Handling Incomplete and Missing Data in Corrosion Pit Measurement Database Using Imputation Methods: Model Development Using Artificial Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 04021033
Author(s):  
Haile Woldesellasse ◽  
Solomon Tesfamariam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Missing Data ◽  
Model Development ◽  
Imputation Methods ◽  
Artificial Neural ◽  
Corrosion Pit

scholarly journals Model for Predicting the Machinability of Continuously Cast and Subsequently Rolled Steel Using the Artificial Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 381-386
Author(s):  
Miha Kovačič ◽  
Shpetim Salihu ◽  
Uroš Župerl
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
Chemical Elements ◽  
Cast Steel ◽  
Model Development ◽  
Cutting Speed ◽  
Steel Production ◽  
Continuously Cast ◽  
Artificial Neural

The paper presents a model for predicting the machinability of steels using the method of artificial neural networks. The model includes all indicators from the entire steel production process that best predict the machinability of continuously cast steel. Data for model development were obtained from two years of serial production of 26 steel grades from 255 batches and include seven parameters from secondary metallurgy, four parameters from the casting process, and the content of ten chemical elements. The machinability was determined based on ISO 3685, which defines the machinability of a batch as the cutting speed with a cutting tool life of 15 minutes. An artificial neural network is used to predict this cutting speed. Based on the modelling results, the steel production process was optimised. Over a 5-month period, an additional 39 batches of 20MnV6 steel were produced to verify the developed model.

Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification

2017 ◽  
Vol 8 (1) ◽  
pp. 137
Author(s):  
Wan n Nazirah Wan Md Adna ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Use ◽  
Model Development ◽  
Operating Time ◽  
Electrical Energy ◽  
Measurement And Verification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hybrid Artificial Neural Network

This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.

scholarly journals Development of missing data prediction model for carbon monoxide

2019 ◽  
Vol 15 (1) ◽  
pp. 13-17
Author(s):  
Nurul Latiffah Abd Rani ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Ku Mohd Kalkausar Ku Yusof ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Missing Data ◽  
Prediction Model ◽  
Optimum Number ◽  
Data Prediction ◽  
Artificial Neural ◽  
Input Variables ◽  
Missing Data Prediction

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.

scholarly journals Modeling Baseline Energy Using Artificial Neural Network – A Small Dataset Approach

2018 ◽  
Vol 12 (2) ◽  
pp. 662
Author(s):  
Wan Nazirah Wan Md Adnan ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Validation ◽  
Model Development ◽  
Energy Model ◽  
Ann Model ◽  
Data Set ◽  
Validation Technique ◽  
Artificial Neural ◽  
Small Dataset

In this work, baseline energy model development using Artificial Neural Network (ANN) with resampling techniques; Cross Validation (CV) and Bootstrap (BS) are presented. Resampling techniques are used to examine the ability of the ANN model to deal with a small dataset. Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to evaluate the model accuracy. For this analysis, R is calculated for the entire data set (R_all) and separately for training set (R_train), validation set (R_valid) dan testing set (R_test). The closer R to 1, the higher similarities between targeted and predicted output. The total of two different models with several number of neurons are developed and compared. It can be concluded that all models are capable to train the network. Artificial Neural Network with Bootstrap Cross Validation technique (ANN-BSCV) outperforms Artificial Neural Network with Cross Validation technique (ANN-CV).  The 3-6-1 ANN-BSCV, with R_train = 0.95668, R_valid = 0.97553, R_test = 0.85726 and R_all = 0.94079 is selected as the baseline energy model to predict energy consumption for Option C IPMVP.

scholarly journals Improving prediction of aphid flights by temporal analysis of input data for an artificial neural network

2002 ◽  
Vol 55 ◽  
pp. 312-316 ◽  
Author(s):  
S.P. Worner ◽  
G.O. Lankin ◽  
S. Samarasinghe ◽  
D.A.J. Teulon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Model Development ◽  
Temporal Analysis ◽  
Weather Data ◽  
Flight Period ◽  
Noisy Information ◽  
Artificial Neural ◽  
Trap Catches

Weather data in its raw form frequently contains irrelevant and noisy information Often the hardest task in model development regardless of the technique used is translating independent variables from their raw form into data relevant to a particular model A sequential or cascading temporal correlation analysis was used to identify weather sequences that were strongly correlated with aphid trap catches recorded at Lincoln Canterbury New Zealand over 19822000 Trap catches in the previous year and 13 weather sequences associated with eight climate variables were identified as significant predictors of aphid trap catch during the autumn flight period The variables were used to train artificial neural network (ANN) models to predict the size of autumn aphid migrations into cereal crops in Canterbury Such models would assist cereal growers to make better informed and more timely pest management decisions ANN predictive performance was compared with multiple regression predictions using jackknifed data The ANN gave superior prediction compared with multiple regression over 13 jackknifed years

Utilizing Artificial Neural Network for Real-Time Prediction of Differential Sticking Symptoms

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Dalila Gomes ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Model Development ◽  
Data Sets ◽  
Proper Training ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Stuck pipe is one of the leading causes of non-productive time (NPT) while drilling. Machine learning (ML) techniques can be used to predict and avoid stuck pipe issues. In this paper, a model based on ML to predict and prevent stuck pipe related to differential sticking (DS) is presented. The stuck pipe indicator is established by detecting and predicting abnormalities in the drag signatures during tripping and drilling activities. The solution focuses on detecting differential sticking risk via assessing hookload signatures, based on previous experience from historical wells. Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues. The solution is based on the Artificial Neural Network (ANN) approach. The model is designed to provide users, i.e., driller or monitoring specialist, a warning whenever a risk is identified. Since multi-step forecasting is used, the warning is given with enough time for the driller or monitoring specialist to evaluate which preventative action or intervention is necessary. The warnings are provided typically between 30 minutes and 4 hours ahead. The model validation includes the performance metrics and a confusion matrix. Practical cases with real-time wells are also provided. The ML model was proven robust and practical with our data sets, for both historical and live wells. The huge amount of data produced while drilling holds valuable information and when smartly fed into an Artificial Intelligence (AI) model, it can prevent NPT such as stuck pipe events as demonstrated in this paper.

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