Evaluation of low-cost optical particle counters for monitoring individual indoor aerosol sources

Abstract

Low-cost optical particle counters (OPC) have gained increasing attention in recent years in exposure studies. Previous studies reported that the OPCs’ performance varies considerably with type of particles being measured; however, little information on their performance in monitoring common indoor aerosols is available. Given the significance of exposure to indoor aerosols and their associated adverse health effects, this experimental study investigates the performance of low-cost OPCs in monitoring individual aerosols that are commonly found indoors in a controlled chamber environment. Performances of four low-cost OPCs were examined under exposure to varying concentrations of biological (dust mite, pollen, cat, and dog fur) and non-biological (monodisperse silica and melamine resin) aerosols. Each particle sample was dispersed into the chamber using a computer-controlled syringe injection system, while size-resolved particle number concentrations were simultaneously measured by four low-cost OPCs (OPC N2, IC Sentinel, Speck, and Dylos) as well as a lab-grade reference sensor (AeroTrak). The study results showed measurable effects of particle size, particle type, and concentration on the low-cost OPC responses. Particle concentration had the most dominant effect on the linearity of low-cost sensors. Results also revealed that the sensor responses to four biological particles follow a similar pattern and converge to a linear line as the number concentration increases above 5?cm^?3. As for non-biological particles, the OPC responses were more varied depending on the particle type and size, especially in the concentration range <10?cm^?3. Calibration equations developed in this study provide baseline information for correcting low-cost OPC readings when utilized to measure concentrations of individual indoor aerosol sources.

Copyright © 2019 American Association for Aerosol Research     

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies.     

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms.