scholarly journals Modeling of river flow rate as a function of rainfall and temperature using response surface methodology based on historical time series

2016 ◽  
Vol 18 (4) ◽  
pp. 651-665 ◽  
Author(s):  
Srđan Kostić ◽  
Milan Stojković ◽  
Stevan Prohaska ◽  
Nebojša Vasović
Keyword(s):  
Time Series ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
River Flow ◽  
External Validation ◽  
Nonlinear Function ◽  
Rainfall Regime ◽  
Mathematical Form ◽  
Flow Rates

In the present paper we propose a new model of monthly river flow rate as a simple nonlinear function of air temperature and rainfall. Response surface methodology is used to analyze the observed monthly flow rates from 1950 to 1990 for Great Morava River, as the largest domestic river in Serbia. Obtained results indicate significant linear and quadratic effect of both individual factors, while two-factor interactions show significantly smaller influence, indicating occurrence of maximum flow rate for low temperature and high rainfall regime. Statistical reliability of the proposed model is verified by internal and external validation, the latter of which included comparison of predicted and observed values from 1991 to 2012. It is shown that predicted flow rates exhibit a similar statistical pattern as observed ones, with a satisfying value of Nash–Sutcliffe coefficient (NSE = 0.73), although the derived model cannot capture well the highest flow rates. Obtained results further indicate the sequence of residuals represents random time series, which is confirmed by appropriate test statistics and surrogate data testing. The advantage of using the derived model for hydrological simulations in river basins instead of existing ones lies in its explicit mathematical form, making it suitable for quick and reliable estimation and prediction of monthly flow rates.

scholarly journals Flux intensification during microfiltration of distillery stillage using a kenics static mixer

2017 ◽  
pp. 285-293
Author(s):  
Vesna Vasic ◽  
Aleksandar Jokic ◽  
Marina Sciban ◽  
Jelena Prodanovic ◽  
Jelena Dodic ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Specific Energy Consumption ◽  
Point Of View ◽  
Transmembrane Pressure ◽  
Feed Flow Rate ◽  
Static Mixer ◽  
Economic Point ◽  
Mathematical Presentation

The present work studies the effect of operating parameters (pH, feed flow rate, and transmembrane pressure) on microfiltration of distillery stillage. Experiments were conducted in the presence of a Kenics static mixer as a turbulence promoter, and its influence on the flux improvement and specific energy consumption was examined. Response surface methodology was used to investigate the effect of selected factors on microfiltration performances. The results showed that response surface methodology is an appropriate model for mathematical presentation of the process. It was found that the use of a static mixer is justified at the feed flow rates higher than 100 L/h. In contrast, the use of a static mixer at low values of feed flow rate and transmembrane pressure has no justification from an economic point of view.

Optimization of photocatalytic degradation of real textile dye house wastewater by response surface methodology

2016 ◽  
Vol 74 (9) ◽  
pp. 1999-2009 ◽  
Author(s):  
Sayed Mohammad Bagher Hosseini ◽  
Narges Fallah ◽  
Sayed Javid Royaee
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Advanced Oxidation Process ◽  
Influential Factor ◽  
P Value ◽  
Textile Dye ◽  
Optimum Conditions ◽  
Effective Parameters ◽  
Initial Ph

This study evaluates the advanced oxidation process for decolorization of real textile dyeing wastewater containing azo and disperse dye by TiO2 and UV radiation. Among effective parameters on the photocatalytic process, effects of three operational parameters (TiO2 concentration, initial pH and aeration flow rate) were examined with response surface methodology. The F-value (136.75) and p-value <0.0001 imply that the model is significant. The ‘Pred R-Squared’ of 0.95 is in reasonable agreement with the ‘Adj R-Squared’ of 0.98, which confirms the adaptability of this model. From the quadratic models developed for degradation and subsequent analysis of variance (ANOVA) test using Design Expert software, the concentration of catalyst was found to be the most influential factor, while all the other factors were also significant. To achieve maximum dye removal, optimum conditions were found at TiO2 concentration of 3 g L−1, initial pH of 7 and aeration flow rate of 1.50 L min−1. Under the conditions stated, the percentages of dye and chemical oxygen demand removal were 98.50% and 91.50%, respectively. Furthermore, the mineralization test showed that total organic compounds removal was 91.50% during optimum conditions.

An Adaptive Response Surface Methodology Based on Active Subspaces for Mixed Random and Interval Uncertainties

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Xingzhi Hu ◽  
Yanhui Duan ◽  
Ruili Wang ◽  
Xiao Liang ◽  
Jiangtao Chen
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Estimation Error ◽  
Three Dimensional ◽  
Nonlinear Function ◽  
Response Analysis ◽  
Dynamic Correlation ◽  
Interval Uncertainties ◽  
Speed Reducer ◽  
Active Subspaces

Abstract The popular use of response surface methodology (RSM) accelerates the solutions of parameter identification and response analysis issues. However, accurate RSM models subject to aleatory and epistemic uncertainties are still challenging to construct, especially for multidimensional inputs, which is widely existed in real-world problems. In this study, an adaptive interval response surface methodology (AIRSM) based on extended active subspaces is proposed for mixed random and interval uncertainties. Based on the idea of subspace dimension reduction, extended active subspaces are given for mixed uncertainties, and interval active variable representation is derived for the construction of AIRSM. A weighted response surface strategy is introduced and tested for predicting the accurate boundary. Moreover, an interval dynamic correlation index is defined, and significance check and cross validation are reformulated in active subspaces to evaluate the AIRSM. The effectiveness of AIRSM is demonstrated on two test examples: three-dimensional nonlinear function and speed reducer design. They both possess a dominant one-dimensional active subspace with small estimation error, and the accuracy of AIRSM is verified by comparing with full-dimensional Monte Carlo simulates, thus providing a potential template for tackling high-dimensional problems involving mixed aleatory and interval uncertainties.

Study of Gas Shortcut Flow Rate in Cyclone with Different Inlet Section Angles Using Response Surface Methodology

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Fuping Qian ◽  
Xingwei Huang ◽  
Mingyao Zhang
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Numerical Solutions ◽  
Inlet Section ◽  
Statistical Software ◽  
Vortex Finder ◽  
Computational Fluid Dynamics Cfd

Numerical simulations of cyclones with various vortex finder dimensions and inlet section angles were performed to study the gas shortcut flow rate. The numerical solutions were carried out using commercial computational fluid dynamics (CFD) code Fluent 6.1. A prediction model of the gas shortcut flow rate was obtained based on response surface methodology by means of the statistical software program (Minitab V14). The results show that the length of the vortex finder insertion, the vortex finder diameter and the inlet section angle play an important role in influencing the gas shortcut flow rate. The gas shortcut flow rate decreases when increasing the inlet section angle, and increases when increasing the vortex finder diameter and decreasing the length of the vortex finder insertion. Compared with the effect of the length of the vortex finder insertion on the shortcut flow rate, the effect of the vortex finder diameter on the gas shortcut flow rate seems more pronounced. The effect of the vortex finder dimension on the gas shortcut flow rate is changed with the different inlet section angles, i.e., the effects of the vortex finder dimension of the conventional cyclone (the inlet section angle is 0º) on the gas shortcut flow rate is stronger than the cyclone with 30º and 45º inlet section angles.

Investigations on Surface Roughness Measurement in Minimum Quantity Lubrication Turning of Titanium Alloys Using Response Surface Methodology and Box–Cox Transformation

2016 ◽  
Vol 16 (2) ◽  
pp. 75-88 ◽  
Author(s):  
Munish Kumar Gupta ◽  
P. K. Sood ◽  
Vishal S. Sharma
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Feed Rate ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Machining Parameters ◽  
Minimum Quantity ◽  
Box Cox Transformation

AbstractIn the present work, an attempt has been made to establish the accurate surface roughness (Ra, Rq and Rz) prediction model using response surface methodology with Box–Cox transformation in turning of Titanium (Grade-II) under minimum quantity lubrication (MQL) conditions. This surface roughness model has been developed in terms of machining parameters such as cutting speed, feed rate and approach angle. Firstly, some experiments are designed and conducted to determine the optimal MQL parameters of lubricant flow rate, input pressure and compressed air flow rate. After analyzing the MQL parameter, the final experiments are performed with cubic boron nitride (CBN) tool to optimize the machining parameters for surface roughness values i. e., Ra, Rq and Rz using desirability analysis. The outcomes demonstrate that the feed rate is the most influencing factor in the surface roughness values as compared to cutting speed and approach angle. The predicted results are fairly close to experimental values and hence, the developed models using Box-Cox transformation can be used for prediction satisfactorily.

Using Response-Surface Methodology to Detect Chaos in Ecological Time Series

Oikos ◽  
1993 ◽  
Vol 68 (2) ◽  
pp. 329 ◽  
Author(s):  
Joe N. Perry ◽  
Ian P. Woiwod ◽  
Ilkka Hanski
Keyword(s):  
Time Series ◽  
Response Surface Methodology ◽  
Response Surface

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 Optimization of Ammonia Stripping of Piggery Biogas Slurry by Response Surface Methodology

2019 ◽  
Vol 16 (20) ◽  
pp. 3819 ◽  
Author(s):  
Mengyuan Zou ◽  
Hongmin Dong ◽  
Zhiping Zhu ◽  
Yuanhang Zhan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Gas Flow ◽  
Ph Value ◽  
Removal Rate ◽  
Ammonia Nitrogen ◽  
Biogas Slurry ◽  
Gas Flow Rate ◽  
Ammonia Stripping

Ammonia stripping is a pretreatment method for piggery biogas slurry, and the effectiveness of the method is affected by many factors. Based on the results of single-factor experiments, response surface methodology is adopted to establish a quadratic polynomial mathematical model relating stripping time, pH value and gas flow rate to the average removal rate of ammonia nitrogen to explore the interactions among various influencing factors, obtain optimized combined parameters for ammonia stripping, and carry out experimental verification of the parameters. The results show that when hollow polyhedral packing is adopted under operating conditions including a stripping time of 90 min, pH value of 11, gas flow rate of 28 m3/h, gas–liquid ratio of 2000 and temperature of 30 °C, the average removal rate of ammonia nitrogen in biogas slurry can reach approximately 73%. The experimental value is only 4.2% different from the predicted value, which indicates that analysis on the interaction among factors influencing ammonia stripping of biogas slurry and parameter optimization of the regression model are accurate and effective.

APPLICATION OF ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY FOR MODELLING OF HYDROGEN PRODUCTION USING NICKEL LOADED ZEOLITE

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Fazureen Azaman ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Mahadhir Mohamed ◽  
Kamaruzzaman Yunus ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Hydrogen Gas ◽  
Hydrogen Yield ◽  
Glycerol Conversion ◽  
Production Of Hydrogen ◽  
Artificial Neural

Hydrogen gas production via glycerol steam reforming using nickel (Ni) loaded zeolite (HZSM-5) catalyst was focused on this research. 15 wt % Ni(HZSM-5) catalyst loading has been investigated based on the parameter of different range of catalyst weight (0.3-0.5g) and glycerol flow rate (0.2-0.4mL/min) at 600 ºC and atmospheric pressure. The products were analyzed by using gas-chromatography with thermal conductivity detector (GC-TCD), where it used to identify the yield of hydrogen. The data of the experiment were analyzed by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in order to predict the production of hydrogen. The results show that the condition for maximum hydrogen yield was obtained at 0.4 ml/min of glycerol flow rate and 0.3 g of catalyst weight resulting in 88.35 % hydrogen yield. 100 % glycerol conversion was achieved at 0.4 of glycerol flow rates and 0.3 g catalyst weight. After predicting the model using RSM and ANN, both models provided good quality predictions. The ANN showed a clear superiority with R2 was almost to 1 compared to the RSM model.

Investigation on Fluidized Bed Bioreactor Treating Ice Cream Wastewater Using Response Surface Methodology and Artificial Neural Network

2014 ◽  
Vol 12 (1) ◽  
pp. 563-573 ◽  
Author(s):  
K. Thirugnanasambandham ◽  
V. Sivakumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Fluidized Bed ◽  
Flow Rate ◽  
Ice Cream ◽  
Agitation Speed ◽  
Fluidized Bed Bioreactor ◽  
Artificial Neural

Abstract In this study, a comparative approach was developed between response surface methodology (RSM) and artificial neural network (ANN) in the predictive capabilities for the removal of chemical oxygen demand (COD) from ice cream industry wastewater using fluidized bed bioreactor. The effects of process variables such as pH, temperature, flow rate and agitation speed investigated using a four-factor three-level Box–Behnken experimental design (BBD). Same design was utilized to train a feed-forward multilayered perceptron (MLP) ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of statistical parameters including coefficient of determination (R2). The results showed that properly trained ANN model is more accurate in prediction as compared to RSM model. Under the optimum conditions (pH of 7, temperature of 40°C, flow rate of 20 ml/min and agitation speed of 175 rpm), 91% of COD was removed.

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