scholarly journals Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table

2020 ◽  
Vol 306 ◽  
pp. 03004
Author(s):  
Biswas Prabir ◽  
Mondal Md Safwan ◽  
Mookherjee Saikat ◽  
Mandal Pranibesh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cooling Rate ◽  
Flow Rate ◽  
Empirical Relation ◽  
Steel Grade ◽  
Laboratory Scale ◽  
Plate Velocity ◽  
Long Time ◽  
Artificial Neural

Run Out Tables (ROTs) have been used for long time in order to achieve different microstructure of steel in the industries. The microstructure of steel controlled by the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others. Achieving new steel grade thus demand a proper combination setting of all such parameters. The observed data like upper nozzle distance, lower nozzle distance and mass flow rate of coolant from the laboratory scale ROTs are used to find out the cooling rate which is important parameter for achieving desired properties in steel. An Artificial Neural Network has been used here to creating an empirical relation between the observed data and thermodynamics parameter which will determine the cooling rate and validate it.

Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

2020 ◽  
Author(s):  
Yangping Li ◽  
Yangyi Liu ◽  
Sihua Luo ◽  
Zi Wang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Cooling Rate ◽  
Volume Fraction ◽  
Sem Images ◽  
Mechanical Hardness ◽  
Nickel Based Superalloy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

Artificial Neural Network in Operation Management Regarding Communication Issue

2021 ◽  
pp. 2303-2320
Author(s):  
Ayan Chatterjee ◽  
Susmita Sarkar ◽  
Mahendra Rong ◽  
Debmallya Chatterjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travelling Salesman Problem ◽  
Operation Management ◽  
Routing Problem ◽  
Important Concern ◽  
Management Area ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Communication issue in operation management is important concern in the age of 21st century. In operation, communication can be described based on major three wings- Travelling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and Transportation Problem (TP). Artificial Neural Network (ANN) is an important tool to handle these systems. In this chapter, different ANN based models are discussed in a comprehensive way. This chapter deals with how various approaches of ANN help to design the optimal communication network. This comprehensive study is important to the decision makers for the analytical consideration. Although there is a lot of development in this particular domain from a long time ago; but only the revolutionary contributed models are taken into account. Another motivation of this chapter is understanding the importance of ANN in the operation management area.

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

2013 ◽  
Vol 13 (2) ◽  
pp. 1085-1098 ◽  
Author(s):  
Mohammad Ali Ahmadi ◽  
Mohammad Ebadi ◽  
Amin Shokrollahi ◽  
Seyed Mohammad Javad Majidi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm ◽  
Oil Flow ◽  
Artificial Neural

scholarly journals Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 578
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Stream Flow ◽  
Low Flow ◽  
Urban Stream ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Artificial Neural

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

Prediction of Resilient Modulus for Hot Mix Asphalt Based on Artificial Neural Network

2011 ◽  
Vol 304 ◽  
pp. 18-23
Author(s):  
Chun Hua Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Hot Mix Asphalt ◽  
Resilient Modulus ◽  
Structure Design ◽  
Forecast Precision ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

A high-efficient batch-recirculated photoreactor packed with immobilized TiO2-P25 nanoparticles onto glass beads for photocatalytic degradation of phenazopyridine as a pharmaceutical contaminant: artificial neural network modeling

2016 ◽  
Vol 73 (11) ◽  
pp. 2804-2814 ◽  
Author(s):  
Mahdie Shargh ◽  
Mohammad A. Behnajady
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Light Source ◽  
Flow Rate ◽  
Irradiation Time ◽  
Volumetric Flow Rate ◽  
Glass Beads ◽  
Initial Concentration ◽  
Immobilized Tio2 ◽  
Artificial Neural

In this study, removal efficiency of phenazopyridine (PhP) as a model pharmaceutical contaminant was investigated in a batch-recirculated photoreactor packed with immobilized TiO2-P25 nanoparticles on glass beads. Influence of various operational parameters such as irradiation time, initial concentration of PhP, volume of solution, volumetric flow rate, pH and power of light source was investigated. Results indicated that removal percentage increases with the rise of irradiation time, volumetric flow rate and power of light source but decreases with the rise of initial concentration of PhP and volume of solution. Highest removal percentage was obtained in the natural pH of PhP solution (pH = 5.9). Results of mineralization studies also showed a decreasing trend of total organic carbon (TOC) and producing mineralization products such as NO3−, NO2− and NH4+. Modeling of the process using artificial neural network showed that the most effective parameters in the degradation of PhP were volume of solution and power of light source. The packed bed photoreactor with TiO2-P25 nanoparticles coated onto glass beads in consecutive repeats have the proper ability for PhP degradation. Therefore, this system can be a promising alternative for the removal of recalcitrant organic pollutants such as PhP from aqueous solutions.

Long-Term Sales Forecast Method Based on Grey Model and Artificial Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 1726-1729
Author(s):  
Yi Jun Wang ◽  
Hong Ying Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulation Experiment ◽  
Grey Model ◽  
Forecast Method ◽  
Sales Forecast ◽  
Long Time ◽  
Artificial Neural ◽  
The U.S

Long-term sales forecasting is a problem that has been focused on for a long time. In order to forecast the long-term sales of an industry or an enterprise accurately, a new method based on Grey Model and Artificial Neural Network is proposed in this paper. The effectiveness and feasibility of the proposed method is verified by simulation experiment using sales data of the manufacturing and trade industry provided by the U.S. government.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

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