Application of Artificial Neural Network in Distillation System: A Critical Review of Recent Progress

Distillation is a unit operation with multiple input parameters and multiple output parameters. It is characterized by multiple variables, coupling between input parameters, and non-linear relationship with output parameters. Therefore, it is very difficult to use traditional methods to control and optimize the distillation column. Artificial Neural Network (ANN) uses the interconnection between a large number of neurons to establish the functional relationship between input and output, thereby achieving the approximation of any non-linear mapping. ANN is used for the control and optimization of distillation tower, with short response time, good dynamic performance, strong robustness, and strong ability to adapt to changes in the control environment. This article will mainly introduce the research progress of ANN and its application in the modeling, control and optimization of distillation towers.

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Improving the Mechanical Properties of Wire-Rope Silk Scaffold by Artificial Neural Network in Tendon and Ligament Tissue Engineering

Journal of Engineered Fibers and Fabrics ◽

10.1177/155892501501000303 ◽

2015 ◽

Vol 10 (3) ◽

pp. 155892501501000 ◽

Cited By ~ 1

Author(s):

Elham Naghashzargar ◽

Dariush Semnani ◽

Saeed Karbasi

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Back Propagation ◽

Wire Rope ◽

Elongation At Break ◽

Input Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Silk Yarn

Finding an appropriate model to assess and evaluate mechanical properties in tissue engineered scaffolds is a challenging issue. In this research, a structurally based model was applied to analyze the mechanics of engineered tendon and ligament. Major attempts were made to find the optimum mechanical properties of silk wire-rope scaffold by using the back propagation artificial neural network (ANN) method. Different samples of wire-rope scaffolds were fabricated according to Taguchi experimental design. The number of filaments and twist in each layer of the four layered wire-rope silk yarn were considered as the input parameters in the model. The output parameters included the mechanical properties which consisted of UTS, elongation at break, and stiffness. Finally, sensitivity analysis on input data showed that the number of filaments and the number of twists in the fourth layer are less important than other input parameters.

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Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions

Journal of Engineered Fibers and Fabrics ◽

10.1177/155892501701200302 ◽

2017 ◽

Vol 12 (3) ◽

pp. 155892501701200 ◽

Cited By ~ 2

Author(s):

Kenan Yıldirimm ◽

Hamdi Ogut ◽

Yusuf Ulcay

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Nonlinear Regression ◽

Fine Tuning ◽

Draw Force ◽

Non Linear ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Production Conditions

In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the nonlinear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R2=0.97 vs. R2=0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.

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An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.524-527.1331 ◽

2012 ◽

Vol 524-527 ◽

pp. 1331-1334

Author(s):

Jun Ni ◽

Zhan Li Ren ◽

Guo Qing Han

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Linear Mapping ◽

Back Propagation Algorithm ◽

Propagation Algorithm ◽

Beam Pump ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Dynamometer Card

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

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Predicting the Workability of Self-Compacting Concrete Using Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.168-170.1730 ◽

2010 ◽

Vol 168-170 ◽

pp. 1730-1734

Author(s):

Fang Xian Li ◽

Qi Jun Yu ◽

Jiang Xiong Wei ◽

Jian Xin Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ann Model ◽

Self Compacting Concrete ◽

Data Set ◽

Slump Flow ◽

Super Plasticizer ◽

Input Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann

An artificial neural network (ANN) is presented to predict the workability of self compacting concrete (SCC) containing slump, slump flow and V-test. A data set of a laboratory work, in which a total of 23 concretes were produced, was utilized in the ANNs study. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, fly ash, blast furnace slag, super plasticizer, sand ratio and water/binder, three output parameters which are slump, slump flow and V-test of SCC. ANN-1, ANN-2 and ANN-3 models which containing 15 ,11 and 5 neurons in the hidden layers, respectively are found to predict workability of concrete well within the ranges of the input parameters considered. The three models are tested by comparing to the results to actual measured data. The results showed that ANN-2 is the best suitable for predicting the workability of SCC using concrete ingredients as input parameters.

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A Robust Embedded Non-Linear Acoustic Noise Cancellation (ANC) Using Artificial Neural Network (ANN) for Improving the Quality of Voice Communications

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.11.2.13010 ◽

2021 ◽

Vol 11 (2) ◽

pp. 525

Author(s):

Azeddine Wahbi ◽

Ahmed Roukhe ◽

Bahloul Bensassi ◽

Laamari Hlou

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acoustic Noise ◽

Noise Cancellation ◽

Voice Communications ◽

Non Linear ◽

Quality Of Voice ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia

MAUSAM ◽

10.54302/mausam.v71i2.22 ◽

2021 ◽

Vol 71 (2) ◽

pp. 233-244

Author(s):

PERERA ANUSHKA ◽

AZAMATHULLA HAZI MD. ◽

RATHNAYAKE UPAKA

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Saudi Arabia ◽

Conjugate Gradient ◽

Atmospheric Temperature ◽

Training Algorithms ◽

Non Linear ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Use of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare different ANN models to predict the minimum atmospheric temperature and maximum atmospheric temperature in Tabuk, Saudi Arabia. ANN models were trained using eight different training algorithms. BFGS Quasi Newton (BFG), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquadt (LM), Scaled Conjugate Gradient (SCG), Fletcher-Reeves update Conjugate Gradient algorithm (CGF), One Step Secant (OSS), Polak-Ribiere update Conjugate Gradient (CGP) and Resilient Back-Propagation (RP) training algorithms were fed to the climatic data in Tabuk, Saudi Arabia. The performance of the different training algorithms to train ANN models were evaluated using Mean Squared Error (MSE) and correlation coefficient (R). The evaluation shows that training algorithms BFG, LM and SCG have outperformed others while OSS training algorithm has the lowest performance in comparison to other algorithms used.

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Dual-Stage Artificial Neural Network (Ann) Model for Sequential Lbmm-μedm Based Micro Drilling

10.21203/rs.3.rs-385339/v1 ◽

2021 ◽

Author(s):

Wazed Ibne Noor ◽

Tanveer Saleh ◽

Mir Akmam Noor Rashid ◽

Azhar Bin Mohd Ibrahim ◽

Mohamed Sultan Mohamed Ali

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tool Wear ◽

Short Circuit ◽

Sequential Process ◽

Input Parameters ◽

Micro Drilling ◽

Dual Stage ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract A sequential process combining laser beam micromachining(LBMM) and micro electro-discharge machining (μEDM) for the micro-drilling purpose was developed to incorporate both methods' benefits. In this sequential process, a guiding hole is produced through LBMM first, followed by μEDM applied to that same hole for more fine machining. This process facilitates a more stable, efficient machining regime with faster processing (compared to pure μEDM) and much better hole quality (compared to LBMMed holes). Studies suggest that strong correlations exist between the various input and output parameters of the sequential process. However, a mathematical model that maps and simultaneously predicts all these output parameters from the input parameters is yet to be developed. Our experimental study observed that the µEDM finishing operation's various output parameters are influenced by the morphological condition of the LBMMed holes. Hence, an artificial neural network(ANN) based dual-stage modelling method was developed to predict the sequential process's outputs. The first stage of the dual-stage model was utilized to predict various LBMM process outputs from different laser input parameters. Furthermore, in the second stage, LBMM predicted outputs (such as pilot hole entry area, exit area, recast layer, and heat affected zone) were used for the final prediction of the sequential process outputs (i.e. machining time by μEDM, machining stability during μEDM in terms of short circuit count and tool wear during μEDM). The model was evaluated based on the average RMSE (Root Mean Square Errors) values for the individual output parameters' complete set data, i.e. μEDM time, short circuit count and tool wear. The values of Average RMSE for the parameters as mentioned earlier were found to be 0.1272(87.28% accuracy), 0.1085(89.15% accuracy), 0.097 (90.3% accuracy), respectively.

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Application of artificial neural network for design: a case of paper helicopter

MATEC Web of Conferences ◽

10.1051/matecconf/201819201044 ◽

2018 ◽

Vol 192 ◽

pp. 01044

Author(s):

Tossapol Kiatcharoenpol ◽

Tanaporn Klangpetch

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Iteration Process ◽

Testing Procedure ◽

Depth Of Cut ◽

Neural Network Approach ◽

Input Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Target Output

The design engineering is one of essential work in modern manufacturing environment. The optimization is principal technique to be used widely for searching the solution. However, primary process of optimization is to know the relation between design input parameters and target output. In this work, an artificial neural network (ANN) approach as an intelligent algorithm is proposed to construct the relation and also provides it in form of mathematic modeling. Even though the ANN modeling is so call a backblock due to difficulty to understand complicated equations, it is simply constructed by automate iteration process. A case of paper helicopter is used as an example of the application. The classical 2k Factorial design is used to provide an experiment plan to create training and testing data. 93 experiments are carried out. The architecture of ANN is set according to lowest Mean square error (MSE) of training and testing procedure. The result of 5-10-1 architecture has shown ability to accurately predict output, landing time, with MSE of 0.012. With such a highly quantitative accuracy of results, the developed model using the neural network approach can be used for finding the suitable input parameters to achieve a desired target output. In this case, the design of dimension (A) Depth of cut wing is 1.3 cm., (B) Length of wing is 12.9 cm., (C) Length of body is 9.0, (D) Width of body is 2.0 cm., and (E) Depth of cut body is 0 cm. yield the lowest area of a paper helicopter that can meet the target landing time, 2.85 + 5% second.

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