Precognize Predictive Analytics Addresses Maintenance and Environment Issues

Written by Valentijn de Leeuw, Senior VP at ARC Advisory group. Valentijn’s responsibilities include research and consulting in the process industries, with a focus on clients in Europe, the Middle East, and Africa.

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June 8, 2018

Summary

A well-designed and executed asset management strategy can lead to significantly better reliability and lower maintenance cost. Such a strategy requires accurate and up-to-date asset and maintenance information to set priorities right. It also includes the maximization of condition-based and predictive maintenance while keeping reactive maintenance to a minimum. There are many approaches to predictive maintenance. To get started with predictive maintenance in process plants, a technique capable of monitoring large numbers of tags, and learning based on historical data covers the need of monitoring the full plant.

Precognize proposes such a solution. Its technology combines machine learning determining normal and abnormal behavior, augmented with a combined asset and process model that has information about cause-and-effect relationships among variables, requiring minimum effort of the plant operator to build. This technique enables minimizing false positives. Carmel Olefins, part of the Bazan Group, recently testified about their use of the solution in a polyolefins plant, and how this helped the company address environmental issues. The company found that the tool supports their operations team in detecting operational issues in the “darkest corners” of the plant, where “it hurts the most.” The solution detected a retrospective valve issue that had resulted in negative publicity due to the environmental impact; and could also provide advance warnings for new valve issues. Since they began using the software, the company gets only a few alerts per day and has not experienced many false positives.

Effective Asset Management Strategies

According to Laurens and van der Molen world class oil and gas producers have 95 to 98 percent availability (even in older facilities), while their maintenance cost is 30 percent less than average. This availability, compared at the time to 85 percent for the average producer, and to less than 75 percent for a poor performer. Also, the safety incident rate is 30 percent lower than average among the leaders. Apparently, these companies make the right decisions on savings and investments. For example, the value of their small-modification project portfolio can be up to 50 percent higher than that of average operators. Furthermore, those operators apply some form of asset maintenance excellence including a regular review of their policies and nurture a culture of continuous elimination of all sources of losses. Seventy percent of the maintenance jobs in the leaders group are of preventative nature. These companies optimize preventive and condition-based maintenance of their critical equipment, while minimizing additional maintenance for less-critical systems. Another finding was that these companies have good planning and scheduling processes in place that optimize the use of their maintenance resources, tool allocation, and flawless process execution. Work that needs to be executed during stops, is particularly well planned and re-planned at well-defined intervals of 90, 28 and 7 days before execution of the plan.

The numbers may have shifted a little over time, and they may be a little different for downstream processing, but the features of this benchmark in maintenance strategy and execution remains valid. Whatever the methodology used, Reliability Centered Maintenance (RCM), or Operational Excellence or World Class Manufacturing, possibly in combination with an asset management standard such as ISO 55000, the point is that an asset management strategy is required to set priorities and devise a fit-for-purpose approach adapted to each priority.

Asset and Maintenance Data Availability, Quality, and Integrity Required

To decide upon asset management priorities, data of good quality are required that are ideally easy to access. This includes statistics on the number of maintenance interventions, the root causes of equipment failures, the cost of repair, the overall equipment efficiency (OEE) broken down into unwanted stops, planned stops, throughput reductions, and quality losses. Very important are also the cost of production losses related to unwanted stops. This will inform the operator how much effort and resources he is spending, where to focus, and which goals to define. A quantified business case could be additional help in deciding where to focus efforts and optimize for return on investment. Even if the quality, availability and integrity of asset information is not optimal, an operator should engage in improving and optimizing maintenance. However, the operator should be aware that the preliminary effort would be higher and the time to results longer.

Fit-for-purpose Asset Management Approaches

Once the owner-operator knows his asset priorities, he can prioritize the maintenance efforts, also referred to as “fit-for-purpose.” Since the majority of equipment fails randomly, preventative maintenance that is based on regular inspections, replacements and maintenance corresponds to a higher workload and cost than reactive maintenance and does not necessarily improve reliability significantly. For lesser critical equipment, for example a lawn mower – a run-to-failure approach (reactive maintenance) may be most economical. Workload and cost can be reduced while preserving reliability by using condition-based maintenance, based on instrumented monitoring of a few key variables and attributes of the equipment. Only when the trends indicate that maintenance is necessary, it will be planned and executed. Often this leads to longer average time between maintenance intervention, but the approach can also determine if maintenance intervals need to be shortened to avoid damage or impact on the process. This can be useful for slowly degrading equipment performance as in the case of clogging of filters and fouling of heat exchangers that depend on the processing conditions and much less on elapsed time. Condition-based maintenance, because it reflects the actual state of the equipment, increases reliability and at the same reduces cost. In these examples, condition-based maintenance includes a simple evaluation by maintenance personnel. In the case of predictive maintenance, the evaluation of the equipment condition and the prediction of a potential failure or issue is made by mathematical methods and algorithms, also called predictive asset analytics.

Choosing an Asset Analytics Approach

In some cases, deviations are only relevant in combination with deviation of other variables. In continuous or batch processing, relationships between variables are very common, and find their origin in the physics and chemistry governing the process. Many can be modeled accurately using scientific and engineering approaches, as is done in process simulation. Simulations can be made for steady-state or transient conditions and can be of great value in process design and analysis. Used in combination with historical data, these tools can help explain relationships that led to unexpected events, design process management measures to avoid these; and when used online, they can help making operating decisions. However, detecting a small deviation that could lead to important consequences would imply high-fidelity simulations, which are extremely costly and resource consuming to build. So, for discovery and predictions, another approach is needed.

Where to Start

Especially when starting to apply asset analytics, and for large processing plants such as in refining, petrochemicals, steel or paper production, with thousands of tags, measurements, possibly augmented with IoT sensors, the task of setting up monitoring of critical equipment can be daunting, let alone monitoring the information and setting up predictive analytics. In such cases, an approach that is capable of monitoring these large numbers of equipment and detecting the most important deviations, creating few false positives, and requiring a relatively small investment in terms of resources would be needed. For any analysis to give meaningful results, the quality and the integrity of operational data quality used is crucial. Data must be screened and cleaned.

Precognize’s Predictive Maintenance Solution

The information is internally converted into graphs that have well-specified mathematical properties and are inputs for the graph analysis engine. Based on the know-how from operations, the engine is capable of scoring the relevance of the detected abnormality, taking its evolution into account, propose a root cause, and display a suggestion for intervention. The model can further be fine-tuned by the user, for example by manually specifying a root cause, thereby adjusting the model. The solution runs in the Precognize or the client’s private cloud on a virtual or physical machine with 32 Mb RAM and 12 cores. Users access the system using their standard browsers.

Addressing Environmental Concerns with Predictive Maintenance

Carmel Olefins reported recently at ARC’s European Industry Forum about its experience with the software. The company is part of the Bazan Group, and produces polyolefins – low density polyethylene (LDPE) and polypropylene (PP) – in the bay of Haifa, Israel, a densely populated area. Currently, in Israel there is no anti-flaring regulation. Flaring is used by Carmel Olefins as a way to safely discharge light hydrocarbons in the case of over pressure. On September 1st, 2017, a huge flare started burning 40 tons of hydrocarbons per hour. Regulation and public opinion are different aspects, and within five minutes the company was in the news for all the wrong reasons. Half an hour later a faulty valve was identified as the cause of the incident. The company decided to “go digital” and apply predictive analytics to reduce, and if possible, avoid such incidents. It selected Precognize software, because it can cover the full plant and can be implemented quickly. Moreover, the company found Precognize is responsive to demands, and it is used by BASF, which is a reliable reference.

A post event analysis of the flaring showed that Precognize could have detected the valve issue three weeks before the incident. After the software application, Carmel Olefins has only a few alerts per day, and experiences only few false positives preserving their maintenance efficiency. The company recommends starting “where it hurts the most,” that is, where risks and impacts are most severe, and to make the production manager with the shift supervisor of the unit responsible for the outcome. The challenges are to change the mindset and attitude of production personnel from “it is broken, let’s fix it,” to “there is something strange here, let’s investigate it.”

A real-time issue provided the opportunity to inculcate new habits. A temperature rise of the contents of a pipe – that depends on a steam valve opening, seemed to have occurred without the valve actuator moving. The problem was detected by Precognize and was not complex to diagnose: the valve did not close properly despite its setpoint and the leaking steam caused the temperature rise. The valve could be repaired before an incident or further damage occurred.

Carmel Olefins recommends using predictive analytics to put a spotlight on the “darkest corners” of the process that remain invisible through human observation of trends. At Carmel, full transparency is applied in the sense that analytics results are visible throughout the ranks, including upper management, although only production personnel and shift managers act on the signals from the software.

Conclusion

ARC recommends elevating asset management to the strategic level because of the importance of its impact on key enterprise KPI’s, such as bottom line, environmental footprint and process safety. This strategy will provide the governance and the management system, enabling a maintenance strategy. The latter depends on the quality of asset information. A maintenance strategy should focus on high priority equipment and processes, maximizing condition-based and predictive maintenance and minimizing preventative maintenance. Predictive analytics are a key technology enabler for predictive maintenance. The operational data used to build models to detect anomalies must be of adequate quality.

The Precognize predictive maintenance solution is helpful to reliably detect operational issues in large process plants with large number of tags, with a limited effort from the plant operator in the configuration phase. The involvement of operations personnel is essential, and the transformation of their habits from a break-and-fix to one of investigation of alerts and preventing incidents and damage may need management attention.

For further information or to provide feedback on this article, please contact your account manager or the author at VdeLeeuw@arcweb.com. ARC Views are published and copyrighted by ARC Advisory Group. The information is proprietary to ARC and no part of it may be reproduced without prior permission from ARC.