scholarly journals Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation

2019 ◽  
Vol 24 (2) ◽  
pp. 697-707 ◽  
Author(s):  
Dominique Guérillot ◽  
Jérémie Bruyelle
Keyword(s):  
Flow Simulation ◽  
Fluid Injection ◽  
Equilibrium Calculation ◽  
Transfer Processes ◽  
Reservoir Flow ◽  
Equilibrium And Kinetics ◽  
Geological Formation ◽  
Artificial Neural ◽  
Time Required

AbstractThe fluid injection in sedimentary formations may generate geochemical interactions between the fluids and the rock minerals, e.g., CO2 storage in a depleted reservoir or a saline aquifer. To simulate such reactive transfer processes, geochemical equations (equilibrium and kinetics equations) are coupled with compositional flows in porous media in order to represent, for example, precipitation/dissolution phenomena. The aim of the decoupled approach proposed consists in replacing the geochemical equilibrium solver with a substitute method to bypass the huge consuming time required to balance the geochemical system while keeping an accurate equilibrium calculation. This paper focuses on the use of artificial neural networks (ANN) to determine the geochemical equilibrium instead of solving geochemical equations system. To illustrate the proposed workflow, a 3D case study of CO2 storage in geological formation is presented.

scholarly journals ANALISIS PENGARUH NILAI KONDUKTIVITAS HIDRAULIK DAN DISPERSIVITAS DINAMIK TERHADAP REMEDIASI AIR TANAH MENGGUNAKAN SIMULASI NUMERIK

2021 ◽  
Vol 12 (2) ◽  
pp. 107-118
Author(s):  
Agus Mochamad Ramdhan ◽  
Arifin Arifin ◽  
Erik Hermawan ◽  
Lambok M. Hutasoit
Keyword(s):  
Numerical Simulation ◽  
Sensitivity Analysis ◽  
Hydraulic Conductivity ◽  
Groundwater Remediation ◽  
Flow Simulation ◽  
Pumping Wells ◽  
Time Required ◽  
Close Distance ◽  
High Hydraulic Conductivity

Groundwater remediation is one of the solutions to restore the contaminated groundwater. This study was conducted to determine the effect of hydraulic conductivity and dynamic dispersivity on the groundwater remediation effectiveness. As a case study, in 2020, in an area located in Balikpapan, groundwater remediation will be carried out by injecting water containing NaOH through five wells and pumping it back through five wells to form a cycle. The method used is a numerical simulation consisting of groundwater flow simulation, mass transport, and sensitivity analysis. The results show that it takes 124 to 300 days for the injected NaOH to arrive at the pumping wells. The sensitivity analysis results show that when the hydraulic conductivity value is ten times greater, the time required is reduced to 84 to 172 days. Meanwhile, when the dynamic dispersivity is twice larger, the time required is reduced to 75 to 189 days. These results indicate that the groundwater remediation method will be effective for aquifers with high hydraulic conductivity and dynamic dispersivity values. For the study area, the groundwater remediation is suggested to be carried out by increasing the number of injection and pumping wells with a relatively close distance, i.e., around 10 meters, so that NaOH arrives at the pumping wells more quickly.Keywords: groundwater, remediation, hydraulic conductivity, dynamic dispersivity, numerical simulation

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

scholarly journals Evaluating Life Cycle of Buildings Using an Integrated Approach Based on Quantitative-Qualitative and Simplified Best-Worst Methods (QQM-SBWM)

2021 ◽  
Vol 13 (8) ◽  
pp. 4487
Author(s):  
Maghsoud Amiri ◽  
Mohammad Hashemi-Tabatabaei ◽  
Mohammad Ghahremanloo ◽  
Mehdi Keshavarz-Ghorabaee ◽  
Edmundas Kazimieras Zavadskas ◽  
...  
Keyword(s):  
Life Cycle ◽  
Computer Software ◽  
Integrated Approach ◽  
Comfort Level ◽  
Combination Method ◽  
New Combination ◽  
Numerical Examples ◽  
Environmental Consequences ◽  
Time Required

Evaluating the life cycle of buildings is a valuable tool for assessing sustainability and analyzing environmental consequences throughout the construction operations of buildings. In this study, in order to determine the importance of building life cycle evaluation indicators, a new combination method was used based on a quantitative-qualitative method (QQM) and a simplified best-worst method (SBWM). The SBWM method was used because it simplifies BWM calculations and does not require solving complex mathematical models. Reducing the time required to perform calculations and eliminating the need for complicated computer software are among the advantages of the proposed method. The QQM method has also been used due to its ability to evaluate quantitative and qualitative criteria simultaneously. The feasibility and applicability of the SBWM were examined using three numerical examples and a case study, and the results were evaluated. The results of the case study showed that the criteria of the estimated cost, comfort level, and basic floor area were, in order, the most important criteria among the others. The results of the numerical examples and the case study showed that the proposed method had a lower total deviation (TD) compared to the basic BWM. Sensitivity analysis results also confirmed that the proposed approach has a high degree of robustness for ranking and weighting criteria.

scholarly journals Reservoir Sediment Management Using Artificial Neural Networks: A Case Study of the Lower Section of the Alpine Saalach River

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 818
Author(s):  
Markus Reisenbüchler ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Real World ◽  
Close Correlation ◽  
Critical Issue ◽  
Management Tool ◽  
Lower Section ◽  
Reservoir Sediment ◽  
Efficiency And Effectiveness ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Reservoir sedimentation is a critical issue worldwide, resulting in reduced storage volumes and, thus, reservoir efficiency. Moreover, sedimentation can also increase the flood risk at related facilities. In some cases, drawdown flushing of the reservoir is an appropriate management tool. However, there are various options as to how and when to perform such flushing, which should be optimized in order to maximize its efficiency and effectiveness. This paper proposes an innovative concept, based on an artificial neural network (ANN), to predict the volume of sediment flushed from the reservoir given distinct input parameters. The results obtained from a real-world study area indicate that there is a close correlation between the inputs—including peak discharge and duration of flushing—and the output (i.e., the volume of sediment). The developed ANN can readily be applied at the real-world study site, as a decision-support system for hydropower operators.

scholarly journals A Model for Developing Existing Ports Considering Economic Impact and Network Connectivity

2021 ◽  
Vol 13 (7) ◽  
pp. 3705
Author(s):  
Veterina Nosadila Riaventin ◽  
Sofyan Dwi Cahyo ◽  
Ivan Kristianto Singgih
Keyword(s):  
Network Connectivity ◽  
Economic Consequences ◽  
Real Problem ◽  
Container Transportation ◽  
Container Ports ◽  
Investment Priority ◽  
Time Required ◽  
Transportation Flows ◽  
Port Clusters

This study discusses the problem of determining which container port should be developed within an existing network and when this should be carried out. A case study of Indonesia’s port network is presented, where several new ports are to be improved to ensure smooth interisland transportation flows of goods. The effects of the investment on economic consequences and increased network connectivity are assessed. When improving the ports, we consider that the available budget limits the investment. The network connectivity is evaluated by considering the number of reachable ports from the developed ports or transportation time required from other ports within the same port cluster. Based on our knowledge, our study is the first one that discusses the investment problem in multiple container ports under single management, as well as its effects regarding the increase in container flows. The problem is introduced and three mathematical models are proposed and used to solve a real problem. The results show that different models have different improved aspects of container transportation flows—e.g., a balanced improvement of the whole port network (Model 2) and appropriate investment priority for port clusters (Model 3).

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