Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions

Haghbakhsh, Reza; Adib, Hooman; Keshavarz, Peyman; Koolivand, Mehdi; Keshtkari, Simin

doi:10.1016/j.tca.2012.10.022

Cited by 50 publications

(21 citation statements)

References 34 publications

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“…Table 3 gives evaluation results of the models in terms of the values of these criteria. It is important to note that the ANN predictions are optimum if RMSE and MAPE are found to be close to 0, while R 2 are found to be close to 1 (Haghbakhsh et al 2013). According to Table 3, the RMSE values for the test phase were determined as 0,308 and 0,491 in predicting volumetric swelling and shrinkage of samples, respectively.…”

Section: Modeling Resultsmentioning

confidence: 96%

“…Among many different kinds of ANNs, the multi-layer perceptron (MLP) is known as the most useful type. It is a feed-forward architecture that is capable of mapping the set of input data onto a set of proper outputs (Haghbakhsh et al 2013). The MLP architecture comprises a combination of input, hidden and output layers.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…They are connected to each other by a weight factor analogously to the biological nervous system (Zhang et al 1998). The main task of the connection weights is to store the information and reveal the relationship between input and output data (Haghbakhsh et al 2013). …”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…Recently, artificial neural networks (ANNs) have attracted attention of researchers as a new method in predicting various properties of materials. ANNs offer effective solutions to deal with complex problems and to uncover unknown relationship between input and output variables (Haghbakhsh et al 2013). ANNs run based on learning and, when the established network is trained using an adequate number of data related to the problem, a new input that has a similar pattern can be predicted based on the previous learning pattern ( Avunduk et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Tiryaki

Bardak

Aydın

et al. 2016

Maderas, Cienc. tecnol.

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Shrinkage and swelling characteristics of wood as a hygroscopic material affect negatively its effective utilization for a variety of applications. Heat treatment is widely used for minimizing the negative effects of volumetric swelling and shrinkage of wood. The present study aims to develop artificial neural network (ANN) models for predicting volumetric swelling and shrinkage of heat treated woods. For this purpose, wood samples were subjected to heat treatment at varying temperatures (130, 150, 170 and 190 ºC) for varying durations (2, 4, 6 and 8 h). Experimental results have showed that volumetric swelling and shrinkage of wood decreased by heat treatment. Then, neural networks models capable of predicting the swelling and shrinkage of the treated woods were developed based on the resulting data. It was seen that ANN models allowed volumetric swelling and shrinkage of such woods to predict successfully with a limited set of experimental data. This approach was able to predict volumetric swelling and shrinkage of wood with a mean absolute percentage error equal to 2,599% and 2,647% in test phase, respectively. The developed models might thus serve as a robust tool to predict volumetric swelling and shrinkage with less number of experiments.

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Section: Modeling Resultsmentioning

confidence: 96%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Tiryaki

Bardak

Aydın

et al. 2016

Maderas, Cienc. tecnol.

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“…Commonly, 80% of the data is used for training and the other 20% is categorized as 10% for optimization and 10% for testing set. [44,45]. Also, it must be mentioned that the optimization algorithm which has been devised to obtain the optimum values of Kernel parameter such as σ 2 during the internal SVM calculations.…”

Section: Svm Simulationsmentioning

confidence: 99%

Experimental investigation and development of a SVM model for hydrogenation reaction of carbon monoxide in presence of Co–Mo/Al2O3 catalyst

Anbari

Adib

Iranshahi

2015

Chemical Engineering Journal

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A machine learning approach for modelling and optimization of complex systems: Application to condensate stabilizer plants

Alkatheri,

Hourfar,

Khoshnevisan

et al. 2024

Can J Chem Eng

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Recent advancements in supervised machine learning tools have demonstrated their ability to achieve accurate and efficient prediction results. In this paper, we leverage these tools as alternative approaches to model a specific application in the gas industry, based on operating data. The chosen application is a natural gas condensate stabilization process, in which light end components are removed to reduce condensate vapour pressure, meeting storage and transportation specifications. Here, we develop and evaluate various supervised machine learning models to predict the performance of two industrial condensate stabilizer units. By utilizing large datasets from these units and encompassing comprehensive operating data of input–output variables, we not only demonstrate the capability of these techniques to offer reliable and accurate predictions but also shed light on their potential impacts and implementations. The impacts of applying selected AI and machine learning algorithms are two‐fold. First, our research presents an innovative approach to process modelling and optimization in the gas industry, showing the potential for enhanced operational efficiency, profitability, and safety. Second, we propose a data‐driven surrogate‐based optimization framework, where the generated machine learning models can replace detailed first‐principle models, offering a streamlined method to find optimal values for variables to reduce operational energy consumption. Furthermore, we address the validation requirements of our machine learning models, ensuring their robustness and reliability in real‐world applications. By incorporating rigorous validation procedures, we guarantee the quality of our predictions and support their practical implementation. In conclusion, our research not only highlights the capabilities of machine learning in gas industry applications but also emphasizes their potential impacts and contributions to operational excellence. So, the presented approach can pave the way for improved performance, efficiency, and profitability in the gas industry.

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Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions

Cited by 50 publications

References 34 publications

Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Experimental investigation and development of a SVM model for hydrogenation reaction of carbon monoxide in presence of Co–Mo/Al2O3 catalyst

A machine learning approach for modelling and optimization of complex systems: Application to condensate stabilizer plants

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