An intelligent model to predict the life condition of crude oil pipelines using artificial neural networks

Shaik, Nagoor Basha; Pedapati, Srinivasa Rao; Othman, Abdul Rahim; Bingi, Kishore; Dzubir, Faizul Azly Abd

doi:10.1007/s00521-021-06116-1

Cited by 27 publications

(23 citation statements)

References 42 publications

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“…Similarly, the mathematical formula of the “purelin” and the delta rule to update weight

are shown in Figure 3 ’s green dotted box. The non-linear and linear combination of functions was used to achieve efficient training [ 38 , 39 ]. Further, Levenberg–Marquardt’s approach was employed to calculate the new weights

and

during the training, as shown in Figure 3 .…”

Section: Materials and Methodsmentioning

confidence: 99%

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Salehuddin

Omar

Ibrahim

et al. 2022

Sensors

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Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and being costly. Hence, we designed an automated model based on an artificial neural network to predict Saybolt color. The network has been built with five input variables, density, kinematic viscosity, sulfur content, cetane index, and total acid number; and one output, i.e., Saybolt color. Two backpropagation algorithms with different transfer functions and neurons number were tested. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to assess the performance of the developed model. Additionally, the results of the ANN model are compared with the multiple linear regression (MLR). The results demonstrate that the ANN with the Levenberg–Marquart algorithm, tangent sigmoid transfer function, and three neurons achieved the highest performance (R2 = 0.995, MAE = 1.000, and RMSE = 1.658) in predicting the Saybolt color. The ANN model appeared to be superior to MLR (R2 = 0.830). Hence, this shows the potential of the ANN model as an effective method with which to predict Saybolt color in real time.

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“…Similarly, the mathematical formula of the “purelin” and the delta rule to update weight

and

during the training, as shown in Figure 3 .…”

Section: Materials and Methodsmentioning

confidence: 99%

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Salehuddin

Omar

Ibrahim

et al. 2022

Sensors

Self Cite

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“…As a result, this layer transformed 15 input variables into 20 parameters. The activation for the first neuron in a first layer can be mathematically written as Equation (), 41

a_{1}^{1} goodbreak= g ((), \sum_{i = 1}^{15} W_{1 i}^{1} {us}_{i} goodbreak+ W_{1 b}^{1} B^{1})

W_{1 i}^{1}

is the weight associated with each input

i

for the first neuron in the first layer.

W_{1 b}^{1}

is the weight associated with biased term for the first neuron in the first layer.…”

Section: Pipeline Safety Management System Using Neural Networkmentioning

confidence: 99%

Gas pipeline safety management system based on neural network

Mujtaba

Lemma

Vandrangi

2022

Process Safety Progress

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The risk of leakage poses a grave threat to natural gas pipeline safety. The high compressibility of gases combined with unsteady boundary conditions makes detecting leaks in pipelines a challenging endeavor. To date, in the literature, only a limited number of studies have focused on leak detection and diagnostics in gas mixture pipelines. The present study provides a system for detecting, locating, and estimating the size of small gas leaks from a compressible and dynamic natural gas flow in pipelines with improved accuracy. As a case study, a long natural gas pipeline of 80 km is simulated with leak sizes of 0%, 2%, and 5%. The safety system is developed using mass flow rate, temperature, and pressure measurements. Six classes for faulty cases and one class for no fault case were considered for the study. A shallow neural network classifier (SNNC) is trained to identify a specific fault class. The SNNC is based on a two-layered network with 20 and 7 neurons. An input vector of 15 variables is provided to the system, and the output is one of the seven possible classes. Leakage as low as 2% at various locations are correctly diagnosed with more than 99% correct classification rate.

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“…e architecture shows that the number of nodes in the input, hidden, and output layers is 2 : 10 : 1. In the figure, bias at the hidden and output layer is denoted as b [10,25]. e activation function chosen at the output layer is purelin, while the activation function "F" at the hidden layer is varied to evaluate the performance.…”

Section: Simulation Studymentioning

confidence: 99%

Fractional Rectified Linear Unit Activation Function and Its Variants

Job

Bhateja

Gupta

et al. 2022

Mathematical Problems in Engineering

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This paper focuses on deriving and validating the fractional-order form of rectified linear unit activation function and its linear and nonlinear variants. The linear variants include the leaky and parametric, whereas the nonlinear variants include the exponential, sigmoid-weighted, and Gaussian error functions. Besides, a standard formula has been created and used while developing the fractional form of linear variants. Moreover, different expansion series such as Maclaurin and Taylor have been used while designing the fractional version of nonlinear variants. A simulation study has been conducted to validate the performance of all the developed fractional activation functions utilizing a single and multilayer neural network model and to compare them with their conventional counterparts. In this simulation study, a neural network model has been created to predict the system-generated power of a Texas wind turbine. The performance has been evaluated by varying the activation function in the hidden and output layers with the developed functions for single and multilayer networks.

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An intelligent model to predict the life condition of crude oil pipelines using artificial neural networks

Cited by 27 publications

References 42 publications

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products

Gas pipeline safety management system based on neural network

Fractional Rectified Linear Unit Activation Function and Its Variants

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