which may help meet future regulations based on particle number and size distribution.

A set of graphical design criteria for slotted liners in steam assisted gravity drainage production wells

Slotted liners have been widely used in steam-assisted gravity drainage (SAGD) wells owing to their low cost and superior mechanical integrity. Multiple factors affect the performance of slotted liners, such as particle size distribution (PSD) of formation sands, aperture size, slot density, fluid flow rate, and wellbore operational conditions. Currently, most of the existing design criteria formulate the lower and upper bounds of the aperture based on one or several points on the particle size distribution curve of oil sands. Most of these design criteria neglect the slot density, wellbore operational conditions, and shape of PSD curve.This study carries out a series of large-scale pre-pack sand retention tests (SRT) in step rates. The aim is to investigate the impacts of aperture size, slot density, and fluid flow rate on the slotted liner performance. Comprehensive design criteria for determining the safe aperture window are presented to maintain the sanding and the wellbore plugging of the zone near the slotted liners within an acceptable level. Sand production governs the upper bound of the aperture size, and flow performance guides the lower bound of the aperture size. The new criteria are presented graphically to illustrate the optimal slot window as a function of the sand PSD, slot density, and fluid flow rate. The results of separate tests are used to demonstrate the performance of the new design criteria. The optimal slot window obtained via the new design criteria guides the slot liner selection in the SAGD process.

The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow

Abstract. The accurate representation of ice particles is essential for both remotely sensed estimates of clouds and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals to assume that all snow is composed of aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to interrogate the internal structure of aggregate snowflakes and to distinguish more dense and homogeneous rimed particles from aggregates. In this study we investigate which parameters of the morphology and size distribution of ice particles most affect the triple-frequency radar signature and must therefore be accounted for in order to carry out triple-frequency radar retrievals of snow. A range of ice particle morphologies are represented, using a fractal representation for the internal structure of aggregate snowflakes and homogeneous spheroids to represent graupel-like particles; the mass–size and area–size relations are modulated by a density factor. We find that the particle size distribution (PSD) shape parameter and the parameters controlling the internal structure of aggregate snowflakes both have significant influences on triple-frequency radar signature and are at least as important as that of the density factor. We explore how these parameters may be allowed to vary in order to prevent triple-frequency radar retrievals of snow from being over-constrained, using two case studies from the Biogenic Aerosols – Effects of Clouds and Climate (BAECC) 2014 field campaign at Hyytiälä, Finland. In a case including heavily rimed snow followed by large aggregate snowflakes, we show that triple-frequency radar measurements provide a strong constraint on the PSD shape parameter, which can be estimated from an ensemble of retrievals; however, resolving variations in the PSD shape parameter has a limited impact on estimates of snowfall rate from radar. Particle density is more effectively constrained by the Doppler velocity than triple-frequency radar measurements, due to the strong dependence of particle fall speed on density. Due to the characteristic signatures of aggregate snowflakes, a third radar frequency is essential for effectively constraining the size of large aggregates. In a case featuring rime splintering, differences in the internal structures of aggregate snowflakes are revealed in the triple-frequency radar measurements. We compare retrievals assuming different aggregate snowflake models against in situ measurements at the surface and show significant uncertainties in radar retrievals of snow rate due to changes in the internal structure of aggregates. The importance of the PSD shape parameter and snowflake internal structure to triple-frequency radar retrievals of snow highlights that the processes by which ice particles interact may need to be better understood and parameterized before triple-frequency radar measurements can be used to constrain retrievals of ice particle morphology.

Detection and Sizing of Submicron Particles in Biologics With Interferometric Scattering Microscopy

We demonstrate the application of interferometric scattering microscopy (IFS) for characterizing submicron particles in stir-stressed monoclonal antibody. IFS uses a layered silicon sensor and modified optical microscope to rapidly visualize and determine the particle size distribution (PSD) of submicron particles based on their scattering intensity, which is directly proportional to particle mass. Limits for particle size and optimal solution concentration were established for IFS characterization of submicron particles. We critically compare IFS data with dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) and find IFS is superior to NTA and DLS for determining the realistic shape of the number-based PSD, whereas NTA and DLS provide superior information about absolute particle size. Together, IFS, NTA, and DLS provide complementary information on submicron particles and enable quantitative characterization of the PSD of submicron aggregates. Finally, we explore quantifying particle size with IFS by developing a calibration curve for particle scattering intensity based on correlative scanning electron microscopy imaging. We found that only a subset of isotropic-shaped particles followed the expected proportionality between IFS intensity and particle mass. Overall, this study demonstrates IFS is a simple approach for detecting and quantifying submicron aggregate PSD in protein-based therapeutics.

Inherent optical properties and particle characteristics of the sea-surface microlayer

The sea-surface microlayer (SML) is known to have physical, chemical, and biological properties that are distinctly different from the underlying subsurface water (USW). However, only a few studies in the past reported on measurements of the optical properties of the SML and were limited to light absorption. In this study we present results for the main inherent optical properties (IOPs), the spectral absorption coefficients and the volume scattering function, as well as particle size distribution (PSD), from measurements of the SML and USW in contrasting ocean environments with near-surface chlorophyll-a concentration ranging from 0.06 mg m−3 in waters off Hawaiian Islands to 1 mg m−3 in the Santa Barbara Channel. Our observations also included prominent surface slick conditions associated with a dense bloom of Trichodesmium. Significant and highly variable enhancements of the optical properties and particle concentration, including significant changes in the shape of PSD, were observed in the SML compared with USW at all investigated sites. In clear oligotrophic waters the total concentration of particles larger than 0.7 μm in size was enriched in the SML more than 8-fold. In all examined waters the enrichment was consistently higher for larger particles (>10 μm) than smaller particles. The highest enhancement of light absorption coefficients, >100-fold for particulate absorption and >200-fold for phytoplankton absorption in the near-UV and red spectral regions, was observed during the Trichodesmium bloom. In clear oligotrophic waters the particulate absorption coefficient was enhanced by as much as 45-fold in the green spectral region and the non-algal component exhibited consistently higher enhancement than phytoplankton component across the examined spectrum. In contrast to absorption, the volume scattering function was enhanced more in clear oligotrophic waters (>15-fold at scattering angles ψ between about 65° and 80°) than in the situation of Trichodesmium bloom. With the exception of Trichodesmium bloom, we consistently observed significantly lower values of the degree of linear polarization of light scattered by suspended particles and whole seawater samples (by as much as 30% in the vicinity of ψ = 90°) in the SML compared with USW. This result indicates that the SML can have important effect on the state of polarization of downwelling light entering the ocean and upwelling light leaving the ocean across the air-sea interface. The determinations of IOPs in the SML can extend the capabilities for characterizing constituents of microlayer and provide useful information for radiative transfer and remote-sensing related studies.

A general mechanistic model of fly ash formation during pulverized coal combustion

A general mechanistic model, involving an ensemble of rate equations, is developed to predict the formation of fly ash during pulverized coal combustion. The most important pathways, including physicochemical mineral vaporizations, dynamic mineral collisions, and statistical mineral fragmentations, are addressed in the model. The coalescence between exposed minerals on the receding char surface is simulated by a three-dimensional hard sphere model, while the coagulation between ash aerosols of all sizes in the flue gas is done by a population balance model. Besides, the fragmentation of both liberated included minerals and isolated excluded minerals is assumed to follow a stochastic Poisson distribution. Numerical results fit well with the fine particulate matter (PM10) data measured by the electrical low pressure impactor and the bulk ash (PM10+) data measured by the Malvern particle sizer in our 25 kW one-dimensional furnace experiments. The model reproduces several features of the experimental data for coals in different ranks, including the shape of particle size distributions (PSDs) and the yield of fine particles (PM0.26, PM1, and PM2.5). For instance, it indicates that the fly ash from Zhundong coal in fractions of PM0.4, PM0.4-2.5, PM2.5-33.2, and PM33.2+ are mainly contributed by mineral precursors, mineral fragmentations, included minerals, and excluded minerals, respectively. With the validated model, the effects of particle coagulation, coal PSD, and ambient conditions on the fly ash formation are intensively examined.

Utilizing big data for batch process modeling and control

This manuscript illustrates the use of big data for modeling and control of batch processes. A modeling and control framework is presented that utilizes data variety (temperature or concentration measurements along with size distribution) to achieve newer control objectives. For an illustrative crystallization process, an approach is proposed consisting of a subspace state-space model augmented with a linear quality model, able to model and predict, and therefore control the particle size distribution (PSD). The identified model is deployed in a linear model predictive control (MPC) with explicit model validity constraints. The paper presents two formulations: (a) one that minimizes the volume of fines in the product by leveraging the variety of measurements and (b) the other that directly controls the shape of the particle size distribution in the product. The former case is compared to traditional control practice while the latter’s superior ability to achieve desired PSD shape is demonstrated.

Utilizing big data for batch process modeling and control

This manuscript illustrates the use of big data for modeling and control of batch processes. A modeling and control framework is presented that utilizes data variety (temperature or concentration measurements along with size distribution) to achieve newer control objectives. For an illustrative crystallization process, an approach is proposed consisting of a subspace state-space model augmented with a linear quality model, able to model and predict, and therefore control the particle size distribution (PSD). The identified model is deployed in a linear model predictive control (MPC) with explicit model validity constraints. The paper presents two formulations: a) one that minimizes the volume of fines in the product by leveraging the variety of measurements and b) the other that directly controls the shape of the particle size distribution in the product. The former case is compared to traditional control practice while the latter’s superior ability to achieve desired PSD shape is demonstrated.

Detailed modeling of soot particle formation and comparison to optical diagnostics and size distribution measurements in premixed flames using a method of moments

Recently improved knowledge about key processes for the formation and growth of soot particles lead to very extensive soot models describing several kinetic pathways for particle nucleation and growth in detail. One of these models has been proposed by D’Anna and coworkers (D’Anna et al., 2010, Sirignano et al., 2010). In these original studies, the multivariate formulation was solved with a sectional approach, while a more recent study (Salenbauch et al., 2017) also showed the suitability of moment methods such as the Conditional Quadrature Method of Moments (CQMOM) (Yuan et al., 2011). However, being a moment method, CQMOM does not allow to directly access the soot particle size distribution (PSD). This prevents the consistent comparability of CQMOM results to soot measurements based on a scanning mobility particle sizer (SMPS). Furthermore, the comparison to laser-induced fluorescence (LIF) experiments is also limited, as the LIF signal only represents small particles and their concentration is only extractable from the simulation results if the PSD shape is known.The aim of this study is to extend the previously developed CQMOM soot model by a PSD reconstruction step applying the concept of entropy maximization. Maintaining the efficiency of the CQMOM moment inversion algorithm to close the moment equations, the model extension enables to evaluate the diameter-based PSD in a post-processing step without prescribing a specific shape as input. The updated algorithm is applied to simulate soot formation in two different burner-stabilized premixed C2H4/O2/N2 flames with a very lightly sooting (C/O = 0.67) and a heavily sooting (C/O = 0.77) character. Numerical results are compared to recently published LIF, laser-induced incandescence (LII) and SMPS measurement data. The analysis investigates the model’s capability to predict phenomena such as uni- or bimodal distribution shapes and the transition of small nanostructures to agglomerates in the two target flames both of which exhibit very different sooting behaviour.

Coupling an LES approach and a soot sectional model for the study of sooting turbulent non-premixed flames

Due to their negative impacts on environment and human health, future regulations on soot emissions are expected to become stricter, in particular by controlling the size of the emitted particles. Therefore, the development of precise and sophisticated models describing the soot production, such as sectional methods, is an urgent scientific and industrial challenge. In this context, the first objective of this work is to use for the first time a sectional model to perform an LES of a sooting turbulent flames in order to demonstrate its capacities. For this, the whole LES formalism for this approach is developed. It includes state-of-art models for the description of the gaseous phase and an extension of a soot subgrid intermittency model to the sectional approach, originally proposed for the hybrid method of moments. Then, the LES is used to analyze a turbulent non-premixed ethylene–air jet diffusion flame and results are validated by available experimental data. The quality of results for the gaseous phase is satisfactory and results for the solid phase show a reasonable agreement with the experimental results in terms of localization, intermittency and soot volume fraction magnitude. Once the coupled LES-sectional approach validated, having access to the full information on the spatial and temporal evolution of the soot Particle Size Distribution (PSD), the second objective of this work is to provide a new fundamental insight on soot production in turbulent non-premixed flames. First, it is observed that a one-peak and a two-peak PSD shapes are observed at the bottom and downstream of the flame, respectively. Second, high fluctuations of the PSD distribution is observed all along the flame. In particular, a time bimodal behavior is observed with the presence of a zone with regular transitions between one- and two-peak PSD shapes. By analyzing soot particles Lagrangian paths, these high fluctuations are shown to be linked with the wide range of history paths of soot particles, which are mainly driven by turbulence.

Combining laser diffraction, flow cytometry and optical microscopy to characterize a nanophytoplankton bloom in the Northwestern Mediterranean

The study of particle size distribution (PSD) gives insights on the dynamics of distinct pools of particles in the ocean, which reflect the functioning of the marine ecosystem and the efficiency of the carbon pump. In this study, we combined continuous particle size estimations and discrete measurements focused on phytoplankton to describe a spring bloom in the North West Mediterranean Sea. During April 2013, about 90 continuous profiles of PSD quantified through in situ laser diffraction and transmissiometry (the Laser in situ Scattering and Transmissiometry Deep (LISST-Deep), Sequoia Sc) were complemented by Niskin bottle samples for flow cytometry analyses, taxonomic identification by optical microscopy and pigments quantification. In the euphotic zone, the PSD shape seen by the LISST was fairly stable with two particle volume peaks covering the 2–11 µm and 15–109 µm size fractions. The first pool strongly co-varied with the chlorophyll fluorescence emitted by phytoplankton cells. In addition, over the 2–11 µm fraction, the LISST derived abundance was highly correlated with the abundance of nanophytoplankton counted by flow cytometry. Microscopy identified a phytoplankton community dominated by nanodiatoms and nanoflagellates. High correlation of LISST derived particle carbon and Particulate Organic Carbon and high nitrogen in the Particulate Organic Matter also supported a dominance of actively growing phytoplankton cells in this pool. The second, broader pool of particles covering sizes 15–109 µm was possibly microflocs coming from rivers and/or sediments. This study demonstrates the complementarity of continuous measurements of PSD combined with discrete measurements to better quantify size, abundance, biomass, and spatial (both vertical and horizontal) distribution of phytoplankton in open ocean environments.

Fault tolerant shape control for particulate process systems under simultaneous actuator and sensor faults

The problem of fault tolerant shape control for particle size distribution (PSD) process subject to simultaneous time-varying actuator and sensor faults is studied. In this work, sensor and actuator faults are taken into consideration in a unified framework, and estimated by using adaptive observer technique. For the PSD process, the output distribution, rather than the system output signal itself, is adopted for shape control. Square root rational B-spline approximation is used to fit the distribution shape. An innovative on-line fault estimation scheme is proposed for simultaneous actuator and sensor faults estimation. Then an augmented controller based on the virtual actuator and virtual sensor techniques is designed to compensate for the faults and realise PSD shape tracking. A simulation example of emulsion polymerisation is used to validate the proposed scheme and satisfactory performance is obtained.