scholarly journals Fed-Batch Sucrose Crystallization Model for the B Massecuite Vacuum Pan, Solution by Deterministic and Heuristic Methods

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1145
Author(s):  
Paulo Eduardo de Morais Gonzales ◽  
Marcos Antônio de Souza Peloso ◽  
José Eduardo Olivo ◽  
Cid Marcos Gonçalves Andrade
Keyword(s):  
Particle Swarm ◽  
Average Diameter ◽  
Optimization Method ◽  
Convergence Time ◽  
Fed Batch ◽  
Empirical Relations ◽  
Gradient Based ◽  
Particle Swarm Method ◽  
Relative Differences ◽  
Energy Balances

Fed-batch crystallization is a crucial step for sugar production. In order to relate parameters that are difficult to measure (average diameter of the crystals and total mass formed) to other easier to measure parameters (volume, temperature, and concentration), a model was developed for a B massecuite vacuum pan composed of mass and energy balances together with empirical relations that describe the crystal development inside equipment. The generated system of ordinary differential equations (ODE) had eight parameters which were adjusted through minimization of relative differences between the model results and experimental data. It was solved through the function fmincon, available in MATLABTM, which is a deterministic and gradient-based optimization method. The objective of this paper is to improve the model obtained and, for this purpose, two metaheuristic functions were used: genetic algorithm and particle swarm. To compare the results, the convergence time of each algorithm was used as well as the resulting quadratic deviation. The particle swarm method was the best option among the three used, presenting a shorter execution time and lower quadratic relative deviation.

scholarly journals Pareto Assumption for Constrained PSO-Based Locomotive Resistance Minimization

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mine Sertsöz ◽  
Mehmet Fidan
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Method ◽  
Particle Swarm ◽  
Optimization Method ◽  
Optimal Operation ◽  
Mechanical Resistance ◽  
Swarm Optimization ◽  
Pareto Analysis ◽  
Operation Conditions ◽  
Particle Swarm Method

The mechanical resistance of a locomotive is crucial for power consumption. It is desirable to maintain this resistance at a minimum value for energy efficiency under optimal operation conditions. The optimal conditions can be found by particle swarm optimization with constraints. The particle swarm optimization method is a highly preferred type of heuristic algorithm because of its advantages, such as fewer parameters, faster speed, and a simpler flow diagram. However, fast convergence can be misleading in finding the optimum solution in some cases. Pareto analysis is used in this proposed study to prevent missing the target. When the literature is searched, it is seen that there are various studies using this method. However, in all of these studies, the results of the particle swarm method have been interpreted as whether or not they complied with Pareto’s 80/20 rule. The validity of the Pareto analysis is taken as an assumption, and with the help of this assumption, the coefficients of a locomotive’s mathematical equation were changed, and finally the results were found by applying the particle herd optimization method. Finally, a novel hybrid method has been created by including the Pareto optimality condition to particle swarm optimization. The results are compared with this innovative hybrid method of Pareto and particle swarm and the results found using only the particle swarm method.

scholarly journals Optimizing the electron acceleration in vacuum by chirped ultrashort laser pulse using particle swarm method

2019 ◽  
Vol 37 (03) ◽  
pp. 242-251 ◽  
Author(s):  
Mohammad Rezaei-Pandari ◽  
Fazel Jahangiri ◽  
Ali Reza Niknam
Keyword(s):  
Laser Pulse ◽  
Ultrashort Laser Pulse ◽  
Particle Swarm ◽  
Electron Acceleration ◽  
Optimization Method ◽  
Energy Gain ◽  
Dependent Manner ◽  
Pronounced Increase ◽  
Particle Swarm Method ◽  
Ultrashort Laser

AbstractEfficient electron acceleration by a linearly chirped ultrashort laser pulse in vacuum is investigated using the particle swarm optimization method. By applying this method for optimizing the initial parameters of the laser pulse, a pronounced increase in final energy gain of the electron is obtained compared to that expected from the successive optimization method. Our results also suggest that the value of the optimal chirp parameter is independent of laser polarization and the energy gain could be insensitive to the sign of this parameter when the initial phase is optimally adjusted. In addition, utilizing the chirped laser pulse with optimized conditions for acceleration of an electron bunch reveals that the energy spectrum is shifted to considerably higher energies and the spatial distribution is significantly improved in a polarization-dependent manner.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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