Applied Machine Learning for Geometallurgical Throughput Prediction—A Case Study Using Production Data at the Tropicana Gold Mining Complex

With the increased use of digital technologies in the mining industry, the amount of centrally stored production data is continuously growing. However, datasets in mines and processing plants are not fully utilized to build links between extracted materials and metallurgical plant performances. This article shows a case study at the Tropicana Gold mining complex that utilizes penetration rates from blasthole drilling and measurements of the comminution circuit to construct a data-driven, geometallurgical throughput prediction model of the ball mill. Several improvements over a previous publication are shown. First, the recorded power draw, feed particle and product particle size are newly considered. Second, a machine learning model in the form of a neural network is used and compared to a linear model. The article also shows that hardness proportions perform 6.3% better than averages of penetration rates for throughput prediction, underlining the importance of compositional approaches for non-additive geometallurgical variables. When adding ball mill power and product particle size, the prediction error (RMSE) decreases by another 10.6%. This result can only be achieved with the neural network, whereas the linear regression shows improvements of 4.2%. Finally, it is discussed how the throughput prediction model can be integrated into production scheduling.

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Data partition and hidden neuron value formulation combination in neural network prediction model: Case study: Non-tax revenue prediction for Indonesian government unit

2018 International Conference on Information and Communications Technology (ICOIACT) ◽

10.1109/icoiact.2018.8350819 ◽

2018 ◽

Cited By ~ 2

Author(s):

Fadly Anshori Lubis ◽

Albarda

Keyword(s):

Neural Network ◽

Prediction Model ◽

Tax Revenue ◽

Data Partition ◽

Model Case ◽

Indonesian Government ◽

Neural Network Prediction ◽

Network Prediction

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Flood Disaster Prediction Model Based on Artificial Neural Network: A Case Study of Kuala Kangsar, Perak

Improving Flood Management, Prediction and Monitoring - Community, Environment and Disaster Risk Management ◽

10.1108/s2040-726220180000020018 ◽

2018 ◽

pp. 103-112 ◽

Cited By ~ 1

Author(s):

Nurul Syarafina Shahrir ◽

Norulhusna Ahmad ◽

Robiah Ahmad ◽

Rudzidatul Akmam Dziyauddin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Flood Disaster ◽

Model Based ◽

Artificial Neural ◽

Disaster Prediction

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Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Mathematics ◽

10.3390/math8091620 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1620 ◽

Cited By ~ 1

Author(s):

Ganjar Alfian ◽

Muhammad Syafrudin ◽

Norma Latif Fitriyani ◽

Muhammad Anshari ◽

Pavel Stasa ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Risk Factors ◽

Diabetic Retinopathy ◽

Prediction Model ◽

Deep Neural Network ◽

Early Stage ◽

Individual Risk ◽

Proposed Model ◽

Individual Risk Factors

Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

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A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

Journal of Energy Resources Technology ◽

10.1115/1.4045543 ◽

2019 ◽

Vol 142 (5) ◽

Author(s):

Byeongho Yu ◽

Dongsu Kim ◽

Heejin Cho ◽

Pedro Mago

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Nonlinear System ◽

Prediction Model ◽

Thermal Load ◽

Ann Model ◽

Load Prediction ◽

Artificial Neural ◽

Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

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Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/2042/1/012002 ◽

2021 ◽

Vol 2042 (1) ◽

pp. 012002

Author(s):

Roberto Castello ◽

Alina Walch ◽

Raphaël Attias ◽

Riccardo Cadei ◽

Shasha Jiang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Computer Vision ◽

State Of The Art ◽

Solar Panel ◽

Post Processing ◽

Processing Step ◽

Recent Method

Abstract The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

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Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images

Diagnostics ◽

10.3390/diagnostics11111976 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1976

Author(s):

Jingyu Kim ◽

Su Young Jeong ◽

Byung-Chul Kim ◽

Byung-Hyun Byun ◽

Ilhan Lim ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neoadjuvant Chemotherapy ◽

Prediction Model ◽

Convolutional Neural Network ◽

Fdg Pet ◽

Texture Features ◽

Test Accuracy ◽

Tumor Center ◽

18F Fdg

We compared the accuracy of prediction of the response to neoadjuvant chemotherapy (NAC) in osteosarcoma patients between machine learning approaches of whole tumor utilizing fluorine−18fluorodeoxyglucose (18F-FDG) uptake heterogeneity features and a convolutional neural network of the intratumor image region. In 105 patients with osteosarcoma, 18F-FDG positron emission tomography/computed tomography (PET/CT) images were acquired before (baseline PET0) and after NAC (PET1). Patients were divided into responders and non-responders about neoadjuvant chemotherapy. Quantitative 18F-FDG heterogeneity features were calculated using LIFEX version 4.0. Receiver operating characteristic (ROC) curve analysis of 18F-FDG uptake heterogeneity features was used to predict the response to NAC. Machine learning algorithms and 2-dimensional convolutional neural network (2D CNN) deep learning networks were estimated for predicting NAC response with the baseline PET0 images of the 105 patients. ML was performed using the entire tumor image. The accuracy of the 2D CNN prediction model was evaluated using total tumor slices, the center 20 slices, the center 10 slices, and center slice. A total number of 80 patients was used for k-fold validation by five groups with 16 patients. The CNN network test accuracy estimation was performed using 25 patients. The areas under the ROC curves (AUCs) for baseline PET maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and gray level size zone matrix (GLSZM) were 0.532, 0.507, 0.510, and 0.626, respectively. The texture features test accuracy of machine learning by random forest and support vector machine were 0.55 and 0. 54, respectively. The k-fold validation accuracy and validation accuracy were 0.968 ± 0.01 and 0.610 ± 0.04, respectively. The test accuracy of total tumor slices, the center 20 slices, center 10 slices, and center slices were 0.625, 0.616, 0.628, and 0.760, respectively. The prediction model for NAC response with baseline PET0 texture features machine learning estimated a poor outcome, but the 2D CNN network using 18F-FDG baseline PET0 images could predict the treatment response before prior chemotherapy in osteosarcoma. Additionally, using the 2D CNN prediction model using a tumor center slice of 18F-FDG PET images before NAC can help decide whether to perform NAC to treat osteosarcoma patients.

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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Geofluids ◽

10.1155/2020/8892556 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Byeongcheol Kang ◽

Kyungbook Lee

Keyword(s):

Neural Network ◽

Machine Learning ◽

Great Influence ◽

Classification Model ◽

Support Vector ◽

Production Data ◽

Geological Uncertainty ◽

Production History ◽

Test Sets ◽

Geological Scenario

Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model’s TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully.

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A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8020099 ◽

2019 ◽

Vol 8 (2) ◽

pp. 99 ◽

Cited By ~ 17

Author(s):

Mahmoud Delavar ◽

Amin Gholami ◽

Gholam Shiran ◽

Yousef Rashidi ◽

Gholam Nakhaeizadeh ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Air Pollution ◽

Prediction Model ◽

Nearest Neighbors ◽

External Input ◽

Maximum Temperature ◽

Pm10 And Pm2.5 ◽

Air Pollution Prediction ◽

Pollution Prediction

Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents have long been struggling with air pollution damage such as the health issues of its citizens. As far as the study area of this research is concerned, a considerable proportion of Tehran air pollution is attributed to PM10 and PM2.5 pollutants. Therefore, the present study was conducted to determine the prediction models to determine air pollutions based on PM10 and PM2.5 pollution concentrations in Tehran. To predict the air-pollution, the data related to day of week, month of year, topography, meteorology, and pollutant rate of two nearest neighbors as the input parameters and machine learning methods were used. These methods include a regression support vector machine, geographically weighted regression, artificial neural network and auto-regressive nonlinear neural network with an external input as the machine learning method for the air pollution prediction. A prediction model was then proposed to improve the afore-mentioned methods, by which the error percentage has been reduced and improved by 57%, 47%, 47% and 94%, respectively. The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3. Finally, using genetic algorithm, data for day of week, month of year, topography, wind direction, maximum temperature and pollutant rate of the two nearest neighbors were identified as the most effective parameters in the prediction of air pollution.

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