PATSIMO SYSTEMS. About Us - Data Analytics for Power and Process Industries

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1 PATSIMO SYSTEMS Data Analytics for Power and Process Industries About Us - Power & Process Industry Consultants Specialize in various services like Thermodynamic modeling, Thermal performance analysis & Simulation, Anomaly Detection Experience in delivering 25+ projects in 10+ countries Experience in Power, Cement, Paper & Pulp industries OEM Experience (GE Energy, USA) O&M Experience (Combined Cycle and Thermal Plants)

2 Industries Profitability Combined Cycle Plants Thermal Power Plants Nuclear Plants Refinery Paper & Pulp Operating profits = (reliability, performance) Reliability & Performance are the two most important factors that govern the profits of any Industry.

3 Problem Statement Equipment Failure Despite adopting various Reliability Centred Maintenance practices, many industries experience failure of expensive equipment often resulting in significant losses For power industry, machinery breakdown and the associated Business Interruption exposure remain key underwriter concerns Source: * Electric Power Research Institute (EPRI) estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA gas turbine compressor at USD $10 to $20 million.

4 Problem Statement Indian Industry Scenario Lack of Data Many industries in India lack Industry standard historians that can capture and store data at their native resolution. This issue is more pronounced in plants with old DCS/SCADA systems. Lack of data seriously limits the capability to understand equipment behavior Lack of Tools/Expertise to Analyze data More than 90% of Power and Process Industries around the world lack intelligent systems that detect anomalies and provide advance warnings about equipment health

5 What do we do and where do we fit in? We Collect & Store We Analyze & Report Sensor Analysis Anomaly Detection Diagnostics Alerts, Notifications Our Integrated platform helps Store and Analyze process data!

6 We convert Data to Information.. Process Data historian Raw Data Data Archive InDB Historian Analytics SensorDNA, ProcDNA TM Information Diagnostics and Notifications DiagnEASE

7 and deliver it to end users

8 Thermodynamic Models First Principles LM 2500 Gas Turbines (GateCycle) Industrial Gas turbines Nuclear Plant Secondary Cycle (IPSEpro) Coal Fired Plant (Supercritical) Detailed Component level models to analyze real time thermal performance of power plant equipment built using GateCycle & IPSEpro software

9 Empirical Methods Methods that understand the response of equipment and condense it into a Signature Neural Networks Statistical Methods etc. Methods that use raw data Clusters Big Data Analytics Empirical Methods are used in areas where building models based on first principles is not practical. Predictive models of Emissions, Metal temperatures, Combustion, Winding temperatures, Vibrations etc.

10 Our Strength - Integrated Approach Predictive Models Thermodynamic models Statistics/Clustering /Big data Artificial Intelligence Reliability

11 How do we do it? Artificial Intelligence Thermodynamics Statistics Our Software Modules - InDB - Historian SensorDNA ProcDNA Trainer ProcDNA TM DiagnEASE 1. Capture and Archive raw data from DCS via OPC 2. Analyze Raw data and detect instrument issues 3. Filter data and build Machine Signatures 4. Analyze real time data for Anomalies using signatures 5. Provide Diagnosis for Anomalies Our Expertise

12 Understanding Equipment Failure Time frame of anomaly detection with our software Onset of Anomaly Advance warning with our software Perceptible change Time frame of problem detection with regular monitoring Machine Condition Vibration Monitoring Oil Analysis RCM techniques etc. DCS Alarm level Predictive Zone Preventive Zone Failure Zone Failure Time

13 Predictive Models How do they work? DCS Data Historian(s) InDB ~ typically 1500 to 2000 sensors per plant (vibrations, flows, temperatures, pressures, currents, voltages etc.) Raw data OSI PI Model Predicted data Raw data Model predicted data Our Predictive Models Difference

14 ProcDNA TM Integrated Solution ProcDNA TM features Artificial Intelligence and Statistical methods are used to detect Anomalies in equipment and processes. OEM independent solution - analyzes data of all critical equipment rotating as well as non-rotating Seamlessly interacts with historians like InDB, OSI PI, MS SQL, Oracle etc. * Applied for Trademark registration Can analyze historical data as well as current data (real time analytics) Very little software footprint. Runs as a service in the background and needs very little user intervention. * Our software can talk to all industry standard historians & data sources Single instance of ProcDNA TM can execute calculations of multiple projects sequentially minimizes the cost of deployment for fleet owners

15 Market Size for our Software and Solutions Our Software would be readily applicable to plants that generate roughly 90% of the Global Power and Process Industries.

16 Cost Vs Benefit to Customers Benefit to customers: Power and process industries are typically equipped with numerous critical & expensive equipment Compressors, Combustors, Generators, Turbines, Pumps, Motors, Valves, Heat exchangers etc. Even if a single anomaly is detected in its entire life time, our anomaly detection system would have paid for itself may times over to customers. *Typical ROI to customer is less than 1 year. It is not unusual to detect anomalies during the project execution phase itself.

17 Industries we serve Our Industrial Data Analytics software and solutions apply to Power Combined Cycle Coal Nuclear Wind & Solar Hydro Co Generation Oil &Gas Process and Manufacturing Industries Paper & Pulp Cement Fertilizer etc.

18 Thank you! Patsimo Systems , Main Road Kakinada India