Dynamic Risk Analyzer TM (DRA)

Transcription

1 Dynamic Risk Analyzer TM (DRA) Early Risk Detection for the Process and Energy Industries Ulku Oktem, PhD Co-founder, CEO and President Ankur Pariyani, PhD Co-founder, Chief Innovation Officer Deb Grubbe Chief Marketing Officer Philadelphia, PA

2 Core Concept Accidents Incidents (Observable Near-Misses) Hidden Near-Misses (Not Casually Observable) 2

3 Impact Resolve Process Risks at Initiation Stage with DRA Early identification of process issues when process is still within normal limits Time Initiation Phase Hidden Risks (in Process Data) Alarm / IOW Incident Major Event 3

4 Dynamic Risk Analyzer TM (DRA) Early Risk Detection Software Detects process problems at initiation stage autonomously Alerts operating team timely Analyzes hundreds of tags using patented technology Problem Initiation DRA Detection Operations Team Safe & Reliable Operation Process Data Precursors Corrective Actions 4

5 DRA Difference Typical Operating Unit: Has thousands of tags (temperatures, pressures, etc.) Typical Engineer: Spends 1-2 hrs and reviews tags/day (~3% tags) using Spreadsheets / Historian / Trending tools Engineer WITH DRA: Spends mins to review all tags (full peripheral vision) DRA points out problem areas autonomously

6 DRA Architecture Industrial Control System Firewall SECURED CUSTOMER NETWORK Managers Firewall DCS ONSITE DRA SERVER SCADA PLC/ Instrument systems OPC Historian Engineers Operators Process Control Network Business Network 6

7 Customer Case Studies Early Detection of Operations Issues EQUIPMENT ISSUES Compressor Vibration Problem Product Bucket Problem Control Valve Plug Erosion Pressure Control Valve Plugging Level Control Valve Plugging Pump Cavitation Pump Clogging PROCESS ISSUES Steam Leak in Sulfur Bath Hydrocarbon Carryover Furnace Plugging Line Plugging Lube Oil Filter Clogging Temp. Controller Problem Overhead Seal Oil Issue OPERATOR ISSUES Operator Error Unstable Operating Procedure INSTRUMENTATION ISSUES Sensor Flatlining Faulty Transmitter I/O Card Module Failure 7

8 Customer Case Study: Compressor Vibration Problem DRA identified anomalies in vibration variables for a rotary equipment 120-day Trend of LPC Axial Vibration (in mm) Early indications by DRA Unit was running at higher load than normal No alarms were activated (high alarm at 1 mm) Rectified by center realignment of compressor Maintenance shutdowns 8

9 Customer Case Study: Control Valve Plug Erosion DRA identified anomalies in valve opening, indicating slow but steady decrease Level Control Valve Opening (60-day trend) Possible root causes were identified early on and spare parts were ordered Investigation showed that the valve plug erosion caused the decrease in opening (see below) Early indications by DRA 9

10 Customer Case Study: Hydrocarbon Carryover Avoidance Findings Anomalies detected by Production Supervisor on mid-level indicator for an amine sweetener column and communicated promptly to Operation Engineer. Actions Operation Engineer confirmed the abnormality and raised notification. Benefits Early detection of the anomalies prevented a possible hydrocarbon carryover which will have resulted in potential further downstream unit interruption. 10

11 Customer Case Study: Pump Cavitation Findings Anomalies detected by Operation Engineer through DRA indicating sudden drops in LC503.PV Early indications by DRA Actions Maintenance Order was raised to apply engineering solutions for the equipment. Benefits Early detection of drops prevented further possible pump cavitation (P ), which will have resulted in costly equipment damage and process interruption. 11

12 Benefits of DRA Beyond Just Risk Detection Uptime, Efficiency, HSE, Quality Prevent emergency maintenance Move from reactive to PROACTIVE culture Increase peripheral vision and team s productivity Benchmark plants and operations 12

13 DRA INSTALLATION

14 DRA Hardware Requirements (Per License) Processor: RAM: Storage: Operating System: Server type: 4 CPU cores (physical cores) at 2.8 Ghz 16 GB 1 TB of HDD Windows Server 2012 or Linux OS (RedHat/SUSE/Ubuntu) Virtual machine (VM) or physical server NOTE: Specs are for each DRA unit and are sufficient to support ~5 years worth of data. A higher end server can be used to install multiple DRA instances on a single server. Near-Miss Management LLC 14

15 DRA Implementation Steps FACILITY CHECKLIST For each DRA Unit to be installed Configure a Server for DRA (IT) Identify tags to be monitored (Operations) Organize tags into groups (Operations) Identify shutdown rule(s) (Operations) Near-Miss Management LLC 15

16 DRA Implementation Steps NMM CHECKLIST Install DRA software in collaboration with Facility IT Populate tags to be monitored Program shutdown criteria Schedule historical calculations Conduct user training ~ 1-2 days ~ 2 weeks ~ 2 hrs x 3 sessions Near-Miss Management LLC 16

17 DRA in PETRONAS Annual Sustainability Report 2015

18 Award Finalist Nov 2017

19 Award Finalist June 2017

20 Process Interventions & SAP Maintenance Summary HYDROCRACKER

21 BEFORE DRA BIG DATA (Per Process Unit) 700+ TAGS Selection of tags by Engineers ~ 2 hrs ~ 2 hrs Analysis via trending tools (Historian, Excel, etc.) Identification of potential problems ~ 1-2 hrs 1M+ DATA POINTS RECORDED DAILY Problem Resolution AFTER DRA Communication with Maintenance/Operations via s, Phone Calls, etc. ~ 15 mins How does one identify the risk information buried in this data timely and efficiently? Autonomous analysis of 700+ tags Results available for morning meetings Quick identification of potential problems Fast communication with Maintenance/Operations Problem Resolution

22 WITHOUT DRA DAILY REVIEW BY ENGINEER MORNING MEETING WITH OPS GROUP FOLLOW-UPS DURING DAY 24x7 PROCESS MONITORING BY OPERATORS Review of ~15-25 tags Review based on operating limits and alarms: Urgent process response Maintenance requests WITH DRA Actions based on review of 25 tags Follow-up on effectiveness of actions DAILY REVIEW BY ENGINEER MORNING MEETING WITH OPS GROUP FOLLOW-UPS DURING DAY Review impact of change Analysis of 700+ tags Quick identification of top 5-10 risks Review based on DRA indicators: Early process interventions Timely Maintenance requests Actions based on review of 700+ tags

23 DRA Differentiators Early Detection of Problems Peripheral Vision (Enterprise-Wide Risk Information) Self-Directed Engine Rapid ROI Drill-Down to Problem Areas Intuitive Interface Institutional Memory 23