scholarly journals Optimization of Ash Yield from Bicomposite Biomass (Terminalia catappa and Chrysophyllum albidium) Seed Barks with Additive upon Combustion

2021 ◽  
pp. 26-38
Author(s):  
Ebenezer Olujimi, Dada ◽  
Uriel Olamilekan, Awe-Obe ◽  
Kamoru Olufemi, Oladosu ◽  
Abass Olanrewaju, Alade ◽  
Tinuade Jolaade, Afolabi
Keyword(s):  
Optimal Design ◽  
Principal Component ◽  
Volatile Matter ◽  
Proximate Analysis ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Dihydrogen Phosphate ◽  
Ammonium Dihydrogen Phosphate ◽  
Ann Model ◽  
Artificial Neural Network Ann

The ash yield from the combustion of a mixture of Africa star apple and tropical almond seeds shells (biocomposite biomass) with ammonium dihydrogen phosphate as an additive in a furnace was optimized using I-Optimal Design under the Combined Methodology of the Design Expert Software. The data obtained were analysed statistically using Analysis of Variance (ANOVA), Artificial Neural Network (ANN) for the prediction of ash yield and Principal Component Analysis (PCA) to determine the coefficient of determination (R²) between variables. Proximate analysis was used to evaluate Moisture Content (MC), Fixed Carbon Content (FCC), and Volatile Matter (VM) values while the Higher Heating Value (HHV) of the mixtures that gave the highest and lowest ash yields was evaluated numerically. The optimum conditions of process variables for the compositions of tropical almond, African star apple, and ammonium dihydrogen phosphate, as well as the temperature, were 30%, 60%, 10% and 704 oC, respectively leading to a minimum ash yield of 24.8%. The mathematical models for the ash using the I-optimal design indicate a good fit to the Quadratic model with a R² of 0.9999. The ANN model agreed significantly with the experimental results with an R² of 0.9939.  The VM, FCC, MC, AC and HHV of the highest ash yield were 11.00%, 2.34%, 3.20%, 33.80% and 4487.747 , respectively. The study established the suitability of optimisation tool to develop solid fuel mixtures for possible use in grate furnaces and its efficiencies.

Decolorization of Reactive Black B from wastewater by electro-coagulation: optimization using multivariate RSM and ANN

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Kajal Gautam ◽  
Rishi K. Verma ◽  
Suantak Kamsonlian ◽  
Sushil Kumar
Keyword(s):  
Current Density ◽  
Operating Cost ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Process Time ◽  
Mathematical Tool ◽  
Electrode Gap ◽  
Artificial Neural Network Ann ◽  
Electro Coagulation ◽  
Optimized Conditions

AbstractThe present study is aimed to model and optimize the electrocoagulation (EC) process with five important parameters for the decolorization of Reactive Black B (RBB) from simulated wastewater. A multivariate approach, response surface methodology (RSM) together with central composite design (CCD) is used to optimize process parameters such as pH (5–9), electrode gap (0.5–2.5 cm), current density (2.08–10.41 mA/cm2), process time (10–30 min), and initial dye concentration (100–500 mg/l). The predicted percentage decolorization of dye is obtained as 97.21% at optimized conditions: pH (6.8), gapping (1.3 cm), current density (8.32 mA/cm2), time (23 min), and initial dye concentration (200 mg/L), which is very close to experimental percent decolorization (98.41%). The statistical analysis of variance (ANOVA) is performed to evaluate the quadratic model (RSM), and shows good fit of experimental data with coefficient of determination R2 >0.93. An Artificial Neural Network (ANN) is also used to predict the percentage decolorization and gives overall 94.96% which shows performance accuracy between the predicted and actual value of decolorization. The additional considerations of operating cost and current efficiency are also taken care to show the efficacy of EC process with mathematical tool. The sludge characteristics are determined by FE-SEM/EDX.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

scholarly journals Development of a methodology to backcalculate pavement layer moduli using the traffic speed deflectometer

2018 ◽  
Vol 45 (5) ◽  
pp. 377-385 ◽  
Author(s):  
Omar Elbagalati ◽  
Momen Mousa ◽  
Mostafa A. Elseifi ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Traffic Control ◽  
Coefficient Of Determination ◽  
Falling Weight Deflectometer ◽  
Ann Model ◽  
Acceptable Accuracy ◽  
Traffic Speed ◽  
Artificial Neural Network Ann ◽  
Falling Weight ◽  
Good Agreement

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) measurements; however, the stationary nature of FWD requires lane closure and traffic control. To overcome these limitations, a number of continuous deflection devices were introduced in recent years. The objective of this study was to develop a methodology to incorporate traffic speed deflectometer (TSD) measurements in the backcalculation analysis. To achieve this objective, TSD and FWD measurements were used to train and to validate an artificial neural network (ANN) model that would convert TSD deflection measurements to FWD deflection measurements. The ANN model showed acceptable accuracy with a coefficient of determination of 0.81 and a good agreement between the backcalculated moduli from FWD and TSD measurements. Evaluation of the model showed that the backcalculated layer moduli from TSD could be used in pavement analysis and in structural health monitoring with a reasonable level of accuracy.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

scholarly journals Rock Fragmentation Prediction through a New Hybrid Model Based on Imperial Competitive Algorithm and Neural Network

2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Danial Jahed Armaghani
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Rock Fragmentation ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Competitive Algorithm ◽  
Model Coefficient ◽  
Parametric Studies ◽  
Artificial Neural Network Ann ◽  
Fragmentation Prediction

Reliable estimation of rock fragmentation is an important issue in the blasting operations in order to predict quality of the production. Since rock fragmentation is affected by various parameters such as blast pattern and rock mass characteristics, it is very difficult to have an appreciate prediction of it. This paper describes a new hybrid imperialism competitive algorithm (ICA)-artificial neural network (ANN) in order to solve shortcomings of ANN itself for prediction of rock fragmentation. In fact, the influence of ICA on ANN results was studied in this research. By investigating the related studies, the most important parameters of ICA were identified and a series of parametric studies for their determination were conducted. All models were built using 8 inputs and one output which is rock fragmentation. To have a fair comparison and show the capability of the new hybrid model, a pre-developed ANN model was also considered and constructed. Evaluation of the obtained results demonstrated that a higher ability of rock fragmentation prediction is received developing a hybrid ICA-ANN model. Coefficient of determination (R2) values of (0.949 and 0.813) and (0.941 and 0.819) were obtained for training and testing of ICA-ANN and ANN models, respectively which indicated that the proposed ICA-ANN model can be implemented better in improving performance capacity of ANN model in estimating rock fragmentation.

scholarly journals The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Şükrü Özşahin ◽  
Hilal Singer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Densified Wood ◽  
Percentage Error ◽  
Angle Of Incidence ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

2012 ◽  
Vol 170-173 ◽  
pp. 1013-1016
Author(s):  
Fu Qiang Gao ◽  
Xiao Qiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Monitoring Point ◽  
Blasting Operation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of peak particle velocity (PPV) is very complicated due to the number of influencing parameters affecting seism wave propagation. In this paper, artificial neural network (ANN) is implemented to develop a model to predict PPV in a blasting operation. Based on the measured parameters of maximum explosive charge used per delay and distance between blast face to monitoring point, a three-layer ANN was found to be optimum with architecture 2-5-1. Through the analysis of coefficient of determination (CoD) and mean absolute error (MAE) between monitored and predicted values of PPV, it indicates that the forecast data by the ANN model is close to the actua1 values.

scholarly journals Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

2021 ◽  
pp. 152808372098497
Author(s):  
Sumit Mandal ◽  
Guowen Song ◽  
Rene M Rossi ◽  
Indu B Grover
Keyword(s):  
Thermal Energy ◽  
Health And Safety ◽  
Empirical Modeling ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Artificial Neural Network Ann ◽  
Protective Fabrics ◽  
Time Required ◽  
Protective Performance ◽  
Best Fit

This study aims to characterize and model the thermal protective fabrics usually used in workwear under Molotov cocktail exposure. Physical properties of the fabrics were measured; and, thermal protective performances of the fabrics were evaluated under a fire exposure generated from the laboratory-simulated Molotov cocktail. The performance was calculated in terms of the amount of thermal energy transmitted through the fabrics; additionally, the time required to generate a second-degree burn on wearers’ bodies was predicted from the calculated transmitted thermal energy. For the characterization, the parameters that affected the protective performance were identified and discussed with regards to the theory of heat and mass transfer. The relationships between the properties of the fabric systems and the protective performances were statistically analyzed. The significant fabric properties affecting the performance were further employed in the empirical modeling techniques − Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for predicting the protective performance. The Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of the developed MLR and ANN models were also compared to identify the best-fit model for predicting the protective performance. This study found that thermal resistance and evaporative resistance are two significant properties (P-Values < 0.05) that negatively affect the transmitted thermal energy through the fabric systems. Also, R2 and RMSE values of ANN model were much higher (R2 = 0.94) and lower (RMSE = 37.42), respectively, than MLR model (R2 = 0.73; RMSE = 191.38); therefore, ANN is the best-fit model to predict the protective performance. In summary, this study could build an in-depth understanding of the parameters that can affect the protective performance of fabrics used in the workwear of high-risk sectors employees and would provide them better occupational health and safety.

scholarly journals An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation – Ahdeb Oil Field

2020 ◽  
pp. 1051-1062
Author(s):  
Zaher JabbarAttwan AL Zirej ◽  
Hassan Abdul Hadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Field ◽  
Coefficient Of Determination ◽  
Drilling Mud ◽  
Ann Model ◽  
Rate Of Penetration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The main objective of this study is to develop a rate of penetration (ROP) model for Khasib formation in Ahdab oil field and determine the drilling parameters controlling the prediction of ROP values by using artificial neural network (ANN).      An Interactive Petrophysical software was used to convert the raw dataset of transit time (LAS Readings) from parts of meter-to-meter reading with depth. The IBM SPSS statistics software version 22 was used to create an interconnection between the drilling variables and the rate of penetration, detection of outliers of input parameters, and regression modeling. While a JMP Version 11 software from SAS Institute Inc. was used for artificial neural modeling.      The proposed artificial neural network method depends on obtaining the input data from drilling mud logging data and wireline logging data. The data then analyzes it to create an interconnection between the drilling variables and the rate of penetration.      The proposed ANN model consists of an input layer, hidden layer and outputs layer, while it applies the tangent function (TanH) as a learning and training algorithm in the hidden layer. Finally, the predicted values of ROP are compared with the measured values. The proposed ANN model is more efficient than the multiple regression analysis in predicting ROP. The obtained coefficient of determination (R2) values using the ANN technique are 0.93 and 0.91 for training and validation sets, respectively. This study presents a new model for predicting ROP values in comparison with other conventional drilling measurements.

Sign in / Sign up

Export Citation Format

Share Document