scholarly journals Measurement and calculation of calorific value of raw coal based on artificial neural network analysis method

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3129-3137 ◽  
Author(s):  
Peng Liu ◽  
Shuran Lv
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rbf Neural Network ◽  
Small Sample Size ◽  
Calorific Value ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Small Sample ◽  
Rbf Network ◽  
Artificial Neural

The calorific value of coal is the basic technical basis for calculating parameters such as boiler heat balance, thermal efficiency, and boiler output. The calorific value of coal has different meanings, such as the calorific value of the cartridge, the high calorific value of coal, and the low calorific value of coal to generate heat at a high level of constant humidity and no ash. This paper focuses on the analysis of the structure and algorithm characteristics of artificial neural network and RBF neural network. On this basis, the predictive modelling of the received low-level calorific value is carried out. Through the test summary, the predictiveness of the neural network is better than the empirical formula. For the prediction problem with small sample size, the RBF network has better prediction performance. In addition, the quality of the sample, including its quantity and comprehensiveness, has an important impact on the predictive performance and generalization ability of the model.

Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions

2020 ◽  
Vol 10 (2) ◽  
pp. 154-162
Author(s):  
Engin Özdemir ◽  
Didem Eren Sarici
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Calorific Value ◽  
Volatile Matter ◽  
Ash Content ◽  
Percentage Error ◽  
Multiple Regression Models ◽  
Artificial Neural

Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values. Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor. Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined. Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90). Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.

The Prediction Research on the Transport & Storage of Guangxi in China with the GMDH Algorithm Method

2010 ◽  
Vol 143-144 ◽  
pp. 1195-1199
Author(s):  
Zhi Gao Liao ◽  
Zhi Ping Lu ◽  
Chen Yu Lu ◽  
Wen Fang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Prediction Model ◽  
Economic System ◽  
Small Sample ◽  
Car Ownership ◽  
Traditional Methods ◽  
History Data ◽  
Artificial Neural

This paper uses GMDH method to establish a prediction model to forecast the output value of transport&storage of Guangxi in China, since the original samples of the output value of transport&storage are less enough to be used with the traditional methods. Compared with traditional linear regression and artificial neural network, the predicted results show that GMDH method is an effective way to predict car ownership with a small sample of history data in the complex economic system.

Artificial Neural Network Based Step-Length Prediction Using Ultrasonic Sensors from Simulation to Implementation in Shoe-Type Measurement Device

2017 ◽  
Vol 21 (2) ◽  
pp. 321-329 ◽  
Author(s):  
Romy Budhi Widodo ◽  
◽  
Chikamune Wada ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Therapist ◽  
Predictive Performance ◽  
Step Length ◽  
Ultrasonic Sensors ◽  
Measurement Device ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Step-length measurement as a spatial gait parameter is useful for the physician and physical therapist for determining the patient’s gait condition. We hypothesized that this could be determined using ultrasonic sensors mounted on a shoe-type measurement device. For that purpose, we have developed a shoe-type measurement device to measure gait parameters. Our system was found to effectively measure step-length and pressure distribution. However, we found that the presence of shoes leads to perishable and fragile conditions for the sensors. Therefore, we redesigned the number, angle, and range of the ultrasonic sensors mounted on the shoes in order to clarify and improve the step-length prediction. This paper discusses the improvement of a shoe-type measurement device from the implementation with real shoes and the step-length prediction using an artificial neural network (ANN). The results of the experiment show that the number, angle, and positioning of ultrasonic sensors affect their ability to capture the human step region, that is, 50×70 cm under the experimental condition of foot progression angle up to 30 degrees. The results of the predictive performance of step-length using the proposed ANN architecture demonstrate an improvement.

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