Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

2010 ◽  
Vol 17 (5) ◽  
pp. 526-534 ◽  
Author(s):  
Saeed Chehreh Chelgani ◽  
Behzad Shahbazi ◽  
Bahram Rezai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Froth Flotation ◽  
Collision Probability ◽  
Operational Parameters ◽  
Flotation Recovery ◽  
Artificial Neural

Forecasting of Cutting Forces in Dental Adjustment of Ceramic Prostheses Using an Artificial Neural Network

2010 ◽  
Vol 152-153 ◽  
pp. 1687-1690
Author(s):  
Jian Hui Peng ◽  
Xiao Fei Song ◽  
Ling Yin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Process ◽  
Computer Assisted ◽  
Ann Model ◽  
Operational Parameters ◽  
Artificial Neural

Intraoral adjustment of ceramic prostheses involving cutting process is a central procedure in restorative dentistry because the quality of ceramic prostheses depends on the cutting process. In this paper, an artificial neural network (ANN) model was developed for the first time to forecast the dynamic forces in dental cutting process as functions of clinical operational parameters. The predicted force values were compared with the measured values in in vitro dental cutting of porcelain prostheses obtained using a novel two-degrees-of-freedom computer-assisted testing apparatus with a high-speed dental handpiece and diamond burs. The results indicate that there existed nonlinear relationships between the cutting forces and clinical operational parameters. It is found that the ANN-forecasted forces were in good agreement with the experiment-measured values. This indicates that the established ANN model can provide insights into the force-related process assessment and forecast for clinical dental cutting of ceramic prostheses.

Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

2009 ◽  
Vol 22 (11) ◽  
pp. 970-976 ◽  
Author(s):  
E. Jorjani ◽  
H. Asadollahi Poorali ◽  
A. Sam ◽  
S. Chehreh Chelgani ◽  
Sh. Mesroghli ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Froth Flotation ◽  
Coal Analysis ◽  
Artificial Neural

scholarly journals Adsorptive removal of dairy industrial pollutants by chitosan zinc –oxide nanoadsorbent- A comparitive study of artificial neural network and quadratic Box-Behnken design

2021 ◽  
Vol 42 (6) ◽  
pp. 1442-1451
Author(s):  
B.L. Dinesha ◽  
◽  
S. Hiregoudar ◽  
U. Nidoni ◽  
K.T. Ramappa ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Zinc Oxide ◽  
High Speed ◽  
Oxygen Demand ◽  
Effluent Treatment ◽  
Precipitation Method ◽  
Ann Model ◽  
Operational Parameters ◽  
Artificial Neural

Aim: To investigate the effect of operational parameters on the adsorption of biological oxygen demand (BOD) and chemical oxygen demand (COD) on to Chitosan zinc oxide (CZnO) nanoadsorbent using cost-effective and eco-friendly nanoadsorbent based effluent treatment processes. Methodology: CZnO nanoadsorbent particle was synthesized using chemical precipitation method. The nano size <100 nm was achieved using high-speed cryo all mill, followed by the characterization using high-end instruments such as scanning electron microscope with elemental detection sensor (SEM-EDS), atomic force microscope (AFM), X-ray diffractometer (XRD) and Fourier transform inform infrared spectroscopy (FT-IR). Modeling and optimization of operational parameters were done with the artificial neural network (ANN) and Box-BehnkenDesign (BBD) statistical tools. Results: Optimized treatment combination for adsorption of BOD and COD were found at initial BOD and COD concentration of 100 and 200 mg l−1, pH of 7.0 and 2.0, adsorbent dosage of 1.25 mg l−1, contact time of 100 and 60 min. In these conditionsthe desirability values of 0.988 and 0.950 were found for BOD and COD adsorption. The maximum per cent reduction of BOD and COD by using CZnO nanoadsorbent was found to be 96.71 and 87.56. Two models such as Quadratic Box-Behnken and ANN were compared in term of sum of square errors (SSE), root mean square error (RMSE) and correlation coefficient (R2) values. Interpretation: The results obtained revel the well trained ANN model found to be more accurate in prediction of BOD and COD adsorption process parameters compared to BBD model.

scholarly journals Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

2018 ◽  
Vol 8 (4) ◽  
pp. 2486
Author(s):  
Nong Nurnie Mohd Nistah ◽  
King Hann Lim ◽  
Lenin Gopal ◽  
Firas Basim Ismail Alnaimi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Critical Issue ◽  
Fault Prediction ◽  
Misclassification Rate ◽  
Correct Prediction ◽  
Operational Parameters ◽  
Artificial Neural

<span>Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.</span>

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