Reconstructing design parameters of a rectangular resonator via peak signal-to-noise ratio and global optimization algorithms

Abstract This article proposes an approach for reconstructing physical parameters of a sample in a rectangular resonator during microwave radiation, knowing a priori, its electric field distribution. The inverse problem was solved using two global optimization algorithms and the peak signal-to-noise ratio (PSNR) criterion. First, the Self-regulated Fretwidth Harmony Search algorithm (SFHS) identified suitable resonant frequencies for a given configuration. Next, the unified Particle Swarm Optimization (UPSO) optimized said configuration. Together, they became a maximization strategy of the PSNR through a dual optimization process. Results showed the ability of the approach for estimating the height of each sample block and the resonating frequency of the cavity. This process takes longer to finish as a higher PSNR is demanded (mainly due to the aforementioned dual optimization). Even so, it allows for more similar electric field distributions between both, the direct and inverse problems.

[1]  Qing Huo Liu,et al.  Electromagnetic Inverse Scattering Series Method for Positioning Three-Dimensional Targets in Near-Surface Two-Layer Medium With Unknown Dielectric Properties , 2015, IEEE Geoscience and Remote Sensing Letters.

[2]  Mohammad Ali Ahmadi,et al.  Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs , 2014 .

[3]  A. Bahadori,et al.  A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature , 2015 .

[4]  Alireza Bahadori,et al.  Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine , 2016 .

[5]  Mohammad Ali Ahmadi,et al.  Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process , 2015 .

[6]  Mohammad Masoumi,et al.  Evolving Connectionist Model to Monitor the Efficiency of an In Situ Combustion Process: Application to Heavy Oil Recovery , 2014 .

[7]  A. Bahadori,et al.  Prediction of a solid desiccant dehydrator performance using least squares support vector machines algorithm , 2015 .

[8]  Mohammad Ali Ahmadi,et al.  Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion , 2012 .

[9]  Mohammad Ali Ahmadi,et al.  Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: Application of particle swarm optimization , 2014 .

[10]  A. Bahadori,et al.  A LSSVM approach for determining well placement and conning phenomena in horizontal wells , 2015 .

[11]  Amin Shokrollahi,et al.  Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir , 2013, Appl. Soft Comput..

[12]  Behzad Pouladi,et al.  Connectionist technique estimates H2S solubility in ionic liquids through a low parameter approach , 2015 .

[13]  Mohammad Ali Ahmadi,et al.  Connectionist approach estimates gas–oil relative permeability in petroleum reservoirs: Application to reservoir simulation , 2015 .

[14]  R. Harrington Time-Harmonic Electromagnetic Fields , 1961 .

[15]  Alireza Bahadori,et al.  Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool , 2015 .

[16]  D. Plettemeier,et al.  Use of Genetic Algorithms to Solve Inverse Scattering Problems a Contribution to the Experiment CONSERT onboard the Spacecraft Rosetta , 2007, 2007 IEEE International Symposium on Electromagnetic Compatibility.

[17]  R. Kharrat,et al.  Gas Analysis by In Situ Combustion in Heavy-Oil Recovery Process: Experimental and Modeling Studies , 2014 .

[18]  M. Ahmadi,et al.  Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration , 2016 .

[19]  Osama Abdel-Raouf,et al.  A Survey of Harmony Search Algorithm , 2013 .

[20]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[21]  Iván Amaya,et al.  Harmony Search algorithm: a variant with Self-regulated Fretwidth , 2015, Appl. Math. Comput..

[22]  A. Aydoğan,et al.  Analysis of Direct and Inverse Problems Related to Circular Waveguides Loaded With Inhomogeneous Lossy Dielectric Objects , 2014, IEEE Transactions on Microwave Theory and Techniques.

[23]  Mohammad Ebadi,et al.  Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence , 2014 .

[24]  Michel Feidt,et al.  Connectionist intelligent model estimates output power and torque of stirling engine , 2015 .

[25]  Alireza Baghban,et al.  Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique , 2015 .

[26]  A. Bahadori,et al.  A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems , 2014 .

[27]  M. Ahmadi,et al.  Phase Equilibrium Modeling of Clathrate Hydrates of Carbon Dioxide + 1,4-Dioxine Using Intelligent Approaches , 2015 .

[28]  Mohammad Masoumi,et al.  Evolving Smart Model to Predict the Combustion Front Velocity for In Situ Combustion , 2015 .

[29]  C. Balanis Advanced Engineering Electromagnetics , 1989 .

[30]  Michael N. Vrahatis,et al.  Unified Particle Swarm Optimization in Dynamic Environments , 2005, EvoWorkshops.

[31]  Iván Amaya,et al.  An improved variant of the conventional Harmony Search algorithm , 2014, Appl. Math. Comput..

[32]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[33]  M. Ahmadi Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

[34]  M. Ahmadi,et al.  New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept , 2012 .

[35]  Saeed Tavakoli,et al.  An intelligent global harmony search approach to continuous optimization problems , 2014, Appl. Math. Comput..

[36]  Mohammad Ali Ahmadi,et al.  Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach , 2014 .

[37]  Alireza Baghban,et al.  Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach , 2014 .

[38]  A. Boucouvalas,et al.  Design of Arbitrary Modal Electric Field in Cylindrical Waveguides , 2014, IEEE Journal of Quantum Electronics.

[39]  M. Ahmadi Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm , 2011 .

[40]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[41]  Mohammad Ali Ahmadi,et al.  Evolving smart approach for determination dew point pressure through condensate gas reservoirs , 2014 .