scholarly journals Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet

2021 ◽  
Author(s):  
Aliakbar Narimani ◽  
Moghimi ◽  
Amir Mosavi
Keyword(s):  
Artificial Intelligence ◽  
Economic Value ◽  
Air Entrainment ◽  
Operating Conditions ◽  
Successful Operation ◽  
Volume Rate ◽  
Hydraulic Equipment ◽  
Bottom Outlets ◽  
Hybrid Artificial Intelligence ◽  
Bottom Outlet

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

scholarly journals Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet

2021 ◽  
Author(s):  
Aliakbar Narimani ◽  
Mahdi Moghimi ◽  
Amir Mosavi
Keyword(s):  
Artificial Intelligence ◽  
Economic Value ◽  
Air Entrainment ◽  
Operating Conditions ◽  
Successful Operation ◽  
Volume Rate ◽  
Hydraulic Equipment ◽  
Bottom Outlets ◽  
Hybrid Artificial Intelligence ◽  
Bottom Outlet

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

scholarly journals A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

2021 ◽  
Author(s):  
Karim Sherif Mostafa Hassan Ibrahim ◽  
Yuk Feng Huang ◽  
Ali Najah Ahmed ◽  
Chai Hoon Koo ◽  
Ahmed El-Shafie
Keyword(s):  
Artificial Intelligence ◽  
Streamflow Forecasting ◽  
Hybrid Artificial Intelligence

Characteristics of air entrainment during dynamic wetting failure along a planar substrate

2014 ◽  
Vol 747 ◽  
pp. 119-140 ◽  
Author(s):  
E. Vandre ◽  
M. S. Carvalho ◽  
S. Kumar
Keyword(s):  
High Speed ◽  
Water Solution ◽  
Air Entrainment ◽  
Dynamic Contact ◽  
Operating Conditions ◽  
Dynamic Wetting ◽  
Glycerol Water ◽  
Planar Substrate ◽  
Wide Range ◽  
Stationary Plate

AbstractCharacteristic substrate speeds and meniscus shapes associated with the onset of air entrainment are studied during dynamic wetting failure along a planar substrate. Using high-speed video, the behaviour of the dynamic contact line (DCL) is recorded as a tape substrate is drawn through a bath of a glycerol/water solution. Air entrainment is identified by triangular air films that elongate from the DCL above some critical substrate speed. Meniscus confinement within a narrow gap between the substrate and a stationary plate is shown to delay air entrainment to higher speeds for a wide range of liquid viscosities, expanding upon the findings of Vandre, Carvalho & Kumar (J. Fluid Mech., vol. 707, 2012, pp. 496–520). A pressurized liquid reservoir controls the meniscus position within the confinement gap. It is found that liquid pressurization further postpones air entrainment when the meniscus is located near a sharp corner along the stationary plate. Meniscus shapes recorded near the DCL demonstrate that operating conditions influence the size of entrained air films, with smaller films appearing in the more viscous solutions. Regardless of size, air films become unstable to thickness perturbations and ultimately rupture, leading to the entrainment of air bubbles. Recorded critical speeds and air-film sizes compare well to predictions from a hydrodynamic model for dynamic wetting failure, suggesting that strong air stresses near the DCL trigger the onset of air entrainment.

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

A Comprehensive Review of Recent Advances in the Estimation of Natural Gas Compressibility Factor

2021 ◽  
Author(s):  
Oluwasegun Cornelious Omobolanle ◽  
Oluwatoyin Olakunle Akinsete
Keyword(s):  
Artificial Intelligence ◽  
Equation Of State ◽  
Compressibility Factor ◽  
Operating Conditions ◽  
Measurement Equation ◽  
Comprehensive Review ◽  
Holistic Understanding ◽  
Deviation Factor ◽  
Gas Compressibility ◽  
Limited Accuracy

Abstract Accurate prediction of gas compressibility factor is essential for the evaluation of gas reserves, custody transfer and design of surface equipment. Gas compressibility factor (Z) also known as gas deviation factor can be evaluated by experimental measurement, equation of state and empirical correlation. However, these methods have been known to be expensive, complex and of limited accuracy owing to the varying operating conditions and the presence of non-hydrocarbon components in the gas stream. Recently, newer correlations with extensive application over wider range of operating conditions and crude mixtures have been developed. Also, artificial intelligence is now being deployed in the evaluation of gas compressibility factor. There is therefore a need for a holistic understanding of gas compressibility factor vis-a-vis the cause-effect relations of deviation. This paper presents a critical review of current understanding and recent efforts in the estimation of gas deviation factor.

Artificial intelligence technique for estimating PV modules performance ratio under outdoor operating conditions

2018 ◽  
Vol 10 (5) ◽  
pp. 053505 ◽  
Author(s):  
Alain K. Tossa ◽  
Y. M. Soro ◽  
Y. Coulibaly ◽  
Y. Azoumah ◽  
Anne Migan-Dubois ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Operating Conditions ◽  
Performance Ratio ◽  
Artificial Intelligence Technique ◽  
Pv Modules ◽  
Intelligence Technique

Avoiding Sedimentation and Air Entrainment in Pump Sump for Wet Pit Pumping Stations

2015 ◽  
Author(s):  
Raja Abou Ackl ◽  
Andreas Swienty ◽  
Flemming Lykholt-Ustrup ◽  
Paul Uwe Thamsen
Keyword(s):  
Physical Model ◽  
Air Entrainment ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Design Criteria ◽  
Flow Conditions ◽  
Pumping Station ◽  
Pumping Stations ◽  
Pump Sump ◽  
Pump Stations

In many places lifting systems represent central components of wastewater systems. Pumping stations with a circular wet-pit design are characterized by their relatively small footprint for a given sump volume as well as their relatively simple construction technique [1]. This kind of pumping stations is equipped with submersible pumps. These are located in this case directly in the wastewater collection pit. The waste water passes through the pump station untreated and loaded with all kind of solids. Thus, the role of the pump sump is to provide an optimal operating environment for the pumps in addition to the transportation of sewage solids. Understanding the effects of design criteria on pumping station performance is important to fulfil the wastewater transportation as maintenance-free and energy efficient as possible. The design of the pit may affect the overall performance of the station in terms of poor flow conditions inside the pit, non-uniform und disturbed inflow at the pump inlet, as well as air entrainment to the pump. The scope of this paper is to evaluate the impact of various design criteria and the operating conditions on the performance of pump stations concerning the air entrainment to the pump as well as the sedimentation inside the pit. This is done to provide documentation and recommendations of the design and operating of the station. The investigated criteria are: the inflow direction, and the operating submergence. In this context experiments were conducted on a physical model of duplex circular wet pit wastewater pumping station. Furthermore the same experiments were reproduced by numerical simulations. The physical model made of acrylic allowed to visualize the flow patterns inside the sump at various operating conditions. This model is equipped with five different inflow directions, two of them are tangential to the pit and the remaining three are radial in various positions relative to the pumps centerline. Particles were used to enable the investigation of the flow patterns inside the pit to determine the zones of high sedimentation risk. The air entrainment was evaluated on the model test rig by measuring the depth, the width and the length of the aerated region caused by the plunging water jet and by observing the air bubbles entering the pumps. The starting sump geometry called baseline geometry is simply a flat floor. The tests were done at all the possible combinations of inflow directions, submergence, working pump and operating flow. The ability of the numerical simulation to give a reliable prediction of air entrainment was assessed to be used in the future as a tool in scale series to define the scale effect as well as to analyze the flow conditions inside the sump and to understand the air entrainment phenomenon. These simulations were conducted using the geometries of the test setup after generating the mesh with tetrahedral elements. The VOF multiphase model was applied to simulate the interaction of the liquid water phase and the gaseous air phase. On the basis of the results constructive suggestions are derived for the design of the pit, as well as the operating conditions of the pumping station. At the end recommendations for the design and operating conditions are provided.

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