Artificial Neural Network for Prediction of Full-Scale Seepage Flow Rate at the Equity Silver Mine

2020 ◽  
Vol 231 (4) ◽  
Author(s):  
Liang Ma ◽  
Cheng Huang ◽  
Zhong-Sheng Liu ◽  
Kevin A. Morin ◽  
Mike Aziz ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Seepage Flow ◽  
Full Scale ◽  
Silver Mine ◽  
Artificial Neural

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

2013 ◽  
Vol 13 (2) ◽  
pp. 1085-1098 ◽  
Author(s):  
Mohammad Ali Ahmadi ◽  
Mohammad Ebadi ◽  
Amin Shokrollahi ◽  
Seyed Mohammad Javad Majidi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm ◽  
Oil Flow ◽  
Artificial Neural

scholarly journals Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 578
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Stream Flow ◽  
Low Flow ◽  
Urban Stream ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Artificial Neural

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

A high-efficient batch-recirculated photoreactor packed with immobilized TiO2-P25 nanoparticles onto glass beads for photocatalytic degradation of phenazopyridine as a pharmaceutical contaminant: artificial neural network modeling

2016 ◽  
Vol 73 (11) ◽  
pp. 2804-2814 ◽  
Author(s):  
Mahdie Shargh ◽  
Mohammad A. Behnajady
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Light Source ◽  
Flow Rate ◽  
Irradiation Time ◽  
Volumetric Flow Rate ◽  
Glass Beads ◽  
Initial Concentration ◽  
Immobilized Tio2 ◽  
Artificial Neural

In this study, removal efficiency of phenazopyridine (PhP) as a model pharmaceutical contaminant was investigated in a batch-recirculated photoreactor packed with immobilized TiO2-P25 nanoparticles on glass beads. Influence of various operational parameters such as irradiation time, initial concentration of PhP, volume of solution, volumetric flow rate, pH and power of light source was investigated. Results indicated that removal percentage increases with the rise of irradiation time, volumetric flow rate and power of light source but decreases with the rise of initial concentration of PhP and volume of solution. Highest removal percentage was obtained in the natural pH of PhP solution (pH = 5.9). Results of mineralization studies also showed a decreasing trend of total organic carbon (TOC) and producing mineralization products such as NO3−, NO2− and NH4+. Modeling of the process using artificial neural network showed that the most effective parameters in the degradation of PhP were volume of solution and power of light source. The packed bed photoreactor with TiO2-P25 nanoparticles coated onto glass beads in consecutive repeats have the proper ability for PhP degradation. Therefore, this system can be a promising alternative for the removal of recalcitrant organic pollutants such as PhP from aqueous solutions.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

scholarly journals Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

2021 ◽  
Vol 13 (21) ◽  
pp. 11654
Author(s):  
Roozbeh Vaziri ◽  
Akeem Adeyemi Oladipo ◽  
Mohsen Sharifpur ◽  
Rani Taher ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Thermal Efficiency ◽  
Exergy Efficiency ◽  
Wire Mesh ◽  
Double Pass ◽  
Artificial Neural

Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and experimentally enhanced under different operating conditions. Forty-eight iron pieces of wool and fifteen steel wire mesh layers were located between the external plexiglass and internal glass, which is utilized as an absorber plate. The experimental outcomes show that the thermal efficiency enhances as the air mass flow rate increases for the range of 0.014–0.033 kg/s. The highest thermal efficiency gained by utilizing the hybrid optimized DPGSAHM and DPGSAHI was 94 and 97%, respectively. The exergy efficiency and temperature difference (∆T) indicated an inverse relationship with mass flow rate. When the DPGSAHM and DPGSAHI were optimized by the hybrid procedure and employing the Taguchi-artificial neural network, enhancements in the thermal efficiency by 1.25% and in exergy efficiency by 2.4% were delivered. The results show the average cost per kW (USD 0.028) of useful heat gained by the DPGSAHM and DPGSAHI to be relatively higher than some double-pass SAHs reported in the literature.

scholarly journals Investigation of the Efficiency of Small-Scale NF/RO Seawater Desalination by Using Artificial Neural Network Modeling

2021 ◽  
Vol 16 (3) ◽  
pp. 239-299
Author(s):  
Asma Adda ◽  
Salah Bezari ◽  
Mohamed Salmi ◽  
Giulio Lorenzini ◽  
Maamar Laidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Coefficient Of Determination ◽  
Neural Network Modeling ◽  
Small Scale ◽  
Water Recovery ◽  
Seawater Desalination ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

An attempt is conducted in this paper to develop an artificial neural network (ANN) model for predicting the efficiency of small-scale NF/RO seawater desalination, then applied to the simulation of permeate flow rate and water recovery. A feed-forward back-propagation neural network with the Levenberg-Marquardt learning algorithm is considered. The performance of ANN compared to the multiple linear regression (MLR) is based on the calculated value of the coefficient of determination (R2). For ANN, R2 permeate flow rate was 0.997, and R2 permeate water recovery was 0.999, and for MLR, R2 permeate flow rate was 0.508, and R2 permeate water recovery was 0.713. It was observed that ANN performed better than the MLR.

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