基于人工智能的采石场调度优化和智能采石场监控

(Dr. Puneet Nigam) WEBINAR FLOW 01Computer Aided Deposit Evaluation (CADE)02Quarry Scheduling Optimization (QSO)0304050607Predictive v/s Actual Quality analysisBlock Model updationRemote Quarry Monitoring through AICost AnalysisGeneral2 GENERAL-LIMESTONE FOR CEMENT INDUSTRY Require StrategicPlanner by blending ofdifferent grade withminimum deviation Non RenewableResources given by STEPS FOR DEPOSIT EVALUATION Exploration Deposit Optimization Validation of Model General investigationDetail investigation Estimated vs ActualDaily Online Monitoring by AI Finalization of Sensitiveplantparameters Computer AidedDeposit Model Long TermQuarry scheduling SiteIdentificationReconnaissance SurveySelection of potential area Conceptualized deposit behaviourReserves Assessment CADE AND QSO–FLOW CHART ❑DrillholeDatabase❑GeologicalCrossSections❑GeologicalModeling❑DigitalTerrainModeling❑Statistical/Geo-statisticalModeling❑BlockModeling❑Resource/ReservesClassification❑GradeTonnagestudy❑MineableReservesandQuality QSO-METHODOLOGY QUARRY MANAGEMENT Vision Setting for Mines and Plant Personal Stable clinker production process with maximum production and minimumproduction cost Emission control from the source through controlling minorelements in rawmix Coordination between Mines and PlantReduce corrective consumption means lower production Maximize quarry lifetime through judicious blending OBJECTIVE TO MINIMISE QUALITY DEVIATION REMOTE QUARRY MONITORING-WHY 01 02 WHAT DOESREMOTEQUARRY Assures steady supply of homogenizedmaterial to meet changing quality 03 04 0505 0406 SMARTSTRATEGIES SYSTEM–REMOTE MONITORING BLOCK MODEL UPDATIONPREDICTIVE V/S ACTUAL QUALITY ANALYSIS REMOTE QUARRY MONITORING THROUGH AIDATA FORMAT MINING PROCESS INTELLIGENCE Source5 Mines/Faces + 3 corrective additives AI-Enabled BlendOptimizer AlgorithmAI based Intelligent system OutputT/day per source + blend chemistry AI-Driven Mine Selection & Corrective Additive Dosing forTarget LSF · SM · AM or any radicals for Raw Mix Optimization ModuliLSF · SM · AM achieved vs target 98.0Target LSF ScenariosQuality mode vs Cost modePile mode vs Raw Mix mode 1.10 2.20 Target AM Target SM SensitivityMine chemistry variability analysis OPTIMIZER OUTPUT B L E N D E D L S F98.03Target: 98.0 |✓OK C 3 S P O T E N T I A L~56%Clinker quality |✓Good S I L I C A M O D U L U S2.204Target: 2.20 |✓OK A L U M I N A M O D U L U S1.183Target: 1.15 |▲Review B L E N D E D F E E D C H E M I S T R Y42.8%CaO13.8%SiO₂4.1%Al₂O₃2.5%Fe₂O₃0.5%MgO31.2%LOI QUALITY vs COST MODE System Architecture & Implementation PHASE 1Month 1–2Data Foundation PHASE 2Month 3–4 AI DeploymentPython based engine integration · Daily blend recommendation output ·Quality vs Cost Mode toggle PHASE 3Phase 3Month 5–6 DCS/SCADA feed integration · Auto-update mine chemistry shifts · Alert Update Frequency PRODUCT ROADMAPCOST ANALYSIS–CASE 2 (SOUTH AFRICA) Bymodelvalidationandreschedulingthereisasavingofapprox.2.10mioUSDperannumjustbyreductioninreject.Theadditionallifefurtheraddstothesavingoveradditional16years. RAWMATERIAL SERVICES RAW MATERIAL SERVICES–HOLTEC’s USPs HoltecConsulting Pvt Ltd HoltecCentreA Block, Sushant Lok-I, Gurgaon, info@holtecnet.com +91 124 4047900 www.holtecnet.com