- Email: [email protected]
Enterprise Manufacturing Services to enhance Energy effectiveness and Sustainability Management at Anglo American Platinum
16th IFAC Symposium on Automation in Mining, Mineral and Metal Processing August 25-28, 2013. San Diego, California, USA
Enterprise Manufacturing Services to enhance Energy effectiveness and Sustainability Management at Anglo American Platinum Osvaldo A. Bascur *. Michael Halhead. **
* OSIsoft, LLC, Houston, TX 77019 USA (Tel: 936 443 6527; e-mail: [email protected]). **Anglo American Platinum, Johannesburg, RSA (e-mail: [email protected])
Abstract: Large Metallurgical Complexes are large users of Energy, Water and Assets. There are 1000 thousands of meters to manage the information at the local and enterprise level. To improve the effectiveness of energy and assets the quality of the data and events, it becomes a paramount for real time operational management. The lack of resources at the local operations and at the enterprise to process the sea of information becomes impossible and many projects have failed. As such, a novel approach for implementation of continuous improvements at the local level and innovations at the strategic level was implemented at all operating plants at AngloPlats. The new capability of an enterprise real time monitoring and diagnosis software infrastructure was available to implement many business strategies in tandem. As such, asset monitoring, energy, production and process control management was implemented in an integrated approach reusing the same data but with different context and time horizons to have standard methodology for local root cause analysis. At the same time, real manufacturing services to support operations and keep the continuous improvement and innovations found as new opportunities at the enterprise are found in industry. This paper will highlight the required computer architecture, data hierarchies’ approaches for adaptive reporting, condition based event management and notifications. The results based on the integrated and collaborative team efforts will be presented. PI System for a targeted reduction in energy consumption of 15% by 2014.
Keywords: Real Time Information Infrastructure, Data Validation, Operational Alerts, Gross Error Detection, Fault Diagnosis, Sustainability, Enterprise Energy Management.
information which allows them to identify long term initiatives and recommend new strategies (innovations) for changing. This collaborative and benchmarking strategy is fuelled by dynamic performance monitoring and a proactive environment that promotes situational awareness. Early adoption and incorporation of the operational design can be incorporated in the process object information models.
1. INTRODUCTION One major challenge to sustainability is the need for collaborative, enterprise data management of real-time and historical data not only within an organization, but also between businesses such as a mining company and the local water and energy utilities. Easily accessible real-time data and information is a key enabler to optimize decision making, and to achieving sustainability.
2. REAL TIME INFORMATION INFRASTRUCTURE STRATEGY
Major corporations are improving ways to design and manage their industrial complexes incorporating Dynamic Information Management Systems Barrios et.al. (2003), Vega, (2003), Hanneman, (2008), Rojas, (1998) and Lobo, Paredes, (2007) to reduce water consumption and environmental impact, realtime data and analytical tools are needed to promote collaboration between domain experts in the company. To achieve positive results, a continuous improvement and innovative management strategy is imperative Bascur and Kennedy (2001) and Bascur, Hertler, Wong, (2011), Bascur, Linares, Schwenzer, (2008) discussed the requirements for building a collaborative enterprise environment to enable collaboration between the operational and strategic teams. These teams currently do not have access to the detailed 978-3-902823-42-7/2013 © IFAC
When integrating metallurgical information into a common platform, it is important to keep the enterprise goals of the company in mind. A common toolset can now be used to access time-series data and also non time-series data from external sources, Bascur et.al, (2011). This strategy gives plant-floor visibility to asset and process performance and allows operators and engineers to trend, correlate, and analyse important operational factors. To make this scalable and visible to the enterprise, it is necessary to create an asset data directory. The business drivers for performance management are compelling. Among them: 182
10.3182/20130825-4-US-2038.00110
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Electricity is becoming increasingly expensive. Anglo Platinum is targeting a 15% reduction in power consumption by 2014. Process division accounts for the bulk of the discretionary electrical power.
• Collaboration throughout the extended enterprise, including global resources, suppliers and customers • Empowering individuals to make profit-contributing decisions at all levels of the organization • Closing the loop between active planning and goal setting, and actual execution in real time • Bridge centralized and decentralized organizational structures.
The major challenges are: lack of power measurements, numerous SCADA Subsystems implemented via independent projects, large quantities of data. So, a companywide integrated approach to energy savings is required. The necessary information to develop these plans is becoming available only now.
The function of the real-time information infrastructure is to support continuous improvement, increased profits and reduced costs. This is quite different than Enterprise Management, which is targeted at continuous improvement of margin but driven by transactions instead of real time events.
The first step is to implement a robust software infrastructure to build many business strategies on a common and simple one to maintain enterprise dynamic real time information system. The first business challenge can be described of having large number of instruments across the group; approximately 100,000. They have large amount of data; approximately 700,000 tags are logged in additional to all operating and equipment events gathered required for data validation, gross error detection and transformation into operating information. Mineral processing plants are a harsh environment for instruments. Certain instruments are critical to safety management. The quality of the data leads to better quality information. In summary, Anglo American required a mechanism to monitor the quality of the instruments/data and clean/reconstruct the data where practical.
With a traditional process information management system, information is gathered into a database and is disseminated as reports and on line inquiries to all requesters, and there the system’s responsibilities END. This system is passive; it is not designed for ACTION. A real-time information infrastructure allows users to apply their knowledge and expertise to find the root causes and continuously improve performance using operation states and event frames, Bascur & Kennedy, (2004). This presentation covers the use of the PI System, specifically the PI Asset Framework (PI AF) and PI Event Frames (PI EF), as the cornerstone of the Operational States Management solution created by Anglo Platinum. The original intent was to create a Downtime Reporting solution, but because of the features of PI AF and PI EF, the application was easily extended to cover any Operation State. Anglo Platinum created a front-end Silverlight Web application to view, search, and report on the event frames that are generated by the application. The benefits of this solution are time savings, standardization; event data can be displayed in many different ways, and can be extended to include new operational states just by configuring new elements in PI AF. 3. ANGLOAMERICAN PLATINUM CASE STUDY Anglo American Platinum is the world’s premier Platinum Group Metals (PGM) producer, supplying approximately 40% of the world’s newly refined Platinum. It has 14 concentrators, 3 Smelters, 1 Converter, 2 Refineries, 9 geographic operating areas. The Platinum process has a long value chain in comparison to most minerals. It is a technically complex and comparatively low volume but high value products. It has a significant material pipeline and Energy and water consumption intensive.
Fig. 1. Enterprise real time integration for operational business support This leads to the implementation of distributed information architecture, primarily due to limited network bandwidth as shown in Fig.1. Each operation has a complete PI System implementation. Key calculations are performed at the level they are used; primarily of the local sites. Selected data is rolled-up to a central Pi System. A master PI AF (Asset Framework) and replicated to site. Fig. 2 shows the PI System real time infrastructure and integration to other systems. The PI Central System is used as a Competence for Manufacturing Services. It is used for Group wide operational management analysis, single point of integration to certain business systems, maintain a standard operational metrics for all assets, industrial plants and chain supply analysis.
Anglo American Platinum has multiple energy sources: Electrical, Diesel and Steam from coal fired boilers. Electrical energy is the initial focus. Anglo Platinum is a large consumer of electricity. The electrical system in Republic of South Africa became constrained in year 2008 leading to nationwide load shedding. The electrical system is still constrained; this is likely to remain constrained for the next couple of years. 183
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
5.1 Gross Error Detection Methods for data validation for early diagnostics. The GED method detects data errors on simple metrics using simple, fast mathematical algorithms. No models are implied or used, therefore there is very little maintenance. The only model used in the standardized time based algorithm to estimate the time series evaluation of time derived metrics in the PI System subset of algorithms (Min, max, rate of change, mode, standard deviation range at a certain interval for the assets (Such the pulse on a human). Each data point can have a single state. The states are prioritized as shown in Fig. 4. The data is then classified into the following states: Missing Data, Not Running, High, Low, Not Updating, High rate of change, Simulated, BAD Data (Bascur, Hertler, Wong, (2011), Bascur, Linares and Schwenzer, (2008)). The bulk in the GED calculation is handled by a customer data reference and a formula data reference in PI AF. The services read the data from PI AF and write the results to a PI AF attribute. PI AF Seamless writes the results to PI. Communication with PI AF is via PI AF Software Development Kit (SDK).
Fig. 2: Real Time information infrastructure integration for analysis and collaboration A generalized plant model has been built with common way to provide context to the data. There are many context opportunities to be given to the data depending on the business needs. One of the best practices is the S95/S88 standards; however, we have found additional simple one, which makes the collaboration between the local plants and the strategic enterprise team much more effective to communicate improvements and provide easier diagnostics.
Fig. 4: Event data classification for transformation into validated information The benefits of an enterprise data validation and instrument monitoring and diagnosis are the key for implementing energy, asset monitoring, environmental and safety monitoring at the enterprise. At the local level, improve production, quality, and costs reductions. These events are then used to perform analysis using Business Intelligent tools using Real Time Data from the operations at the original resolution. As such, the system enables to detect all type of losses which can be easily attributed to the operating or equipment states. This is the KEY objective of using the PI System tools and algorithms empowering the transformation of raw data into business information.
Fig. 3. Data validation strategies The Common Structure Models are consistent across all sites. The object structures are maintained on the central PI System and are replicated to the sites. One of the main preliminary activities conducted for the deployment of the Enterprise PI System was to standardize in the data validation methods. There are benefits and maintenance issues, depending on the amount of complexity. The traditional data validation methods can be classified according to the degree of maintenance required; these are gross error detection, filters, statistical methods and model based data reconciliation as shown in Figure 3. These methods are not mutually exclusive. Gross error detection (GED) is normally the basis of all data validation techniques (Bascur et.al 2011). The cost of implementation increases with the complexity.
The standard GED implementation contributes to a broader vision, supports the “One version of truth, better quality of information and most importantly a standardize method for alerting, predictive alerts and overall continuous improvements and diagnosis for optimization. The solution makes the instruments availabilities unequivocal. Faulty and simulated instruments have resulted in serious equipment failures in the past. The visibility highlights these issues in proactive manner avoiding costly stoppages and repairs. Fig. 5 shows an example of the analysis using Real Time Data and Operating and Equipment events.
The more sophisticated techniques are typically reserved for a small collection of instruments/data. GED can be applied with minimum effort to a variety of cases.
184
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Fig. 6: Overall Energy Consumption and specific analysis for the 80/20 actionable report. The key to Energy reduction has been empowering people to act with information. The most important factors are: Present the information in an easy to understand manner, develop high level metrics linked to production and raw material resources. “You cannot control what don’t measure and UNDERSTAND” The Understand is the value of implementing a common currency scale model (PI AF Context and transformation of time of derived metrics) for actionable information. This strategic implementation makes the difference of just providing data out of context and not scaled properly. Now, people can assign value to the performance metrics for effectiveness integrating asset availability, environmental impact, energy and water specific consumption all process plants, areas based on type of products. As such, clearly identifying products losses, resources consumption and improving process safety. This is what has been called by a strategic sustainable environment by Michael Porter, 2011.
Fig. 5. Two view of the Gross Error Detection and Data Validation example. 5. REAL TIME ENERGY MANAGEMENT AT THE ENTERPRISE LEVEL. A companywide integrated approach to energy savings is required. The necessary information to develop these plans is becoming available only now. This approach requires both a new strategy in building a reliable multi-functional systems as well as a change the operational culture required for continuous collaboration at the local and enterprise strategic organization and systems. It is human behavioural change as well as a new computer and software integration (Halhead, (2011)).
The granular break down of the power consumption in an easy format makes it possible to take action, to evaluate very quickly the 80/20 Paretto analysis to get the problem very fast rather than wasting hours in finding the 20% important things to do that will contribute to the 80% of the savings. As such, the implementation of a continuous improvement and strategic innovation has proven to be the WHAT, WHERE and HOW to work with today’s technologies and collaboration at the LOCAL and Strategic teams. Figure 6 shows to the whole enterprise the online interactive online reports using Microsoft SharePoint 2010 with Excel Services using PowerPivot and the PI Webparts.
A central energy measurement and management sub system (CEMMS) was installed in 2009/2010. Now, Anglo Platinum has online measurements of its power consumption. Top level power consumption has been available but lacks granularity for costs avoidance and losses of energy. The precious metal refinery was used as a pilot site. This subsystem was integrated to the WAN and it was done with as fault tolerant system using standard interface for better support and NOC monitoring. The PI AF is used to provide the connections of validated information into the right context to analyse the information in multiple ways. Think of a real-time multidimensional data cube where the organized real time information by UNITS and AREAS can be filtered by operational states, equipment operation states, type of products and raw materials for any time and for one plant or many plants.
As such, actionable information becomes available to define the next step for root cause analysis or for the strategic team to make an economical assessment of the big picture (Energy Management, Environmental Management, Asset Optimization, Training, and Collaboration). The real specific energy consumption is commonly evaluated to look for improvements in the short term. Using this approach PI Notifications is used to proactively alert if getting closer to minimum or maximum constraint based on the real time
185
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
calculations of rate of change, min, max, totals, standard deviation, mode and averages.
Fig. 8 Daily energy consumption analysis by process unit. Despite the advances in automatic data collection and archiving, business decision makers face the problem of exploiting the information that is relevant for plant operations and the sustainability the business enterprise. Providing the data at the original resolution and add-in GED using a Model Tool to add context to the data based on event and Enterprise Best Practices based on a common currency state makes the main difference. This novel strategy improves collaboration and gives real time feedback on decisions made at all levels in the organization. The efficient use of water, energy, and resources is critical and the best approach is to have the infrastructure in place to be able to conduct small focused projects, collaborate between different teams in the organization, and understand that this is a continuous improvement process. Off the shelf technologies are available today to optimize the business while dealing with water and environmental constraints. They generate high quality performance information from process data. The synergy of combining process data with transactional data provides a deeper understanding of the data for continuous improvement and innovation.
Fig. 7 Graphical Analysis of the Energy relative the Enterprise Basis. Figures 6, 7 and 8 show some the multiple types of Energy Analysis enabled by the PI System at the Enterprise. As such, Anglo American Platinum (Amplats) is geared to meet their objective to reduce their Specific Energy Consumption by continuous improvements and innovations which are critically identified by collaboration at the local and enterprise levels of the organization. The integration of all sites and all type of relevant information with event data classification is a must to build a solid robust sustainable operation management strategy.
With this knowledge and examples provided, opportunities for better water and environmental management must be realized in the mining and metal industry to ensure long term sustainable business. Collaboration within and between businesses, government, and citizens to achieve cleaner mining practices is a necessity to maintain and to improve the overall quality of life. The OSIsoft Enterprise Agreement (EA) allowed for the deployment of a consistent PI System infrastructure across the enterprise. Anglo American Platinum uses a variety of OSIsoft products including: PI Server, PI ACE, PI AF, PI DataLink, PI DataLink for Excel Services, PI ProcessBook, PI WebParts, and PI OLEDB Enterprise.
6. CONCLUSIONS One major challenge to better sustainability is the need for collaborative, enterprise data management of real-time and historical data not only across an organization, but also between businesses such as a mining company and the local water utility. Easily accessible real-time data and information is a key enabler to decision making, optimization and sustainability.
REFERENCES Barrios, P., Alonso, A., & Rick, C. (2003). Plant Information System at the Atlantic Copper Smelter and Refinery, In Proceeding of EMC2003, European Metallurgical Conference, GDMH, Germany, pp. 1-11. Bascur, O.A. & Kennedy, J.P. (2001). Real Time Information Management for Asset Optimization, Mineral Processing Plant Design, Mular et al. (eds.), Society of Metallurgical Engineers Publication, Little-ton, CO. www.smenet.org Bascur, O.A. and Kennedy, J.P. (2004). Are You Really Using Your Information to In-crease the Effectiveness of Assets and People?, In Plant Operator Proceedings, E.C. Dowling and J.I. Marsden, (eds.), SME, Littleton, CO: pp. 47-62. www.smenet.org Bascur, O.A., Linares, R. and Schwenzer, G. (2008). Web Based Real time integration of Mining and Metallurgical Information, In First International Congress on Automation in the Mining Industry, Romero F. and Levi. F. (eds.), Automining 2008, www.gecamin.com
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IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Bascur, O.A. Hertler C. and Wong, G. (2011). Improving Sustainability Strategies in Industrial Complexes: Integration and Collaboration, In Proceedings of the EMC 2011, Dusseldorf, Germany Halhead, M. (2011), Data Validation with PI AF at Anglo American Platinum, In Users Conference 2011 Proceedings, OSIsoft, LLC, San Francisco, CA. Hanneman D., and Lubbe, J. (2010). Corporate Sustainability, In Proceedings of Copper 2010, Hamburg, Germany, June. Kennedy, J.P., Bapat and P. Kurchina, (2008). In Pursuit of the Perfect Plant, Evolved Technologist Press. Lobo, J and Paredes, R, (2007). ENDESA Center for Monitoring and Diagnostics. In PI System Regional Seminar, Santiago, www.osisoft.com Rojas, H. & Valenzuela, H.M. (1998). Strategic Plan for Automation and Process Management at the CODELCO Chuquicamata Mine, In Bascur, O.A. (ed.) Latin American Perspectives: Exploration, Mining and Processing, Society of Metallurgical Engineers, Littleton, CO, pp. 281-292. www.smenet.org Vega, R. (2003). Process Analysis using PI Batch View at CODELCO Teniente, 2003 IV Technical PI System Seminar, Santiago, Chile.
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Enterprise Manufacturing Services to enhance Energy effectiveness and Sustainability Management at Anglo American Platinum Osvaldo A. Bascur *. Michael Halhead. **
* OSIsoft, LLC, Houston, TX 77019 USA (Tel: 936 443 6527; e-mail: [email protected]). **Anglo American Platinum, Johannesburg, RSA (e-mail: [email protected])
Abstract: Large Metallurgical Complexes are large users of Energy, Water and Assets. There are 1000 thousands of meters to manage the information at the local and enterprise level. To improve the effectiveness of energy and assets the quality of the data and events, it becomes a paramount for real time operational management. The lack of resources at the local operations and at the enterprise to process the sea of information becomes impossible and many projects have failed. As such, a novel approach for implementation of continuous improvements at the local level and innovations at the strategic level was implemented at all operating plants at AngloPlats. The new capability of an enterprise real time monitoring and diagnosis software infrastructure was available to implement many business strategies in tandem. As such, asset monitoring, energy, production and process control management was implemented in an integrated approach reusing the same data but with different context and time horizons to have standard methodology for local root cause analysis. At the same time, real manufacturing services to support operations and keep the continuous improvement and innovations found as new opportunities at the enterprise are found in industry. This paper will highlight the required computer architecture, data hierarchies’ approaches for adaptive reporting, condition based event management and notifications. The results based on the integrated and collaborative team efforts will be presented. PI System for a targeted reduction in energy consumption of 15% by 2014.
Keywords: Real Time Information Infrastructure, Data Validation, Operational Alerts, Gross Error Detection, Fault Diagnosis, Sustainability, Enterprise Energy Management.
information which allows them to identify long term initiatives and recommend new strategies (innovations) for changing. This collaborative and benchmarking strategy is fuelled by dynamic performance monitoring and a proactive environment that promotes situational awareness. Early adoption and incorporation of the operational design can be incorporated in the process object information models.
1. INTRODUCTION One major challenge to sustainability is the need for collaborative, enterprise data management of real-time and historical data not only within an organization, but also between businesses such as a mining company and the local water and energy utilities. Easily accessible real-time data and information is a key enabler to optimize decision making, and to achieving sustainability.
2. REAL TIME INFORMATION INFRASTRUCTURE STRATEGY
Major corporations are improving ways to design and manage their industrial complexes incorporating Dynamic Information Management Systems Barrios et.al. (2003), Vega, (2003), Hanneman, (2008), Rojas, (1998) and Lobo, Paredes, (2007) to reduce water consumption and environmental impact, realtime data and analytical tools are needed to promote collaboration between domain experts in the company. To achieve positive results, a continuous improvement and innovative management strategy is imperative Bascur and Kennedy (2001) and Bascur, Hertler, Wong, (2011), Bascur, Linares, Schwenzer, (2008) discussed the requirements for building a collaborative enterprise environment to enable collaboration between the operational and strategic teams. These teams currently do not have access to the detailed 978-3-902823-42-7/2013 © IFAC
When integrating metallurgical information into a common platform, it is important to keep the enterprise goals of the company in mind. A common toolset can now be used to access time-series data and also non time-series data from external sources, Bascur et.al, (2011). This strategy gives plant-floor visibility to asset and process performance and allows operators and engineers to trend, correlate, and analyse important operational factors. To make this scalable and visible to the enterprise, it is necessary to create an asset data directory. The business drivers for performance management are compelling. Among them: 182
10.3182/20130825-4-US-2038.00110
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Electricity is becoming increasingly expensive. Anglo Platinum is targeting a 15% reduction in power consumption by 2014. Process division accounts for the bulk of the discretionary electrical power.
• Collaboration throughout the extended enterprise, including global resources, suppliers and customers • Empowering individuals to make profit-contributing decisions at all levels of the organization • Closing the loop between active planning and goal setting, and actual execution in real time • Bridge centralized and decentralized organizational structures.
The major challenges are: lack of power measurements, numerous SCADA Subsystems implemented via independent projects, large quantities of data. So, a companywide integrated approach to energy savings is required. The necessary information to develop these plans is becoming available only now.
The function of the real-time information infrastructure is to support continuous improvement, increased profits and reduced costs. This is quite different than Enterprise Management, which is targeted at continuous improvement of margin but driven by transactions instead of real time events.
The first step is to implement a robust software infrastructure to build many business strategies on a common and simple one to maintain enterprise dynamic real time information system. The first business challenge can be described of having large number of instruments across the group; approximately 100,000. They have large amount of data; approximately 700,000 tags are logged in additional to all operating and equipment events gathered required for data validation, gross error detection and transformation into operating information. Mineral processing plants are a harsh environment for instruments. Certain instruments are critical to safety management. The quality of the data leads to better quality information. In summary, Anglo American required a mechanism to monitor the quality of the instruments/data and clean/reconstruct the data where practical.
With a traditional process information management system, information is gathered into a database and is disseminated as reports and on line inquiries to all requesters, and there the system’s responsibilities END. This system is passive; it is not designed for ACTION. A real-time information infrastructure allows users to apply their knowledge and expertise to find the root causes and continuously improve performance using operation states and event frames, Bascur & Kennedy, (2004). This presentation covers the use of the PI System, specifically the PI Asset Framework (PI AF) and PI Event Frames (PI EF), as the cornerstone of the Operational States Management solution created by Anglo Platinum. The original intent was to create a Downtime Reporting solution, but because of the features of PI AF and PI EF, the application was easily extended to cover any Operation State. Anglo Platinum created a front-end Silverlight Web application to view, search, and report on the event frames that are generated by the application. The benefits of this solution are time savings, standardization; event data can be displayed in many different ways, and can be extended to include new operational states just by configuring new elements in PI AF. 3. ANGLOAMERICAN PLATINUM CASE STUDY Anglo American Platinum is the world’s premier Platinum Group Metals (PGM) producer, supplying approximately 40% of the world’s newly refined Platinum. It has 14 concentrators, 3 Smelters, 1 Converter, 2 Refineries, 9 geographic operating areas. The Platinum process has a long value chain in comparison to most minerals. It is a technically complex and comparatively low volume but high value products. It has a significant material pipeline and Energy and water consumption intensive.
Fig. 1. Enterprise real time integration for operational business support This leads to the implementation of distributed information architecture, primarily due to limited network bandwidth as shown in Fig.1. Each operation has a complete PI System implementation. Key calculations are performed at the level they are used; primarily of the local sites. Selected data is rolled-up to a central Pi System. A master PI AF (Asset Framework) and replicated to site. Fig. 2 shows the PI System real time infrastructure and integration to other systems. The PI Central System is used as a Competence for Manufacturing Services. It is used for Group wide operational management analysis, single point of integration to certain business systems, maintain a standard operational metrics for all assets, industrial plants and chain supply analysis.
Anglo American Platinum has multiple energy sources: Electrical, Diesel and Steam from coal fired boilers. Electrical energy is the initial focus. Anglo Platinum is a large consumer of electricity. The electrical system in Republic of South Africa became constrained in year 2008 leading to nationwide load shedding. The electrical system is still constrained; this is likely to remain constrained for the next couple of years. 183
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
5.1 Gross Error Detection Methods for data validation for early diagnostics. The GED method detects data errors on simple metrics using simple, fast mathematical algorithms. No models are implied or used, therefore there is very little maintenance. The only model used in the standardized time based algorithm to estimate the time series evaluation of time derived metrics in the PI System subset of algorithms (Min, max, rate of change, mode, standard deviation range at a certain interval for the assets (Such the pulse on a human). Each data point can have a single state. The states are prioritized as shown in Fig. 4. The data is then classified into the following states: Missing Data, Not Running, High, Low, Not Updating, High rate of change, Simulated, BAD Data (Bascur, Hertler, Wong, (2011), Bascur, Linares and Schwenzer, (2008)). The bulk in the GED calculation is handled by a customer data reference and a formula data reference in PI AF. The services read the data from PI AF and write the results to a PI AF attribute. PI AF Seamless writes the results to PI. Communication with PI AF is via PI AF Software Development Kit (SDK).
Fig. 2: Real Time information infrastructure integration for analysis and collaboration A generalized plant model has been built with common way to provide context to the data. There are many context opportunities to be given to the data depending on the business needs. One of the best practices is the S95/S88 standards; however, we have found additional simple one, which makes the collaboration between the local plants and the strategic enterprise team much more effective to communicate improvements and provide easier diagnostics.
Fig. 4: Event data classification for transformation into validated information The benefits of an enterprise data validation and instrument monitoring and diagnosis are the key for implementing energy, asset monitoring, environmental and safety monitoring at the enterprise. At the local level, improve production, quality, and costs reductions. These events are then used to perform analysis using Business Intelligent tools using Real Time Data from the operations at the original resolution. As such, the system enables to detect all type of losses which can be easily attributed to the operating or equipment states. This is the KEY objective of using the PI System tools and algorithms empowering the transformation of raw data into business information.
Fig. 3. Data validation strategies The Common Structure Models are consistent across all sites. The object structures are maintained on the central PI System and are replicated to the sites. One of the main preliminary activities conducted for the deployment of the Enterprise PI System was to standardize in the data validation methods. There are benefits and maintenance issues, depending on the amount of complexity. The traditional data validation methods can be classified according to the degree of maintenance required; these are gross error detection, filters, statistical methods and model based data reconciliation as shown in Figure 3. These methods are not mutually exclusive. Gross error detection (GED) is normally the basis of all data validation techniques (Bascur et.al 2011). The cost of implementation increases with the complexity.
The standard GED implementation contributes to a broader vision, supports the “One version of truth, better quality of information and most importantly a standardize method for alerting, predictive alerts and overall continuous improvements and diagnosis for optimization. The solution makes the instruments availabilities unequivocal. Faulty and simulated instruments have resulted in serious equipment failures in the past. The visibility highlights these issues in proactive manner avoiding costly stoppages and repairs. Fig. 5 shows an example of the analysis using Real Time Data and Operating and Equipment events.
The more sophisticated techniques are typically reserved for a small collection of instruments/data. GED can be applied with minimum effort to a variety of cases.
184
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Fig. 6: Overall Energy Consumption and specific analysis for the 80/20 actionable report. The key to Energy reduction has been empowering people to act with information. The most important factors are: Present the information in an easy to understand manner, develop high level metrics linked to production and raw material resources. “You cannot control what don’t measure and UNDERSTAND” The Understand is the value of implementing a common currency scale model (PI AF Context and transformation of time of derived metrics) for actionable information. This strategic implementation makes the difference of just providing data out of context and not scaled properly. Now, people can assign value to the performance metrics for effectiveness integrating asset availability, environmental impact, energy and water specific consumption all process plants, areas based on type of products. As such, clearly identifying products losses, resources consumption and improving process safety. This is what has been called by a strategic sustainable environment by Michael Porter, 2011.
Fig. 5. Two view of the Gross Error Detection and Data Validation example. 5. REAL TIME ENERGY MANAGEMENT AT THE ENTERPRISE LEVEL. A companywide integrated approach to energy savings is required. The necessary information to develop these plans is becoming available only now. This approach requires both a new strategy in building a reliable multi-functional systems as well as a change the operational culture required for continuous collaboration at the local and enterprise strategic organization and systems. It is human behavioural change as well as a new computer and software integration (Halhead, (2011)).
The granular break down of the power consumption in an easy format makes it possible to take action, to evaluate very quickly the 80/20 Paretto analysis to get the problem very fast rather than wasting hours in finding the 20% important things to do that will contribute to the 80% of the savings. As such, the implementation of a continuous improvement and strategic innovation has proven to be the WHAT, WHERE and HOW to work with today’s technologies and collaboration at the LOCAL and Strategic teams. Figure 6 shows to the whole enterprise the online interactive online reports using Microsoft SharePoint 2010 with Excel Services using PowerPivot and the PI Webparts.
A central energy measurement and management sub system (CEMMS) was installed in 2009/2010. Now, Anglo Platinum has online measurements of its power consumption. Top level power consumption has been available but lacks granularity for costs avoidance and losses of energy. The precious metal refinery was used as a pilot site. This subsystem was integrated to the WAN and it was done with as fault tolerant system using standard interface for better support and NOC monitoring. The PI AF is used to provide the connections of validated information into the right context to analyse the information in multiple ways. Think of a real-time multidimensional data cube where the organized real time information by UNITS and AREAS can be filtered by operational states, equipment operation states, type of products and raw materials for any time and for one plant or many plants.
As such, actionable information becomes available to define the next step for root cause analysis or for the strategic team to make an economical assessment of the big picture (Energy Management, Environmental Management, Asset Optimization, Training, and Collaboration). The real specific energy consumption is commonly evaluated to look for improvements in the short term. Using this approach PI Notifications is used to proactively alert if getting closer to minimum or maximum constraint based on the real time
185
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
calculations of rate of change, min, max, totals, standard deviation, mode and averages.
Fig. 8 Daily energy consumption analysis by process unit. Despite the advances in automatic data collection and archiving, business decision makers face the problem of exploiting the information that is relevant for plant operations and the sustainability the business enterprise. Providing the data at the original resolution and add-in GED using a Model Tool to add context to the data based on event and Enterprise Best Practices based on a common currency state makes the main difference. This novel strategy improves collaboration and gives real time feedback on decisions made at all levels in the organization. The efficient use of water, energy, and resources is critical and the best approach is to have the infrastructure in place to be able to conduct small focused projects, collaborate between different teams in the organization, and understand that this is a continuous improvement process. Off the shelf technologies are available today to optimize the business while dealing with water and environmental constraints. They generate high quality performance information from process data. The synergy of combining process data with transactional data provides a deeper understanding of the data for continuous improvement and innovation.
Fig. 7 Graphical Analysis of the Energy relative the Enterprise Basis. Figures 6, 7 and 8 show some the multiple types of Energy Analysis enabled by the PI System at the Enterprise. As such, Anglo American Platinum (Amplats) is geared to meet their objective to reduce their Specific Energy Consumption by continuous improvements and innovations which are critically identified by collaboration at the local and enterprise levels of the organization. The integration of all sites and all type of relevant information with event data classification is a must to build a solid robust sustainable operation management strategy.
With this knowledge and examples provided, opportunities for better water and environmental management must be realized in the mining and metal industry to ensure long term sustainable business. Collaboration within and between businesses, government, and citizens to achieve cleaner mining practices is a necessity to maintain and to improve the overall quality of life. The OSIsoft Enterprise Agreement (EA) allowed for the deployment of a consistent PI System infrastructure across the enterprise. Anglo American Platinum uses a variety of OSIsoft products including: PI Server, PI ACE, PI AF, PI DataLink, PI DataLink for Excel Services, PI ProcessBook, PI WebParts, and PI OLEDB Enterprise.
6. CONCLUSIONS One major challenge to better sustainability is the need for collaborative, enterprise data management of real-time and historical data not only across an organization, but also between businesses such as a mining company and the local water utility. Easily accessible real-time data and information is a key enabler to decision making, optimization and sustainability.
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