Sensitivity analysis in using of artificial neural network models to determine infill well locations in a mature oil field

Fathaddin, Muhammad Taufiq; Arshanda, Muhammad Ichsan; Rachman, Y.A.; Putra, Erwinsyah; Nugrahanti, Asri; Kasmungin, Sugiatmo

doi:10.1088/1755-1315/212/1/012070

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…The main goal is to reduce the number of input parameters and to improve the accuracy of the ANN models with less error using sensivity analysis. Sensitivity analysis has been used in various applications like prediction, optimization and so on [21][22][23][24][25]. Mohammad Hasan Shojaeefard applied sensitivity analysis approach in Friction Stir Lap Joining of Aluminum to Brass [26].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

Meenal¹,

Selvakumar²,

Michael³

et al. 2020

IJEECS

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The objective of this paper is to build an artificial neural network model to predict Global Solar Radiation (GSR) with improved accuracy using less number of best input parameters selected using sensitivity analysis. In this work, the input parameters used for training the artificial neural network (ANN) models are bright sunshine duration, maximum and minimum temperature, day length, months, extra terrestrial radiation (H0), relative humidity and geographical parameters of the locations namely the latitude and longitude. Sensitivity analysis is used to discover how the output data are influenced by the changeability of the input data.Three ANN models namely T-ANN, S-ANN and TS-ANN are proposed with most suitable input parameters selected using sensitivity analysis. The principle of this feature selection using sensitivity analysis is to improve the prediction accuracy of solar radiation models with less number of inputs. The proposed ANN model is also tested under noisy data and proved that ANN is able to perform reasonably good in GSR prediction on practical applications where the data is affected by noise caused by errors on measuring, fault of data acquisition system, recording problems, and so on.

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Section: Introductionmentioning

confidence: 99%

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

Meenal¹,

Selvakumar²,

Michael³

et al. 2020

IJEECS

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Sensitivity Analysis and Anaerobic Digestion Modeling: A Scoping Review

Barahmand

Samarakoon

2022

Fermentation

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A growing awareness of global climate change has led to an increased interest in investigating renewable energy sources, such as the anaerobic digestion of biomass. This process utilizes a wide range of microbial communities to degrade biodegradable material in feedstock through a complex series of biochemical interactions. Anaerobic digestion exhibits nonlinear dynamics due to the complex and interacting biochemical processes involved. Due to its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions, anaerobic digestion is highly susceptible to instabilities. Therefore, in order to model and operate a biogas production unit effectively, it is necessary to understand which parameters are most influential on the model outputs. This also reduces the amount of estimation required. Through a scoping review, the present study analyzes the studies on the application of sensitivity analysis in anaerobic digestion modeling. Both local and global sensitivity analysis approaches were carried out using different mathematical models. The results indicate that anaerobic digestion model no.1 (ADM1) was the most commonly used model for analyzing sensitivity. Both local and global sensitivity analyses are widely employed to investigate the influence of key model parameters such as kinetic, stoichiometric, and mass transfer parameters on model outputs such as biogas production, methane concentration, pH, or economic viability of the plant.

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Sensitivity analysis in using of artificial neural network models to determine infill well locations in a mature oil field

Cited by 2 publications

References 2 publications

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

Sensitivity Analysis and Anaerobic Digestion Modeling: A Scoping Review

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