W hile the Industrial Internet of Things (IIoT) and cloud computing are relatively new capabilities to support asset management, within processing and refining plants there is

a mature and successful installed base of legacy digital industrial technology that has served critical machine assets and reliability-centred maintenance/condition-based maintenance (RCM/CBM) strategies for decades. Given the level of existing digital footprint it may be tempting to dismiss the current excitement and believe that the capabilities of an existing asset performance management (APM) system are sufficient. However, to achieve peak asset performance and maximise machinery uptime, hydrocarbon processing plants and refineries should consider the step change in capabilities that a cloud-based APM implementation provides for driving greater enterprise asset management.

The industrial technology serving hydrocarbon processing and refining facilities has evolved tremendously during the past 20 years; yet even two decades ago technology was patently remarkable. In retrospect, as the onset of the 21st Century neared, fast microprocessor-based control and automation systems were relatively ubiquitous. Data historians had deep capacity and networked seamlessly across a plant. PCs and data servers were being commoditised and included well-evolved, configurable productivity and condition monitoring software. Furthermore, on machinery upstream of this remarkable data engine was a sea of precision sensors: ultrasonic flowmeters, contactless vibration probes, mobile equipment monitors, pressure transducers and accelerometers, and infrared cameras. And this only scratches the surface of machinery control, automation and monitoring technology available to plant operators and reliability professionals in the previous century.

The Y2K came and went, and the dot-com bubble deflated, but some new internet technologies belied the hype and emerged to deliver demonstrable value-adding functionality. Those were incorporated into the remarkable data engine, too. By borrowing from rapid advances in cellular technology, 3G spread-spectrum wireless networks

and industrial sensors facilitated reach to difficult asset locations and stretched monitoring distances to new boundaries. Accordingly, with this remarkable kit, sales engineers and business developers started to promote their wares and promised to connect machines, people and enterprise locations to deliver, among other things, asset performance insight, failure prediction, greater operational efficiency and actionable information.

This may sound familiar to veteran reliability professionals, and most likely they agree that the technology served them well. Indeed, the continued evolution of industrial APM systems has advanced present-day systems significantly further, and combined with computerised maintenance management system (CMMS) software and modern RCM/CBM processes the value provided by installed technology has risen to even greater levels. Therefore, today’s plant directors, managers, and reliability and maintenance personnel might ask: what does a digital transformation look like for a modern hydrocarbon processing plant or refinery that appears digitally mature already? And, if greater maturity is obtainable, how does a plant get there?

Drivers of digitalisationBefore getting to answers, consider the following four drivers of the current technological push.

Newest mature technologiesCloud computing and machine learning analytics are now quite mature following a decade of intense development, application, and demonstrable results. Akin to how the millennial internet boom and cellular advances augmented APM systems previously, today’s Big Data analytics – evident in Amazon or Google consumer predictions – can augment APM systems as well. This, among other advances, provides the foundation for a step change in predictive asset management. By aggregating all plant data sources into a single cloud service, today’s technology transforms asset monitoring from a collection of discrete and disjointed data collection systems into an integrated asset performance and data analysis engine, which truly provides

Ben Berwick, Honeywell Connected Plant, USA, talks through methods of digital transformation for the digitally mature refinery.

Reprinted from January 2019 HYDROCARBON ENGINEERING

the actionable information long promised – yet scarcely delivered – by previous solutions.

Corporate financial performanceFollowing decades of pressure to get lean and drive continuous improvement, it is harder to generate incremental productivity gains year over year. Consequently, advancing over one’s competitors requires a higher sophistication of performance measurement and process improvement. One relatively untapped productivity improvement exists in predictive machinery management, which historically could be viewed as impractical, too expensive, or simply too difficult to fully understand. Current cloud APM technology provides the capabilities to address such constraints while providing a next logical step towards edging out competitors.

The great crew changeWhile not a new concern, what felt like an idle threat two decades ago is now a reality: the great crew change

has arrived. In the current landscape both the availability of human talent and average employee tenure are declining. Meanwhile the depth of expertise and the spectrum of skillsets required for downstream processing facilities are increasing. As decades of experience retire and there is only an inadequate supply of asset subject matter experts available to backfill their vacancies, today’s APM digital technologies are demonstrably adept at augmenting human analytical capabilities. It is not intended to replace them; instead, the analytic and performance models provided by cloud computing deliver vetted alerts to help SMEs pinpoint root causes sooner and more accurately. Likewise, the cloud environment offers a centralised view of an asset enterprise, easier configurability, and access to productivity tools that increase the volume of manageable assets by a single individual.

Maximising digital assetsClearly the objective of any asset management system is to maximise return on all assets, including the electronic systems controlling and monitoring the traditional rotating assets. Within these electronic systems there are likely immeasurable terabytes of data that were collected yet never analysed, configuration methods that are cumbersome and differ widely, and user training requirements that are very difficult or impossible to manage effectively. Additional value is derived by connecting these systems to a common cloud user interface and, through use of the cloud data lake, to analyse data in its entirety, resulting in maximisation of both installed rotating and installed digital assets.

With consideration of these four factors, describing what a digital transformation looks like at a modern plant – one with a high level of digital maturity already – involves creating a system out of the existing systems by aggregating all data sources and applying modern analysis on the unified dataset. Utilising cloud computing, analysis is performed via first principles performance models, machine learning analytics, big data mining, and statistical analysis – with the output being delivered to personnel across the reliability and maintenance value chain.

Achieving greater maturityHaving recognised that greater maturity is obtainable, the real challenge is seeing a way there. There are several aspects to consider in realising greater effectiveness of an existing APM system and augmenting the digital systems underlying it. These include an in-depth strategy developed in cooperation between operations, maintenance and IT departments, who must consider the strengths and weaknesses of the current APM system and asset management processes, as well as linking APM with CMMS and enterprise resource planning systems. Such depth regarding strategy is better discussed separately. Leveraging existing digital equipment to greater levels of functionality will hinge heavily on which APM platform is selected to supply the cloud-based APM system. For a macro view of APM strategy development, and a cursory examination of platform vendors, a recommended supplementary article is ‘Asset Performance Management (APM) 4.0 – Platform Vendors’.1

Figure 1. Digital transformation in mature refining and petrochemical facilities is less of a transformation; it is a digital augmentation of installed digital systems with modern digital capabilities.

Figure 2. At the core of modern asset management is the intersection of APM with mature cloud analytics – improving feedback between man and machine and interaction among colleagues.

Reprinted from January 2019HYDROCARBON ENGINEERING

The guide presents a primer to the APM 4.0 strategy and key factors to consider in platform selection.

The following additional key factors may also be considered in platform selection.

Time to valuePlatform selection should give considerable attention to the platform’s ability to address integration complexity and aggregate data in a time-effective manner. Vendors that can demonstrate their techniques and prove expeditious aggregation capability will ensure a smooth transformation to the cloud. A plant may easily have tens of thousands of points and control tags that must be mapped to the cloud digital twin. It is not a trivial task and can be the most time-consuming element of a transformation. Whether contracting the platform vendor to perform aggregation and tag mapping, or choosing to keep it in-house, it is critical to ensure that aggregation activities do not overly consume resources or jeopardise the targeted timeframe to realise results. The platform’s effectiveness in data aggregation will dictate this.

Likewise, model building is time-consuming and has proven to be a nonstarter for asset model adoption for many plants, so template libraries, cloud connectors, and context-aware aggregators available from a platform will improve the initial engineering productivity and aid in model sustaining and evolution over the system’s lifecycle. The maturity and depth of libraries and connectors will usually dictate a platform’s ability to address a wide variety of digital platforms, software and connected machines within the plant. Likewise, the depth of libraries and connectors will likely be in proportion to the platform vendor’s experience within the refining and processing industry, as many models are evolutionary from past implementation of automation, control and condition monitoring systems, as well as previous modelling and simulation of such systems.

AnalyticsPlatform vendors may incorporate their own analytic capabilities and machine learning engines or have integrated third-party solutions (or some combination). Regardless, a vendor’s analytic capabilities should address three areas:

n Descriptive analytics, or ‘what has happened?’, through information derived from aggregating multiple plant data sources and mining to provide insight on past performance.

n Predictive analytics, or ‘what could happen?’, through statistical models and forecast techniques to gauge the future.

n Prescriptive analytics, or ‘what should be done?’, through optimisation and simulation algorithms to advise on possible outcomes given theoretical inputs.

Performance modellingWhile cloud analytics provide a newer means for deriving greater asset insight through statistical analysis, the traditional first principles performance models remain a core element of a comprehensive APM system.

First principles (i.e. formulae and equations derived from the laws of physics) have and will continue to be successful in machinery performance assessment. However, such modelling techniques require extensive experience and skills with appropriate calibration techniques. A vendor’s experience with process industry compressors, turbines, pumps, etc. and associated processes is imperative for successfully calculating the performance efficiency of a machine, its energy usage relative to optimal, and timeframe to next maintenance or overhaul. Any vendor who claims that only statistical analytics are required may be avoiding the challenges in understanding the physics or may lack the industrial experience to apply it successfully.

CybersecurityIndustrial machine network hacks are growing in frequency. With the increased connectivity, network breadth and direct connection to machine control systems, a cloud APM solution must demonstrate an imperviousness to a breadth of cyberattack methods from global sources. A platform vendor’s cybersecurity solution should have thousands of demonstrable installations defending the availability, reliability and safety of industrial control systems (ICS) and plant operations.

Conventional security fails to protect against proliferating cyberthreats to both OT and IT systems. ICS on OT networks have different operational requirements that impact the ability to adapt and respond to new cyberthreats and reveal new avenues for cyberattack. The APM digital platform must be specifically designed with asset and operational requirements in mind, while protecting critical processes without negatively impacting efficiency or safety.

ConclusionThe current wave of excitement over new digital solutions for industrial assets and APM may represent the highest level of push marketing in a long time, but it is certainly not the first. As with previous waves, all that it promises must pass the test of time, yet it remains patently clear that new and value-adding functionality is here to stay – regardless of the level of digitisation and RCM/CBM existing in a plant already. Indeed, the current stage of APM evolution is following its traditional, remarkable path. However, presently it happens to be intersecting with the evolution of cloud computing analytics as well. This is significant and to harness the benefits a plant should choose its digital transformation partner carefully. The vendor should have extensive experience in the digital systems, refining processes and rotating equipment of the plant, and the chosen platform must leverage the best of cloud-based analytics, productivity and connectivity capabilities, but also be implementable in an efficient and minimally invasive manner with ongoing operations.

Reference1. MIKLOVIC, D., ‘Asset Performance Management (APM) 4.0 –

Platform Vendors’, Solution Selection Guide, LNS Research (2018).