Profiling Particles of Sahara Dust Settled on the Ground by a Simplified Dynamic Light Scattering Procedure and Sedimentation.

Dust particles exist in the form of mineral aerosols and play a significant role in climate change patterns, while also having the potential to affect human health. The size of these particles is crucial, as it determines the atmosphere's albedo. In the past few years, a Saharan dust cloud has moved and arrived above Romania during spring, followed by rain containing the dust particles, which are deposited on various objects. We collected these particles in an aqueous suspension and employed natural sedimentation to separate them by density. We then conducted a dynamic light scattering (DLS) experiment to analyze their size. Our DLS setup was straightforward, and the time series analysis involved evaluating the frequency spectrum of the scattered light intensity-also known as the power spectrum-filtering it, and fitting the expected Lorentzian line to it to determine the parameters and the average diameter of the suspended particles. We found that the dust particles had a continuous distribution, with the biggest particles having a diameter around 1100 nm. The results obtained from the combination of sedimentation and DLS are consistent with reports on the size of Saharan dust particles in other regions of Europe.

An Advanced Sensor for Particles in Gases Using Dynamic Light Scattering in Air as Solvent.

Dynamic Light Scattering is a technique currently used to assess the particle size and size distribution by processing the scattered light intensity. Typically, the particles to be investigated are suspended in a liquid solvent. An analysis of the particular conditions required to perform a light scattering experiment on particles in air is presented in detail, together with a simple experimental setup and the data processing procedure. The results reveal that such an experiment is possible and using the setup and the procedure, both simplified to extreme, enables the design of an advanced sensor for particles and fumes that can output the average size of the particles in air.

Discharge profile of a zinc-air flow battery at various electrolyte flow rates and discharge currents.

Nowadays, due to global warming stemming from excessive use of fossil fuel, there is considerable interest in promoting renewable energy sources. However, because of the intermittent nature of these energy sources, efficient energy storage systems are needed. In this regard, zinc-air flow batteries (ZAFBs) are seen as having the capability to fulfill this function. In flow batteries, the electrolyte is stored in external tanks and circulated through the cell. This study provides the requisite experimental data for parameter estimation as well as model validation of ZAFBs. Each data set includes: current (mA), voltage (V), capacity (mAh), specific capacity (mAh/g), energy (Wh), specific energy (mWh/g) and discharge time (h:min:s.ms). Discharge data involved forty experiments with discharge current in the range of 100-200 mA, and electrolyte flow rates in the range of 0-140 ml/min. Such data are crucial for the modelling and theoretical/experimental analysis of ZAFBs.

Linear parameter-varying model for a refuellable zinc-air battery.

Due to the increasing trend of using renewable energy, the development of an energy storage system (ESS) attracts great research interest. A zinc-air battery (ZAB) is a promising ESS due to its high capacity, low cost and high potential to support circular economy principles. However, despite ZABs' technological advancements, a generic dynamic model for a ZAB, which is a key component for effective battery management and monitoring, is still lacking. ZABs show nonlinear behaviour where the steady-state gain is strongly dependent on operating conditions. The present study aims to develop a dynamic model, being capable of predicting the nonlinear dynamic behaviour of a refuellable ZAB, using a linear parameter-varying (LPV) technique. The LPV model is constructed from a family of linear time-invariant models, where the discharge current level is used as a scheduling parameter. The developed LPV model is benchmarked against linear and nonlinear model counterparts. Herein, the LPV model performs remarkably well in capturing the nonlinear behaviour of a ZAB. It significantly outperforms the linear model. Overall, the LPV approach provides a systematic way to construct a robust dynamic model which well represents the nonlinear behaviour of a ZAB.

Discharge performance and dynamic behavior of refuellable zinc-air battery.

Zinc-air batteries (ZABs) are considered a promising energy storage system. A model-based analysis is one of the effective approaches for the study of ZABs. This technique, however, requires reliable discharge data as regards parameter estimation and model validation. This work, therefore, provides the data required for the modeling and simulation of ZABs. Each set of data includes working time, cell voltage, current, capacity, power, energy, and temperature. The data can be divided into three categories: discharge profiles at different constant currents, dynamic behavior at different step changes of discharge current, and dynamic behavior at different random step changes of discharge current. Constant current discharge profile data focus on the evolution of voltage through time. The data of step changes emphasize the dynamic behavior of voltage responding to the change of discharge current. Besides, the data of random step changes are similar to the data of step changes, but the patterns of step changes are random. Such data support the modeling of a zinc-air battery for both theoretical and empirical approaches.

ZKF-based optimal robust fault estimation of descriptor LPV systems with measurement error-affected scheduling variables.

This paper proposes a robust state and fault estimation (SFE) method for discrete-time descriptor linear-parameter-varying (LPV) systems with inexact scheduling variables. As an important robust method dealing with system uncertainties, the set-membership estimation method is combined with the technique of generalized fault detectability indices and matrix to compute a state and fault tube to contain the real system states and fault signals at each time instant under the assumption that the system uncertainties (i.e., modeling errors, process disturbances, measurement noises and errors of scheduling variables) are bounded to guarantee the robustness of SFE. Theoretically, any trajectory in the tube can be used as a point-wise estimation of the real system states and fault signals. Meanwhile, the optimal parametric matrices both for set-membership estimation and unknown input observer (UIO) are designed by using the zonotopic Kalman filter (ZKF) procedure to guarantee the optimality of SFE under a set-theoretic framework. Furthermore, a collection of stability conditions for the proposed optimal SFE method are established based on the linear matrix inequalities (LMIs). At the end, a practical electric circuit and a vehicle example are used to illustrate the effectiveness of the proposed SFE method.