Application of Mathematical Model using Artificial Neural Network in Photovoltaic Electrical Power Generation Building

The influence of storage time, temperature, and packaging on some physicochemical characteristics of gluten-free rice-buckwheat cookies was studied. Shelf life markers, such as water activity (aw), hydroxymethylfurfural (HMF), firmness, and color parameters were modelled in relation to different storage conditions. Principal component analysis was applied to study the similarity among samples according to the observed parameters. The mathematical model in the form of an artificial neural network was developed to predict the physicochemical parameters of cookies during 6-month storage. The most evident differentiation among samples was observed for color coordinate a*, aw , and HMF. Regarding the methods for determination of the parameters, priority should be given to the instrumental determination of color as the most convenient method. The processing of experimental data allowed the creation of useful mathematical model to be used in predicting the behavior of physicochemical changes of cookies by different factor combinations during storage.

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Comparison of different artificial neural network techniques in prediction of solar radiation for power generation using different combinations of meterological variables

2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) ◽

10.1109/pedes.2014.7042063 ◽

2014 ◽

Cited By ~ 10

Author(s):

Amit Kumar Yadav ◽

Hasmat Malik

Keyword(s):

Neural Network ◽

Power Generation ◽

Artificial Neural Network ◽

Solar Radiation ◽

Artificial Neural

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Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation and RSM (Response Surface Methodology) Model for Optimal Performance of Sliver Cutting Operation of Bamboo

Procedia Materials Science ◽

10.1016/j.mspro.2014.07.105 ◽

2014 ◽

Vol 6 ◽

pp. 877-891 ◽

Cited By ~ 3

Author(s):

C.N. Sakhale ◽

S.N. Waghmare ◽

S.K. Undirwade ◽

V.M. Sonde ◽

M.P. Singh

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Network Simulation ◽

Optimal Performance ◽

Neural Network Simulation ◽

Cutting Operation ◽

Artificial Neural

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Predicting Model in the Smelting Magnesium by Silicon-Thermo-Reduction Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.146-147.571 ◽

2010 ◽

Vol 146-147 ◽

pp. 571-574

Author(s):

Liang Bo Ji ◽

Yong Zhi Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Mathematical Model ◽

Artificial Neural Network ◽

Process Parameters ◽

Effective Means ◽

Limited Liability ◽

Predicting Model ◽

Artificial Neural ◽

Set Up

This paper described the application of neural networks in predicting the rate of producing magnesium by silicon-thermo-reduction. Fir st of all, a mathematical model between the process parameters and the the rate of producing magnesium was set up with neural network. When the model was satisfied, it could be used for predicting the rate of producing magnesium. Through doing a great number of productive tests in the winca(hebi) magnesium company with limited liability according to the satisfied model, the rate of the producing magnesium is increasing obviously. So it is a kind of effective means for increasing producing magnesium by silicon-thermo-reduction.

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MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

International Journal of Computational Materials Science and Engineering ◽

10.1142/s2047684113500048 ◽

2013 ◽

Vol 02 (01) ◽

pp. 1350004 ◽

Cited By ~ 1

Author(s):

S. RATH ◽

P. P. SENGUPTA ◽

A. P. SINGH ◽

A. K. MARIK ◽

P. TALUKDAR

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Artificial Neural Network ◽

Flow Stress ◽

Mathematical Models ◽

Hybrid Model ◽

Data Driven ◽

Roll Force ◽

Hot Strip ◽

Artificial Neural

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

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