Design and Optimization of a Filter Based on Artificial Neural Network Applied to a Distillation Column

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
Jose S Torrecilla ◽  
Adela Fernández ◽  
Julian Garcia ◽  
Francisco Rodríguez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Engineering Process ◽  
Design And Optimization ◽  
Noisy Signals ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Design And Application

This paper discusses the design and application of a filter based on an Artificial Neural Network (ANN) in a chemical engineering process. The design of a filter consists of adapting the algorithms that make up the filter to the process to be filtered. Taking into account that the ANN is able to model almost every type of chemical process, the design and application of a filter based on ANN was studied. In this work, every ANN used was based on Multilayer Perceptron (MLP). Bearing in mind that ANN should reproduce the process as accurately as possible, an optimisation of the ANN (training function and parameters) was carried out. A mathematical model of a reflux in the upper part of a distillation column was used to test the ANN filter. The ANN is able to filter noisy signals with a mean prediction error less than 2.5•10-3 %.

scholarly journals Artificial Neural Network based Applications in Mechanical Engineering

2020 ◽  
Vol 9 (1) ◽  
pp. 77-86
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sheet Metal ◽  
Good Deal ◽  
Experimental Information ◽  
Literature Analysis ◽  
Engineering Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Physical Engineering

Search of sheet metal components along with physical items calls for a good deal of expertise in addition to creating expertise on the part of designers. Lately, different Artificial Intelligence (AI) methods now are being used in sheet metallic labor to minimize complexity; take lower the dependency on male, time and also expertise ingested physical appearance of pieces and expires and additionally to enhance one success. Artificial Neural Network (ANN) method is by using the most effective materials for fixing engineering layout issues and minimizing errors to drop with experimental information within actual physical engineering. This specific investigate documents particulars a substantial comment of applications of ANN strategy to sheet metallic perform how about hand-operated engineering apps. Major printed analysis do inside the domain name of bodily engineering is really summarized. In line with the vital comment of accessible literature, a lot more analysis range is really determined. The present literature analysis uncovers that there's stern need in an attempt that you are able to make use of ANN means to press items style as well as in addition to foresee gear lifetime contained sheet metal industries or perhaps maybe even in for bodily engineering process.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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