Moisture Content of Crude Oil Based on BP Neural Network Algorithm to Predict in Oilfield

2012 ◽  
Vol 170-173 ◽  
pp. 1290-1293 ◽  
Author(s):  
Ying Ming Zhou ◽  
Qiu Shi Wang ◽  
Peng Wang ◽  
Li Na Yao
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Crude Oil ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Implementation Process ◽  
Evaluation Index ◽  
Precision Control ◽  
Neural Network Algorithm ◽  
Bp Neural Network Model

This paper proposed new method of testing a moisture content of the crude oil which is based on BP neural network. It describes the principle of BP neural network model and calculation method to predict the moisture content of crude oil. The normalization of evaluation index and the implementation process of this method in computers. In the end, an application example of this method used in the process of practice and precision control requirements.

scholarly journals Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process

Forests ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Haojie Chai ◽  
Xianming Chen ◽  
Yingchun Cai ◽  
Jingyao Zhao
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
High Frequency ◽  
Neural Network Modeling ◽  
Drying Process ◽  
Measuring Point ◽  
Bp Neural Network Model

The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.

An Application of BP Neural Network Model to Predict the Moisture Content of Crude Oil

2012 ◽  
Vol 524-527 ◽  
pp. 1327-1330 ◽  
Author(s):  
Ying Ming Zhou ◽  
Shu Wei Wang ◽  
Lin Lin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Moisture Content ◽  
Crude Oil ◽  
Heavy Oil ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Bp Neural Network Model

With the constant expansion of super heavy oil SAGD conversion development, the accurate testing of the crude oil in the high moisture content range is particularly important. In this paper, against the characteristics of Adopting SAGD technology exploiting heavy oil, BP neural network prediction model and calculation method has been adopted to predict the moisture content of crude oil. Through the study, the experimental data of the model were verified by the maximum prediction error is less than 3%, the accuracy of the forecast moisture content of crude oil to meet the site requirements. Through this study, the experimental data to the model was validated by the maximum prediction error is less than3%, the prediction accuracy of which to moisture content of crude oil is able to meet the requirements of the project site.

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

Misfire Diagnosis of Diesel Engine Based on Short-Time Vibration Characters

2010 ◽  
Vol 34-35 ◽  
pp. 301-305
Author(s):  
Zhao Qian Zhu ◽  
Jue Yang ◽  
Xiao Ming Zhang ◽  
Xiao Lei Li
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Monitoring Test ◽  
Bp Neural Network Model ◽  
Short Time

This paper studied misfire diagnosis of diesel engine based on short-time vibration characters. Misfire of diesel engine was simulated by the vibration monitoring test. Cylinder vibration signal and top center signal were collected under different states. The short-time vibration signal of each cylinder was intercepted according to the diesel combustion sequence, effective value was calculated, and BP Neural Network model built with this character was used to diagnose diesel misfire. The result shows that this method can locate the misfire cylinder effectively, and it is meaningful for guiding the detection and repair of vehicles.

Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network

2014 ◽  
Vol 1003 ◽  
pp. 226-229 ◽  
Author(s):  
Ying Hong Xie ◽  
Xiao Wei Han ◽  
Qi Li
Keyword(s):  
Neural Network ◽  
Information Technology ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Evolution Model ◽  
Good Reliability ◽  
Bp Neural Network Model ◽  
Near Future ◽  
Xi’An City

In this paper, BP neural network model is used to establish the occurrence and evolution model of PM2.5 in an area in Xi'an city. In the model, wind, humidity, season, SO2,NO2,PM10, CO,O3 (in one hour ) and O3 (in eight hours ) and other influence factors are all considered. The model has good reliability, it can accurately forecast the value of PM2.5 and its variation in the near future, which can provide the basis for the PM2.5 control.

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