Inverse estimation of the particle size distribution using the Fruit Fly Optimization Algorithm

He, Zhenzong; Qi, Hong; Yao, Yuchen; Ruan, Li-Ming

doi:10.1016/j.applthermaleng.2014.08.057

Cited by 45 publications

(22 citation statements)

References 26 publications

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“…Different scents that are moving in air can be searched by fruit-flies by their osphresis organs; from a space of 40 km it can even smell food source. Then, it attempts to go to the food location, it uses its sensitive vision to find food and the company's flocking location and fly in that direction [15]. For each iteration, the best fruit-fly information is shared among the whole swarm and the last best fruit-fly information will be forwarded to next iteration.…”

Section: Fruit-fly Optimization Algorithmmentioning

confidence: 99%

Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm

Acharyulu¹,

Hota²,

Mohanty³

2018

IJET

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In this paper, fruit-fly optimization algorithm (FOA) is applied to automatic generation control (AGC) of multi-area power systems. In the proposed three-area system, reheat thermal systems are considered in all areas incorporating solar thermal power plant (STPP) in one of the areas. The optimum gain of proportional-integral-derivative (PID) controller is optimized applying FOA technique. The strength of FOA is established by comparing the results with well-established Grey Wolf optimizer (GWO) technique for the same interconnected power system. The performances of the system with FOA technique are found to be better than GWO algorithm for both with and without incorporating STPP in area-1. Further, from the sensitivity analysis, it is evident that the PID controller gains obtained by FOA technique under normal conditions are found to be better even for large changes in slip and system load conditions.

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Section: Fruit-fly Optimization Algorithmmentioning

confidence: 99%

Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm

Acharyulu¹,

Hota²,

Mohanty³

2018

IJET

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“…In the past few years, the optical measurement method (e.g., spectral extinction method, angular light-scattering method, diffraction lightscattering method, dynamic light-scattering method, laserinduced incandescence method, and laser-induced breakdown spectroscopy method) has been widely utilized to study the properties of particle system owing to offering a useful and effective approach to characterize a large number of industrial production processes. eir measurements vary from nanometer to millimeter [14]. Among these optical measurement methods, the angular light-scattering (ALS) method is regarded as one of the most viable.…”

Section: Introductionmentioning

confidence: 99%

Application of Multistep Inversion Method for Online Monitoring Aerosol Particle Size Distribution and Aerosol Concentration

Mao

et al. 2020

Mathematical Problems in Engineering

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Aerosol concentration in the flow is usually time varying, and aerosol particle size distribution (PSD) is considered to be unchanged, which increases the difficulty of the measurement of aerosol PSD and concentration online. To solve these problems, a kind of multistep inversion method based on the angular light-scattering (ALS) signals is proposed. First, the aerosol PSD is estimated using shuffled frog-leaping algorithms (SFLAs) from relative ALS signals. Then, with aerosol PSD as priori information, the aerosol concentration is obtained by the Kalman filter (KF) algorithm, widely used in the real-time control system of industrial facilities for its ability of fast predictions. The result reveals that the performance of the improved SFLA is better than that of the original SFLA in solving the aerosol PSD. Moreover, in studying the aerosol concentration, more accurate results can be obtained with larger standard deviation of process noise or smaller standard deviation of measurement noise, while decreasing sampling time interval can improve the accuracy of retrieval results and reduce time delay to a certain degree. So, to improve retrieval accuracy, the noise should be controlled, and appropriate sampling time interval should be selected. All the numerical simulations confirm that the methodology provides effective and reliable results in real-time estimating.

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“…In addition, these methods change the state of the aerosol particles in the atmosphere and may lead to biased results. In contrast, optical measurements are effective in-situ observation methods for quickly obtaining the optical and microphysical properties of the aerosol [23][24][25][26][27]. For example, the widespread ground-based aerosol robotic network (AERONET), as well as the sky radiometer network mainly located in eastern Asia (SKYNET), realize real-time monitoring of atmospheric aerosols by measuring the direct sun and diffuse sky radiance and provide related parameters including aerosol optical depth (AOD), single scattering albedo (SSA), m, and volume size distribution (VSD) [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

et al. 2019

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Detailed knowledge of the complex refractive indices (m) of fine- and coarse-mode aerosols is important for enhancing understanding of the effect of atmospheric aerosol on climate. However, studies on obtaining aerosol modal m values are particularly scarce. This study proposes a method for inferring m values of fine- and coarse-mode aerosol using the inversion products from the AERONET ground-based aerosol robotic network. By identifying the aerosol type, modal m values are constrained and then inferred based on a maximum likelihood method. Numerical tests showed that compared with the reference values, our method slightly overestimates the real parts of the refractive indices (n), but underestimates the imaginary parts (k) by 2.11% ± 11.59% and 8.4% ± 26.42% for fine and coarse modes, respectively. We applied this method to 21 AERONET sites around China, which yielded annual mean m values of (1.45 ± 0.04) + (0.0109 ± 0.0046)i and (1.53 ± 0.01) + (0.0039 ± 0.0011)i for fine- and coarse-mode aerosols, respectively. It is observed that the fine mode n decreased from 1.53 to 1.39 with increasing latitude, while fine mode k values were generally larger than 0.008 over most of China. The coarse-mode n and k ranged from 1.52 to 1.56 and from 0.002 to 0.006, respectively.

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Inverse estimation of the particle size distribution using the Fruit Fly Optimization Algorithm

Cited by 45 publications

References 26 publications

Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm

Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm

Application of Multistep Inversion Method for Online Monitoring Aerosol Particle Size Distribution and Aerosol Concentration

Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

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