Real Time Operational Perfomance and Variability Supervision

Transcription

1 Real Time Operational Perfomance and Variability Supervision Diego Arenas CONTAC INGENIEROS OPERATIONAL INTELLIGENCE TECHNOLOGIES Empowering your PI RtPM infrastructure

2 Why real time KPI s? Ore body Chemical Energy Electrical Energy Key Variables: Ore Grade Ore Hardness Ore Type Equipment availability Mining Activities: Stripping Drilling Blasting Truck loading Sulphide Ore Waste Oxide Ore Low Grade Ore ROM Ore (run of mine) Same for Steel, Pulp&paper, Petrochem, Utilities, etc. Quality Calidad Rendimiento Efficiency Cost Costos Confiabilidad Reliability Process operations are complex systems, where perfomance goals are evaluated using different business KPI s criteria. Business KPI time-frames ranges from long term operational management control loops, such as mine planing-mine operation, to short term, such as concentrator recovery control vs. short term program. Despite the KPI time frame, most of the business KPI are rooted to the operation, it can be said that business KPI are built in real time.

3 The value fishbone,...how the business KPI s are built Quality Calidad Cost Costos Rendimiento Efficiency Confiabilidad Reliability Business KPI s are built in real time. Gains and losses happen in real time A process bottleneck (leading to quality, throughput losses): a conveyor, or a pump or a control valve working at its maximum capacity or speed for an extended period of time A cascaded (and $$$$ amplified) variability: cyclone feed density variability that leads to a size variability that leads to a flotation recovery variability. Quality losses (plus: energy, operational time, consumables, etc.): because of an inefficient operation of its regulatory system.

4 What we know about the process behavior? Large number of process variables Complex cause-effect relationships Different (time based) Operation Modes: drift, noise, start-up, set point changes, disturbances, etc. Large data bases for Real Time and Historical information about process variables and equipment vital signs. The Need: an automated system capable to online analize thousands of process variable data and generate context based meaningful information How to determine early alerts if the process or machine is deviating from a pattern or moving to a new one, being an on quality pattern, a throughput pattern, a malfunction pattern, etc.? How to determine the most influencing factors that drives the evolution of a certain process variable or equipment vital sign KPI?

5 Performance Model Mapping: Op. Goals of: With: Quality, Emissions Real Time KPI index Energy, Consumables Thoughput, etc. Perfomance Model Critical Factor Critical Factor Inst. CAPA &CONTROL DE CONTROLLAYER Perfomance Critical Factor Inputs CAPA Process DE PROCESOS Layer Factor Factor Perfomance Goal Outputs Potential Capacity Factor Factor Capacity Factor Factor Production Perfomance Goal Critical Factor

6 Technology CONTAC Perfomance Tests (RtKPI) Statistical Control Loop quality Variability Multivariable Analysis Visualization Modules Control charts Tree Map Benchmarks Star diagram Dispersion diagram Histogram Other

7 ACE Calculation Engine PI System AF Model Technology, the architecture Template Collection of perfomance tests (RtKPI) Perfomance Model Real Time and Historical Information The high level view Visualization, reporting, alarming DCS Regulatory Systems the detailed view Operational Processes Business Processes

8 Technology, the test template collection Monovariate Indicator D3 Index Measures Scheffe s test allows detecting important disturbances, given a determined reference Harris s Index Oscillation Index Time in State Saturation IAE Index ACR Index FFT Index Standard Variation Index VFA PLS Multivariate Indicator Compares the variance of the controller with its minimal expected variance Index of oscillation of the controller for a given setpoint. It can also be used with a pair of related variables Time in which the the controller has been in manual mode or another pre-defined state % of time where the analyzed variable is above or below a certain limit The integral of the absolute difference between the SP and the PV Miao s and Seborg s oscillation index Oscillation index considering the fast Fourier transform Complement to the Harris s index. It measures the deviation with respect to a reference point Measures Weight or relative influence that a set of signals has on the variability of the process Soft-sensors Comparison between expected and measured values (residual generation) Prediction of signal values A collection of online KPI to: Identify Measure Alert Real Time variability Abnormal Op. Cond. Disturbances GOAL: Business KPI

9 PI-AF, PI-ACE, Technology, the implementation A form to define the specific parameters for each RtKPI (tag s, limits, offset, etc.) The collection of RtKPI templates

10 Technology, the implementation Applying the RtKPI to a perfomance Model

11 The implementation The model is a context based representation of the Factor-Critical Factor-Cause&Effect relationships Factor A Sump Pump Quality Calidad Costos Perfomance Efficiency Rendimiento Throughput and quality KPI Confiabilidad Reliability Ore Size Factor B Density Control Factor C Class. Harris Index

12 The technology, summary Allows for a direct mapping between the Business KPI and its Real Time drivers. Allow for the implementation of an automatic, online perfomance supervision system, working in a 24x7 shift A scalable and modular technical architecture. Plants, Lines, Processes, Equipment models can be implemented. Since the calculated index are sent back to the plant historian, the values can be trended, alarmed, shown on process diagrams, reported, ed, etc.

13 ACE Calculation Engine PI System AF Model The Implementation Template Collection of perfomance tests (RtKPI) Perfomance Model Real Time and Historical Information AD-HOC -Project specific- (PI-PB, DL, WebParts, etc.) RtIndex visualization, reporting and alarming Configurable RT Analysis DCS Regulatory Systems Operational Processes Business Processes Read data from PI Calculate Rt KPI Send calculated data to PI (as PI Tag s) ---RT-Continuously-24x7---

14 Implementation example Reminder: 1. Core system: KPI templates, PI-AF/ACE calculations are standard 2. Reporting and visualization are built using standard PI-RtPM tools 3. Nevertheless, even PB and DL are project specific, since the construction are made using PI-AF context based models, it can be reused for other applications. Again: what will be shown is a PB+DL app., it can be modified, reused, re-done, etc., using the RT Indexes being calculated by the standard PI-ACE/AF RT KPI templates

15 Implementation

16 Índices Utilizados Índices Monovariables Harris IAE (ITAE, ISE, ITSE) Miao Seborg Forsman Statting D3 Tiempo en un estado Distribución Desviación Estándar Compara la varianza del error con respecto a la mínima varianza alcanzable. Integral del valor absoluto del error. Índice de oscilación basado en la auto covarianza. Índice de oscilación. Comparación de áreas del error entre cruces por cero. Test de Scheffe s, detecta variaciones de una señal con respecto a una referencia. Tiempo en que el controlador ha estado en modo manual u otro pre definido. Tiempo que una variable ha permanecido en determinados rangos de sus valores posibles. Medida de dispersión para un conjunto de datos.

17 RtKPI Portal (a PI-PB/AF app.) Selected controller or variable Other reports Date selection Navigation tree Variable values distribution histogram Controller state statistics

18 RtKPI Portal (a PI-PB/AF app.) IAE Trend (cummulated deviation) Forsman Index Trend (oscillations) Miao Index Trend (oscillation)

19 RtKPI Portal (a PI-PB/AF app.) AutoCovariance Test (variability-oscillations) Variable values trend

20 RtKPI Report (a PI-DL/AF app.) Report Configuration Model Selection PI-Server Date selection Database and PI-AF Model Name ex.: Plant, Area, Process, Water, Energy, Quality, etc. Color highlighting configuration

21 RtKPI Report (a PI-DL/AF app.) Report

22 RtKPI Report (a PI-DL/AF app.) Report Views

23 DEMO OPERATIONAL INTELLIGENCE TECHNOLOGIES Empowering your PI RtPM infrastructure

24 Real Time Operational Perfomance Supervision Luis Yacher S.;Felipe Kripper; Felipe Lineo CONTAC INGENIEROS OPERATIONAL INTELLIGENCE TECHNOLOGIES Empowering your PI RtPM infrastructure