An improved particle swarm optimization method for locating time-varying indoor particle sources

Abstract

A B S T R A C TThe indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.

Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation

“…In our previous papers [9,41] , we introduced the basic principles of the standard PSO algorithm and the IPSO algorithm in detail, as shown in Fig. 3 .…”

“…To quickly locate a contaminant source in a time-varying indoor environment with mechanical ventilation, we presented and experimentally validated a CPSO method that was developed based on the IPSO algorithm presented in our previous study [41] . This study extended the applications of the multi-robot active olfaction method from steady-state indoor environments to time-varying indoor environments with mechanical ventilation.…”

“…However, this algorithm can easily cause the robots to become trapped in local extremum areas. To overcome this drawback, we previously developed an improved particle swarm optimization (IPSO) algorithm by combining concentrations and airflows to enable robots to continuously track the plume [41] . However, this algorithm was only validated in steady-state indoor environments that were simulated by CFD.…”

“…Schematic of the plume tracking principle using the (a) standard particle swarm optimization (SPSO) algorithm[36] and (b) improved particle swarm optimization (IPSO) algorithm[41] .…”

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments

“…Tracking the weak information released by the releasing sources in nature and human society through the olfactory tracking device can be regarded as the optimization problem of dynamic multiparameter function. Olfactory searchers can be used in areas related to the gas releases, such as toxic gas detection and location [ 1 ], rescue and relief [ 2 ], explosives detection and location [ 3 ] air pollution source tracking [ 4 ], and fire accident [ 5 ].…”