Data Analytics Applied to the Mining Industry

Digital Transformation of MiningIntroductionDT in the Mining IndustryData SourcesConnectivityInformation of Things (IoT)Data ExchangeSafety of the CybersRemote Operations Centers (ROCs)Platforms IncorporatedWireless CommunicationsOptimization AlgorithmsAdvanced AnalyticsIndividualsProcess of AnalysisTechnology in Advanced AnalyticsDT and the Mining PotentialThe Role of People in Digital Mining Transformation for Future MiningThe Role of Process in Mining Digital Transformation for Future MiningThe Role of Technology in Mining Digital Transformation for Future MiningAcademy Responsibilities in Mining DT ImprovementSummaryReferencesAdvanced Data AnalyticsIntroductionBig DataAnalyticsDeep LearningCNNsDeep Neural NetworkRecurrent Neural Network (RNN)MLFuzzy LogicClassification TechniquesClusteringEvolutionary TechniquesGenetic Algorithms (GAs)Ant Colony Optimization (ACO)Bee Colony Optimization (BCO)Particle Swarm Optimization (PSO)Firefly Algorithm (FA)Tabu Search Algorithm (TS)BDA and IoTSummaryReferencesData Collection, Storage, and RetrievalCritical Performance ParametersData QualityData Quality AssessmentData Quality StrategiesDealing with Missing DataDealing with Duplicated DataDealing with Data HeterogeneityData Quality ProgramsData AcquisitionData StorageData RetrievalData in the Mining IndustryGeological DataOperations DataGeotechnical DataMineral Processing DataSummaryReferencesMaking Sense of DataIntroductionI: From Collection to Preparation and Main Sources of Data in the Mining IndustryII: The Process of Making Data Prepared for ChallengesData Filtering and Selection: Can Tell What is Relevant?Data Cleaning: Bad Data to Useful DataData Integration: Finding a Key is KeyData Generation and Feature Engineering: Room for the NewData TransformationData Reduction: Dimensionality ReductionIII: Further Considerations on Making Sense of DataUnfocused Analytics (A Big Data Analysis) vs. Focused Analytics (Beginning with a Hypothesis)Time and Date Data Types TreatmentDealing with Unstructured Data: Image and Text ApproachesSummaryReferencesAnalytics ToolsetsStatistical ApproachesStatistical Approaches SelectionAnalysis of VarianceStudy of the CorrelationCorrelation MatrixReliability and Survival (Weibull) AnalysisMultivariate AnalysisState-Space ApproachState-Space ModelingState-Space ForecastingPredictive ModelsRegressionLinear RegressionLogistic RegressionGeneralized Linear ModelPolynomial RegressionStepwise RegressionRidge RegressionLasso RegressionElastic Net RegressionTime Series ForecastingResidual PatternExponential Smoothing ModelsARMA modelsARIMA ModelsMachine Learning Predictive ModelsSupport Vector Machine and AVM for Support Vector Regression (SVR)Artificial Neural NetworksSummaryReferencesProcess AnalyticsProcess AnalyticsProcess Analytics Tools and MethodsLean Six SigmaBusiness Process AnalyticsCases & ApplicationsBig Data Clustering for Process ControlCloud-Based Solution for Real-Time Process AnalyticsAdvanced Analytics Approach for the Performance GapBDA and LSS for Environmental PerformanceLead Time Prediction Using Machine LearningApplications in MiningMineral Process AnalyticsDrill and Blast AnalyticsMine Fleet AnalyticsSummaryReferencesPredictive Maintenance of Mining Machines Applying Advanced Data AnalysisIntroductionThe Digital TransformationHow Can Advanced Analytics Improve Maintenance?Key PdM – Advanced Analytics Methods in the Mining IndustryRF Algorithm in PdMANN in PdMSupport Vector Machines in PdMK-Means in PdMDL in PdMDiagnostic Analytics and Fault AssessmentPredictive Analytics for Defect PrognosisSystem Architecture and Maintenance in MiningMaintenance Big Data CollectionFramework for PdM ImplementationRequirements for PdMCases and ApplicationsDigital Twin for Intelligent MaintenancePdM for Mineral Processing PlantsPdM for Mining FleetReferencesData Analytics for Energy Efficiency and Gas Emission ReductionIntroductionAdvanced Analytics to Improve the Mining Energy EfficiencyData Science in Mining IndustryHaul Truck FC EstimateEmissions of GHGMine Truck FC CalculationArtificial Neural NetworkModeling BuiltApplication Established NetworkApplied Model (Case Studies)Product Results EstablishedOptimization of Efficient Mine Truck FC ParametersGenetic AlgorithmsGA System DevelopedOutcomesConclusionReferencesMaking Decisions Based on AnalyticsIntroductionOrganization Design and Key Performance Indicators (KPIs)Embedding KPIs in the Organizational CultureDecision Support ToolsPhase 1 – IntelligencePhase 2 – Data PreparationPhase 3 – DesignPhase 4 – ChoicePhase 5 – ImplementationAAs Solutions Applied for Decision-MakingIntelligent Action Boards (Performance Assistants)Predictive and Prescriptive ModelsOptimization ToolsDigital Twin ModelsAugmented AnalyticsExpert SystemsESs Components, Types, and MethodologiesESs TypesESs Methodologies and TechniquesRule-Based SystemsKnowledge-Based SystemsArtificial Neural NetworksFuzzy Expert SystemsCase-Based ReasoningESs in MiningSummaryReferencesFuture Skills RequirementsAdvanced-Data Analytics Company Profile – Operating ModelWhat is and How to Become a Data-Driven Company?Corporative CultureTalent Acquisition and RetentionTechnologyThe Profile of a Data-Driven Mining CompanyJobs of the Future in MiningFuture Skills NeededChallengesNeed for Mining Engineering Academic Curriculum ReviewIn-House Training and QualificationLocation of Future WorkRemote Operation CentersOn-Demand ExpertsSummaryReferences
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