Name
Title | ||
|---|---|---|
NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment |
Abstract | ||
|---|---|---|
Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2012.08.018 | Expert Syst. Appl. |
Keywords | Field | DocType |
prediction interval,gas equipment,accurate prediction,coverage probability,nn structure,scale deposition rate,nsga-ii-trained neural network approach,associated prediction uncertainty,experimental data,case study,nsga-ii-trained neural network,scale deposition,prediction intervals,neural networks,cross validation | Data mining,Experimental data,Computer science,Sorting,Prediction interval,Artificial intelligence,Artificial neural network,Coverage probability,Cross-validation,Machine learning,Pareto principle,Genetic algorithm | Journal |
Volume | Issue | ISSN |
40 | 4 | 0957-4174 |
Citations | PageRank | References |
19 | 1.01 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronay Ak | 1 | 47 | 4.61 |
| Y. F. Li | 2 | 422 | 29.24 |
| Valeria Vitelli | 3 | 72 | 6.93 |
| Enrico Zio | 4 | 1100 | 145.38 |
| Enrique López Droguett | 5 | 121 | 9.20 |
| Carlos Magno Couto Jacinto | 6 | 19 | 1.35 |