An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter (hw/dH), ratio of pressure of process to atmosphere pressure (P/Pa), Weber number (lTe).
Statistical analysis showed that the proposed models have an average absolute relative error (AARE) of 9.3% and
standard deviation (SD) of 9.7%for first model, AARE of 9.35% and SD of 10.5%for second model and AARE of 9.8%
and SD of 7.5% for the third model.
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
Sieve tray
Point efficiency
artificial neural network
Back-propagation algorithm
Publication Date
Mon Jan 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem
The Branch and Bound method (BAB)
The Local search algorithms
The Genetic Algorithm (GA)
The Particle swarm optimization (PSO)
The Multi-Objective problems.
In this paper, two of the local search algorithms are used (genetic algorithm and particle swarm optimization), in scheduling number of products (n jobs) on a single machine to minimize a multi-objective function which is denoted as (total completion time, total tardiness, total earliness and the total late work). A branch and bound (BAB) method is used for comparing the results for (n) jobs starting from (5-18). The results show that the two algorithms have found the optimal and near optimal solutions in an appropriate times.
