scholarly journals Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments

2020 ◽  
Vol 6 (10) ◽  
pp. 1895-1905
Author(s):  
Abdalrhman Abrahim Milad ◽  
Sayf A. Majeed ◽  
Nur Izzi Md. Yusoff
Keyword(s):  
Neural Network ◽  
Response Surface Methodology ◽  
Prediction Model ◽  
Response Surface ◽  
Flexible Pavements ◽  
Flexible Pavement ◽  
Coefficient Of Determination ◽  
Tropical Region ◽  
Pavement Maintenance ◽  
Treatment Techniques

The use of Artificial Intelligence (AI) for the prediction of flexible pavement maintenance that is caused by distressing on the surface layer is crucial in the effort to increase the service life span of pavements as well as reduce government expenses. This study aimed to predict flexible pavement maintenance in tropical regions by using an Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) for predicting models for pavement maintenance in the tropical region. However, to predict the performance of the treatment techniques for flexible pavements, we used critical criteria to choose our date from different sources to represent the situation of the current pavement. The effect of the distress condition on the flexible pavement surface performance was one of the criteria considered in our study. The data were chosen in this study for 288 sets of treatment techniques for flexible pavements. The input parameters used for the prediction were severity, density, road function, and Average Daily Traffic (ADT). The finding of regression models in (R2) values for the ANN prediction model is 0.93, while the (R2) values are (RSM) prediction model dependent on the full quadratic is 0.85. The results of two methods were compared for their predictive capabilities in terms of the coefficient of determination (𝑅2), the Mean Squared Error (MSE), and the Root Mean Square Error (RMSE), based on the dataset. The results showed that the prediction made utilizing ANN was very relevant to the goal in contrast to that made using the statistical program RSM based on different types of mathematical methods such as full quadratic, pure quadratic, interactions, and linear regression.

scholarly journals Prediction of standard aeration efficiency of propeller diffused aeration system using response surface methodology and artificial neural network

2021 ◽  
Author(s):  
Subha M. Roy ◽  
Mohammad Tanveer ◽  
Debaditya Gupta ◽  
C. M. Pareek ◽  
B. C. Mal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Deviation ◽  
Operating Parameters ◽  
Aeration Efficiency ◽  
Artificial Neural

Abstract Aeration experiments were conducted in a masonry tank to study the effects of operating parameters on standard aeration efficiency (SAE) of a propeller diffused aeration (PDA) system. The operating parameters include the rotational speed of shaft (N), submergence depth (h), and propeller angle (α). The response surface methodology (RSM) and artificial neural network (ANN) were used for modelling and optimizing the standard aeration efficiency (SAE) of a PDA system. The results of the both approaches were compared for their modelling abilities in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), computed from experimental and predicted data. ANN models were proved to be superior to RSM. The results indicate that for achieving the maximum standard aeration efficiency (SAE), N, h and α should be 1,000 rpm, 0.50 m, and 12°, respectively. The maximum SAE was found to be 1.711 kg O2/ kWh. The cross-validation results show that the best approximation of optimal values of input parameters for maximizing SAE is possible with a maximum deviation (absolute error) of ±15.2% between the model predicted and experimental values.

scholarly journals Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

2021 ◽  
Author(s):  
Jorge Marcos Rosa ◽  
Flavio Guerhardt ◽  
Silvestre Eduardo Rocha Rocha Ribeiro Júnior ◽  
Peterson Adriano Belan ◽  
Gustavo Araujo Lima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Coefficient Of Determination ◽  
Main Process ◽  
Modeling And Optimization ◽  
Dyeing Process ◽  
Artificial Neural ◽  
Cotton Dyeing

Abstract This work explores the modeling and optimization of the conditions to obtain a set of blue pigments for dyeing reactive cotton, by means of an approach that combines the techniques response surface methodology (RSM) and artificial neural network (ANN). By means of RSM technique the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K.S-1) were investigated. For this, a 26 central composite rotational design (CCRD) was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The results obtained show that all investigated factors have considerable effect on the behavior of K.S-1. The data produced in the dyeing experiments were used to build and train a Multilayer Perceptron ANN (MLP-ANN) to predict K.S-1, being the input layer of the MLP-ANN designed according to the results achieved by the RSM. The non-linear behavior of dyeing with RB5 was successfully modeled by a three-layer MLP-ANN comprising 6 input neurons, 15 hidden neuros, and 1 output neuron to indicate the value of K.S-1. The results achieved in the performed simulations confirmed the ANN effectiveness to predict K.S-1 values in RB5 the dyeing process, with high coefficient of determination (R2=0.942). The developed approach allowed the composition of a table containing optimized conditions to obtain a set of colors of the blue palette using RB5 dye, varying from sky blue to oxford blue, which will facilitate the assembly of the dyes. Finally, the experiments conducted in this work allowed the development of a computational tool to support the dyeing process, saving chemical inputs and time in cotton dyeing with specific dyestuff.

Modeling of Finned-Tube Evaporator Using Neural Network and Response Surface Methodology

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Ze-Yu Li ◽  
Liang-Liang Shao ◽  
Chun-Lu Zhang
Keyword(s):  
Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Cooling Capacity ◽  
Finned Tube ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Pressure Drops ◽  
Dry Conditions ◽  
Polynomial Response Surface

A new response surface methodology (RSM) based neural network (NN) modeling method is proposed for finned-tube evaporator performance evaluation under dry and wet conditions. Two RSM designs, Box–Behnken design (BBD) and central composite design (CCD), are applied to collect a small but well-designed dataset for NN training, respectively. Compared with additional 7000 sets of test data, for all the evaporator performance including total cooling capacity, sensible heat ratio and pressure drops on both refrigerant and air sides, the standard deviation (SD) and coefficient of determination of trained NNs are less than 2% and higher than 0.998, respectively, under dry conditions while those are less than 4% and greater than 0.974, respectively, under wet conditions. Classic quadratic polynomial response surface models were also included for reference. By comparison, the proposed model achieves higher accuracy. Finally, parametric study based on the trained NNs is conducted. This new method can remarkably downsize the training dataset and mitigate the over-fitting risk of NN.

scholarly journals Removal of Pharmaceutical Micropollutants with Integrated Biochar and Marine Microalgae

2020 ◽  
Vol 9 (1) ◽  
pp. 4
Author(s):  
Amin Mojiri ◽  
Maedeh Baharlooeian ◽  
Reza Andasht Kazeroon ◽  
Hossein Farraji ◽  
Ziyang Lou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Cell Viability ◽  
Response Surface ◽  
Contact Time ◽  
Marine Microalgae ◽  
Chaetoceros Muelleri ◽  
Mean Squared Errors ◽  
High Concentrations

Using microalgae to remove pharmaceuticals and personal care products (PPCPs) micropollutants (MPs) have attracted considerable interest. However, high concentrations of persistent PPCPs can reduce the performance of microalgae in remediating PPCPs. Three persistent PPCPs, namely, carbamazepine (CBZ), sulfamethazine (SMT) and tramadol (TRA), were treated with a combination of Chaetoceros muelleri and biochar in a photobioreactor during this study. Two reactors were run. The first reactor comprised Chaetoceros muelleri, as the control, and the second reactor comprised Chaetoceros muelleri and biochar. The second reactor showed a better performance in removing PPCPs. Through the response surface methodology, 68.9% (0.330 mg L−1) of CBZ, 64.8% (0.311 mg L−1) of SMT and 69.3% (0.332 mg L−1) of TRA were removed at the initial concentrations of MPs (0.48 mg L−1) and contact time of 8.1 days. An artificial neural network was used in optimising elimination efficiency for each MP. The rational mean squared errors and high R2 values showed that the removal of PPCPs was optimised. Moreover, the effects of PPCPs concentration (0–100 mg L−1) on Chaetoceros muelleri were studied. Low PPCP concentrations (<40 mg L−1) increased the amounts of chlorophyll and proteins in the microalgae. However, cell viability, chlorophyll and protein contents dramatically decreased with increasing PPCPs concentrations (>40 mg L−1).

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