Two Dimensional Model for In Situ Bioremediation of Groundwater Using Meshfree Point Collocation Method (PCM) and Particle Swarm Optimization (PSO)

Author(s):  
Meenal Mategaonkar ◽  
T. I. Eldho
2018 ◽  
Vol 20 (4) ◽  
pp. 886-897 ◽  
Author(s):  
Meenal Mategaonkar ◽  
T. I. Eldho ◽  
Sahajanand Kamat

Abstract Groundwater contamination due to contaminants like trichloroethylene (TCE), tetrachloroethylene, dichloroethylene, phenol, etc., is an alarming concern for most of the manufacturing areas. It is important to identify the type of pollutant, concentration, location, and direction of the contaminant plume for groundwater remediation. Bioremediation has been identified as one of the important remediation techniques for these types of contaminants. Bioremediation modeling comprises solutions to biodegradation equations and fixing the time of remediation and locating the oxygen injection wells. In this study, a simulation-optimization (S/O) model based on the coupled meshfree point collocation method (MFree-PCM) and particle swarm optimization (PSO) is proposed for in-situ bioremediation design. The in-situ bioremediation process of groundwater contamination is explored using the developed PCM-BIO-PSO multi-objective model with different strategies of minimization of cost, number of wells and time of remediation. The proposed model can be effectively used for the in-situ bioremediation design of contaminated sites.


2021 ◽  
Vol 13 (6) ◽  
pp. 1207
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Yuming Jiang ◽  
Liying Xu

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.


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