Digital Transformation in 2024: A Guide

By Jonas Berge
March 01, 2024
InTech Magazine
Feature

Summary

Transformation of work and new automation approaches improve reliability, sustainability, safety and productivity. This feature originally appeared in the February 2024 issue of InTech digital magazine.

Imagine an intelligent plant, with no surprise equipment failures and no manual data collection–and how it could improve reliability. Imagine, no hidden equipment fouling and no undetected flaring–and what it could mean for sustainability. Imagine, no surprise loss-of-containment and no undetected leaks or spills–and how it could enhance safety. Finally, imagine no manual valves in the wrong position and no non-uniform temperature profiles–and how it could increase production. This is the vision of efficiency and performance that digital transformation promises for process plants. Since automation technology is essential to any digital transformation strategy, automation software and hardware selection is one of the most important decisions a plant can make.

Why: Plant operational challenges

First, to set some context, let’s outline the operational challenges that drive change in most plants today. Broadly speaking, these challenges fall into four operational domains: safety, sustainability, reliability and production. Inefficiencies in these areas are often linked to manual tasks, like data collection and interpretation.

Manual data collection, by reading mechanical gauges or using portable testing devices, is typically done too infrequently to predict problems and is very labor intensive. In some cases there may not be sufficient expertise on-site to interpret the collected data. This can cause a host of problems, including health, safety and environmental (HSE) incidents, over-consumption, flaring, emissions, equipment failure, process downtime, loss of containment and off-spec products.

How: Transformation of work

Plants can overcome these challenges by transforming how work is done across the major operational domains (Table 1) to become more situationally aware, more responsive, more predictive and more productive. When plant managers set aspirational goals like "zero" incidents, "zero" emissions and "zero" downtime, digital automation tools, including threat monitoring, performance monitoring, condition monitoring, process monitoring and remote valve control, make it possible to either meet them all or come very close.

Top-performing plants do this by automating data collection with sensors, automating data interpretation with analytics in artificial intelligence (AI) apps and automating workflow with ERP integration and alarms.

Table 1: Common digital automation use-cases

Health, Safety and Environment	Emergency safety shower and eyewash stations Manual valves Flammable gas Toxic gas Tank overfill Breather valve and blanketing Pipe corrosion and erosion And getting people out of harm’s way
Sustainability, Energy Efficiency and Emissions	Steam trap blowing steam or trapping condensate Pressure relief valve safety release and internal passing Air-cooled heat exchanger fouling Cooling tower fouling Heat exchangers fouling Other over-consumption and losses Methane leaks Lighting power consumption
Reliability, Maintenance and Integrity	Pumps Air-cooled heat exchangers Cooling towers Pipe corrosion and erosion Inspection rounds Control valves Heat trace system Breather valves
Production and Quality	Field operator rounds Offsite tank farm storage tanks Wellhead and control panel Temperature profile Local control panels Offsite standby pumps Consumables inventory Manual valves

What: A new approach to automation

Many leading companies are deploying a new approach to plant automation that incorporates the latest advances in analytics, sensors, I/O schemes, wireless networking and edge computing to drive top-quartile performance. But the devil is in the details, so let’s look at some detailed recommendations for realizing this new type of digital transformation strategy that can be done fast and with low risk.

AI Analytics. Attempting to code custom software applications for analytics purposes, or paying a contractor to do it, is more difficult than one might think; it takes time and can be very costly because a business must usually pay for the entire development. Ready-made apps (Figure 1), on the other hand, involve no programming, no testing, no development cost, no delay and no pilot proof of concept. Ready-made apps are already widely in use, so they already offer a proven track record. Most have a rich feature set based on inputs from thousands of other users and so are more capable than custom-coded applications.

Figure 1: Examples of ready-made apps for common process plant use cases.

Maintenance and reliability engineers are typically not data scientists, so it isn’t wise to expose them to data science. It’s better to use analytics that require no historical data import, no data cleansing, no algorithm selection, nor any training runs or testing iterations, and do not use unfamiliar terminology. Such industrial analytics tools are much easier to use and require minimal upkeep, which is vital as talent is hard to come by.

An all-purpose data analytics app would furthermore require lots of customization for industrial use cases. Analytics software specialized for use cases in process plants offer domain-specific features, such as relevant dashboards, detail visualization capabilities and terminology (Figure 2), are easier to use and more capable.

Figure 2: Specialized apps for each domain and task.

Machine learning (ML) is one form of artificial intelligence (AI) that receives a lot of attention as of late, but it is not always the best form of analytics for all use cases (Figure 3). Many top-performing plants today instead employ engineered analytics with mechanistic AI for equipment and processes with well-known causes and effects and first principles. Mechanistic AI is deterministic, so very robust, and it is verifiable. Deep learning (DL), on the other hand, is used in non-process use-cases like image and speech recognition.

Figure 3: There are many forms of AI, only some of which are ideal for manufacturing.

While they are ideal for simulation, design and training, it’s often not necessary to build models or digital twins to predict problems. Condition-based analytics tools use "agents" designed to recognize patterns or detect instability and are easier and less costly to deploy and maintain (Figure 4).

Figure 4: Cause and effect relationships are built into software "agents."

Enterprise resource planning (ERP) systems handle business processes like accounts and inventory, but will not identify where there are utility leaks, which manual valves are in the wrong position, which heat exchanger is fouling, which process unit is about to have an upset, which pump is about to cavitate, etc. Instead, top-performing plants use specialized operations management automation systems consisting of software and sensors for energy management, condition monitoring and performance monitoring. These technologies use real-time data to provide notifications that pinpoint where in the plant the problem is (Figure 5).

Figure 5: Operations management software for multiple use-cases.

System Architecture. In these cases, cloud computing is optional. But if a plant does use the cloud, note that putting business administration and plant automation data in the same cloud instance would make security harder to manage. Many plant managers today use an independent cloud instance for plant data and apps as a cybersecurity zone along the lines of the IEC 62443 standard. Some data is passed between the office administration systems and plant automation systems as part of the automatic workflow. For instance, pump analytics notifies the ERP/CMMS when a pump problem is predicted. Dashboards can show both business and plant data.

IIoT/M+O Sensors. The familiar refrain, “You already have all the data,” is often not correct. Plants have lots of data, but it is mostly process data. Many plants don’t have enough real-time equipment data, because today that is collected manually using portable testers and reading gauges. The goal, therefore, is to automate that data collection by installing permanent sensors.

Figure 6: Various types of wireless sensors can take the place of manual data collection.

It would be impractical to wire hundreds or thousands of additional sensors in an operating plant, and it would be impractical to cut, drill, or weld hundreds or thousands of additional process connections while the process is running. Instead, top-performing plants use advanced sensors that are wireless and non-intrusive and that bolt onto the outside of the equipment, clamp-on to the outside of the pipe, slip between existing flanges, or reuse existing process connections. These may be referred to as industrial internet of things (IIoT) or monitoring and optimization (M+O) sensors. Replacing mechanical pressure gauges with wireless pressure gauges, for example, is relatively easy (Figure 6).

Wireless Technology. Existing systems and devices in most plants like programmable logic controllers (PLC), distributed control systems (DCS) and vibration monitoring systems use Modbus and HART protocols. The easiest way to integrate these systems into new software and systems is to use their native protocols. Therefore, it’s best to use systems and software that support HART-IP and Modbus/TCP (Figure 7). This way no drivers need to be written or tested, and more data is transferred making the system more capable.

Figure 7: Industrial standard protocols and interfaces.

Hiring a developer to code software interfaces to custom APIs often results in costly lock-ins. This can be avoided by using systems and software with standard IEC62541 (OPC-UA) software interfaces. OPC-UA is widely supported in many edge devices, in all modern automation systems, in a large number of apps for all kinds of process automation functions, and in all modern data management platforms. No APIs need to be written or tested. OPC-UA also supports metadata for richer display, automatic server discovery and a structured information model (IM) for easy browsing of data. An operator can both read and write data, and the protocol is supported in all major cloud platforms. Lastly, OPC-UA provides transparent integration with older systems using OPC Classic.

Wireless sensor networking technologies originally designed for smart cities or agriculture do not integrate well with plant automation systems. Instead, most plants use standard IEC62591 (WirelessHART) wireless sensor networks. WirelessHART enables automatic data conversion to OPC-UA, HART-IP, Modbus and other industrial protocols without custom coding/programming to a non-standard API or scripting to parse vendor-specific data formats. This way data is easily integrated, and no sensor data is left behind. WirelessHART enables centralized sensor management such as configuration of remote sensors, and a single common app can be used for diagnostics of all sensors from multiple vendors. Other wireless technologies cannot achieve the same result.

As a result of this new approach to automation and transforming work, plants can operate safer, greener, longer and faster (Table 2).

Table 2: Operational excellence thanks to the new automation

Safety, Health and Environment	Fewer injuries Fewer incidents Reduced clean-up cost Reduced fines
Sustainability, Energy Efficiency and Emissions	Reduced energy consumption and loss Reduced energy cost Reduced emissions
Reliability, Maintenance and Integrity	Reduced downtime Reduced loss of containment Lower maintenance cost
Production and Quality	Reduced off-spec product Greater throughput Improved yield Fewer site visits

_{This feature originally appeared in the February 2024 issue of InTech digital magazine.}

About The Author

Jonas Berge is an ISA Fellow and the senior director of Applied Technology at Emerson in Singapore. He is a trusted advisor for plants and EPCs to adopt new technologies moving the industry forward with digital transformation. He has more than 30 years of experience in the field of industrial automation. Berge is a subject matter expert (SME) in digital transformation (DX)/Industrie 4.0 including data management, analytics, wireless sensors and the Industrial Internet of Things (IIoT) with particular emphasis on sustainability and decarbonization. Berge is the author of two books and has contributed to several others. He is frequently featured in articles and white papers and is a well-known speaker and panelist. He has also authored a standard and holds patents in safety communications.

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