Use of an Artificial Neural Network to Evaluate the Oleo-Flotation Process to Treat Coal Fines

Charan, T. Gouri; Kalyani, V. K.; Sharma, Kunal; Kumar, Lalan; Kumari, Sweta; Sinha, A.

doi:10.1080/19392699.2013.869585

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Thus, if the number of neurons is small, the model does not accurately reflect the nonlinear mapping between inputs and outputs. On the other hand, if the number of middle layer neurons is too large, the model becomes overtrained and loses its generalizability [79,80]. To choose the best structure, the number of neurons in the hidden layer was changed to achieve minimum MSE.…”

Section: Sulfur Removal Estimation Based On Mlrmentioning

confidence: 99%

Prediction of Sulfur Removal from Iron Concentrate Using Column Flotation Froth Features: Comparison of k-Means Clustering, Regression, Backpropagation Neural Network, and Convolutional Neural Network

2022

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Froth feature extraction plays a significant role in the monitoring and control of the flotation process. Image-based soft sensors have received a great deal of interest in the flotation process due to their low-cost and non-intrusive properties. This study proposes data-driven soft sensor models based on froth images to predict the key performance indicators of the flotation process. The ability of multiple linear regression (MLR), the backpropagation neural network (BPNN), the k-means clustering algorithm, and the convolutional neural network (CNN) to predict the amount of sulfur removal from iron ore concentrate in the column flotation process was examined. A total of 99 experimental results were used to develop the predictive models. Extracted froth features including color, bubble shape and size, texture, stability, and velocity were used to train the traditional predictive models, whereas in the CNN model the froth images were directly fed into the model. The results comparison indicated that the three-layered feedforward NN model (17-10-1 topology) and CNN model provided better predictions than the MLR and k-means algorithm. The BPNN model displayed a correlation coefficient of 0.97 and a root mean square error of 4.84% between the actual data and network output for both training and the testing datasets. The error percentages of the CNN, BPNN, MLR and k-means models were 10, 11, 15 and 18%, respectively. This study can become a key technical support for the application of intelligent models in the control of the operational variables for the flotation process used to desulfurize iron concentrate.

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Section: Sulfur Removal Estimation Based On Mlrmentioning

confidence: 99%

Prediction of Sulfur Removal from Iron Concentrate Using Column Flotation Froth Features: Comparison of k-Means Clustering, Regression, Backpropagation Neural Network, and Convolutional Neural Network

2022

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“…Counterfeit Neural Networks are the naturally propelled reenactments performed on the PC to play out certain particular errands like grouping, arrangement, design acknowledgment and so forth [4]. Simulated Neural Networks, in general - is an organically roused system of counterfeit neurons designed to perform particular undertakings.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Weather Forecasting Pridiction using Mamdani Fuzzifier

Zahra

Abbas

2019

Research Journal Of Computer Science And Information Technology

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A climate expectation display is under study in view of the neural system and fuzzy surmising framework, and after that apply it to anticipate every day fuzzy precipitation given meteorological premises for testing. A "fuzzy ranked based neural system", which reenacts successive relations among fuzzy sets utilizing the manufactured neural system. It is outstanding that the requirement for exact climate expectation is clear while thinking about the advantages. Nonetheless, the over the top quest for exactness in climate expectation makes a portion of the "precise" forecast comes about pointless and the numerical forecast show is regularly intricate and tedious.

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“…Fuzzy logic [1][2][3][4] and artificial neural networks (ANNs) [5][6][7][8] are important in the intelligent control of complex systems. A combination of them is widely used in solving classification, pattern recognition problems, and so on.…”

Section: Introductionmentioning

confidence: 99%

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Xue

Zhang

et al. 2014

Atmosphere

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Abstract:We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the "fuzzy rule-based neural network", which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the "neural fuzzy inference system", which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the "accurate" OPEN ACCESSAtmosphere 2014, 5 789 prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

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Use of an Artificial Neural Network to Evaluate the Oleo-Flotation Process to Treat Coal Fines

Cited by 6 publications

References 7 publications

Prediction of Sulfur Removal from Iron Concentrate Using Column Flotation Froth Features: Comparison of k-Means Clustering, Regression, Backpropagation Neural Network, and Convolutional Neural Network

Prediction of Sulfur Removal from Iron Concentrate Using Column Flotation Froth Features: Comparison of k-Means Clustering, Regression, Backpropagation Neural Network, and Convolutional Neural Network

Weather Forecasting Pridiction using Mamdani Fuzzifier

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

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