Experimental Analysis on Adsorption Characteristics of Methanol and R134A by Activated Carbon in Adsorption Refrigeration System

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
V. Baiju ◽  
C. Muraleedharan
Keyword(s):  
Activated Carbon ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Maximum Adsorption Capacity ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Adsorption And Desorption ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm

This paper presents adsorption and desorption characteristics of two different working pairs—activated carbon–methanol and activated carbon–R134a—determined experimentally. Dubinin–Radushkevich (D–R) equation is used to correlate the adsorption isotherms and to form the pressure, temperature, and concentration diagrams for both the assorted working pairs. The results show that the maximum adsorption capacity of activated carbon–R134a working pair is 1.21 times that of activated carbon–methanol. Temperature and pressure distribution throughout the adsorbent bed and their variation with adsorption time are also predicted. Use of artificial neural network (ANN) is proposed to determine the uptake from measured pressure and temperature. The back propagation algorithm with three different variants, namely, scaled conjugate gradient (SCG), Pola–Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) and logistic sigmoid transfer function are used, so that the best approach could be found out. After training, it is found that LM algorithm with 11 neurons is the most suitable for modeling adsorption refrigeration system. The adsorption and desorption uptake obtained experimentally are compared with the uptake predicted by D–R equation and ANN modeling.

scholarly journals Performance Prediction of Solar Adsorption Refrigeration System by Ann

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
V. Baiju ◽  
C. Muraleedharan
Keyword(s):  
Neural Network ◽  
Performance Prediction ◽  
Conjugate Gradient ◽  
Coefficient Of Performance ◽  
Cooling Power ◽  
Performance Parameters ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Discharge Temperature

This paper proposes a new approach for the performance analysis of a single-stage solar adsorption refrigeration system with activated carbon-R134a as working pair. Use of artificial neural network has been proposed to determine the performance parameters of the system, namely, coefficient of performance, specific cooling power, adsorbent bed (thermal compressor) discharge temperature, and solar cooling coefficient of performance. The ANN used in the performance prediction was made in MATLAB (version 7.8) environment using neural network tool box.In this study the temperature, pressure, and solar insolation are used in input layer. The back propagation algorithm with three different variants namely Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt (LM) and logistic sigmoid transfer function were used, so that the best approach could be found. After training, it was found that LM algorithm with 9 neurons is most suitable for modeling solar adsorption refrigeration system. The ANN predictions of performance parameters agree well with experimental values with R2 values close to 1 and maximum percentage of error less than 5%. The RMS and covariance values are also found to be within the acceptable limits.

Artificial neural network modelling of adsorbent bed in a solar adsorption refrigeration system

2012 ◽  
Vol 227 (2) ◽  
pp. 346-358 ◽  
Author(s):  
V Baiju ◽  
C Muraleedharan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This article analyses the adsorbent bed in an adsorption refrigeration system. After establishing the similarity to the compression process in a vapour compression system, thermodynamic analysis of the adsorbent bed in vapour adsorption system is carried out for evaluating the performance index, exergy destruction, uptake efficiency and exergetic efficiency of the adsorbent bed in a typical solar adsorption refrigeration system. This article also presents isothermal and isobaric modelling of methanol on highly porous activated carbon. The experimental data have been fitted with Dubinin–Astakhov and Dubinin–Radushkevitch equations. The isosteric heat of adsorption is also extracted from the present experimental data. The use of artificial neural network model is proposed to predict the performance of the adsorbent bed used. The back propagation algorithm with three different variants namely scaled conjugate gradient, Pola–Ribiere conjugate gradient and Levenberg–Marquardt and logistic sigmoid transfer function are used, so that the best approach could be found. After training, it is found that Levenberg–Marquardt algorithm with 14 neurons is the most suitable for modelling, the adsorbent bed in a solar adsorption refrigeration system. The artificial neural network predictions of performance parameters agrees well with experimental values with correlation coefficient ( R2) values close to 1 and maximum percentage of error less than 5%. The root mean square and covariance values are also found to be within the acceptable limits.

Intelligent Vision Inspection System for IC Engine Head: An ANN Approach

2012 ◽  
Vol 479-481 ◽  
pp. 2242-2245 ◽  
Author(s):  
Rajesh Kanna ◽  
Manikandan Saravana
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Back Propagation ◽  
Engine Block ◽  
Inspection System ◽  
Back Propagation Algorithm ◽  
Ic Engine ◽  
Vision Inspection ◽  
System Module ◽  
Artificial Neural Network Ann

A machine vision system based on Artificial Neural Network (ANN) for inspection of IC Engine block was developed to identify the misalignment and improper diminishing of holes in the IC Engine block. The developed machine vision and ANN module is compared with the commercial MATLAB® software and found results were satisfactory. This work is broadly divided into four stages, namely Intelligent inspection module, Machine Vision module, ANN module and Expert system module. A system with a camera was used to capture the various segments of head of the IC Engine block. The captured bitmap format image of IC Engine block has to be filtered to remove the noises present while capturing and the size is also altered using SPIHT method to an acceptable size and will be given as input to ANN. Generalized ANN with Back-propagation algorithm was used to inspect the IC Engine block. ANN has to be trained to provide the inspected report.

Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

2016 ◽  
Vol 231 (6) ◽  
pp. 1206-1216 ◽  
Author(s):  
Mustafa Ayyıldız ◽  
Kerim Çetinkaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Geometric Shapes ◽  
Testing Data ◽  
Artificial Neural

In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

Analysis of artificial neural network performance based on influencing factors for temperature forecasting applications

2020 ◽  
Vol 26 (3) ◽  
pp. 209-223
Author(s):  
M. Madhiarasan ◽  
M. Tipaldi ◽  
P. Siano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Network Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Research Fields ◽  
Temperature Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN)-based methods belong to one of the most growing research fields within the artificial intelligence ecosystem, and many novel contributions have been developed over the last years. They are applied in many contexts, although some “influencing factors” such as the number of neurons, the number of hidden layers, and the learning rate can impact the performance of the resulting artificial neural network-based applications. This paper provides a deep analysis about artificial neural network performance based on such factors for real-world temperature forecasting applications. An improved back propagation algorithm for such applications is also presented. By using the results of this paper, researchers and practitioners can analyse the encountered issues when applying ANN-based models for their own specific applications with the aim of achieving better performance indexes.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer

2017 ◽  
pp. 1076-1093 ◽  
Author(s):  
Mohamed F. Hassanin ◽  
Abdullah M. Shoeb ◽  
Aboul Ella Hassanien
Keyword(s):  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Cuckoo Search ◽  
Evolutionary Strategy ◽  
Heuristic Methods ◽  
Grey Wolf ◽  
Back Propagation Algorithm ◽  
Swarm Optimization ◽  
Ann Models ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) models are involved in many applications because of its great computational capabilities. Training of multi-layer perceptron (MLP) is the most challenging problem during the network preparation. Many techniques have been introduced to alleviate this problem. Back-propagation algorithm is a powerful technique to train multilayer feedforward ANN. However, it suffers from the local minima drawback. Recently, meta-heuristic methods have introduced to train MLP like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimizer (ACO), Social Spider Optimization (SSO), Evolutionary Strategy (ES) and Grey Wolf Optimization (GWO). This chapter applied Multi-Verse Optimizer (MVO) for MLP training. Seven datasets are used to show MVO capabilities as a promising trainer for multilayer perceptron. Comparisons with PSO, GA, SSO, ES, ACO and GWO proved that MVO outperforms all these algorithms.

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