Emerging Technologies in Process Industries
Published on : Saturday 04-12-2021
The process industry is an early adopter of automation in its operations, but is it slow in adopting digital transformation?
Like all other businesses, the process industry too face pulls and pressures meeting production targets, minimising costs and maximising production, while maintaining quality, besides conforming to relevant safety regulations and following environmental regulations. Today, digital transformation is seen as a solution to address many of the issues, especially with many emerging technologies providing better insights into the various functions, and more effective tools for automation and control. But the process industry has used automation in its operations for over 60 years now. In fact when the concept of Industry 4.0 was launched at Hannover in 2012, the process industry was largely unimpressed. It was seen as something that had more relevance in discrete manufacturing as the process industry in general was way ahead on the automation curve. So is the process industry perceived to be slower in adopting digital transformation compared to discrete manufacturing?
“It's a bit of an apples-to-oranges comparison. The process industry had a 30-year head start on digital transformation, because the industry is inherently so complex that it required sensors and control systems from the beginning. It's not like assembling a car, which you can do without many sensors,” says Berk Birand, Founder and CEO, Fero Labs, a company that brings actionable machine learning to the industrial sector. “Today, discrete manufacturers have caught up and now use a lot of cutting-edge technologies, like tracking goods inside the factory wirelessly, or using computer vision to test quality. So in some ways, they've gained the advantage. But now process industries have the chance to get ahead again by adopting machine learning solutions, which enable them to optimise jointly for sustainability and profitability – the two key challenges manufacturers currently face,” he adds.
“Digital transformation is a fairly well understood term these days. It has taken root in many industries. However, different functions in an organisation have been moving on at different speeds. Similarly, the speed of adoption is observed to be strongly dependant on the size of the organisation,” says PV Sivaram, Evangelist for Digital Transformation and Industrial Automation. Backed by his over 4 decades of industry experience in organisations like BARC, Siemens and B&R Automation, Sivaram has experience spanning the user industry as well as two global vendors. He draws attention to the fact that the degree of instrumentation in a process plant is high. This comes from a reason that these plants have always needed automatic controls, even before the advent of electronics. Where there is instrumentation, there is data. Data is the primary requirement for digitalisation. Thus, the prerequisites of digital transformation are very much present. “The desired outcome of digitalisation – to achieve higher efficiency in manufacturing by tight process control, reduction of wastage, improvement in OEE – these are already well entrenched, and therefore not a novelty for a process industry. Digitalisation can best help them in getting more customer centricity. The approach here is quite common to all industries, and therefore there is not such a big contrast between continuous process industry and discrete manufacturing,” he elaborates.
According to Prasad Kulkarni, Assistant Manager, Marketing Department – e-F@ctory Solutions, Mitsubishi Electric India, the process industry is slower in adopting digital transformation compared to discrete manufacturing in recent years. However, the use of heavily automated production, as well as centralised control and data collection dates back to the 1960s. “Process industry is the early adopter of ISA-95 standard, which is needed to ensure consistent terminology and operation models. With ISA-95, most process industry facilities achieve a nearly fully automated production process, where assets are connected to a central control system – e.g., distributed control systems (DCSs) and supervisory control and data acquisition (SCADA) – and historians/manufacturing execution systems (MESs) are often deployed,” he states.
“Competition is increasing, so digital transformation becomes important and gives powerful leverage to companies to stay relevant,” says Jasbir Singh, Director, ECPR Technologies, who is an Automation Expert and Implementation Strategist having long experience in Factory Automation and Line Automation. He points out how companies in process industry are adopting advanced digital technologies at a slower pace, mainly because of cultural barriers with which they were stuck in time for the past many years. It is also noted that the acceptance of advancement in digitalisation such as the internet, personal computing, mobile computing, learning benefits from social media and the recent development by Artificial Intelligence in technological innovations that cause the slow adapters to fail to implement correctly. “There are a number of factors behind this slowness where some entrepreneurs are not ready to act and change their business processes as per market demand and few others continue with the same process with an understanding that this fluctuation/surge in demand is temporary phenomena to react,” notes Singh.
With Big Data and analytics in the IIoT era, is the traditional Data Historian becoming history? Is this a smooth transition? “Data historians and big data systems serve different needs. There's room for both approaches,” says Berk Birand. “If we look at the tech world, every company has one fast database that stores transactional data, and a separate analytics database that's slower but contains more data. Both provide value – so I don't see the data historian going away any time soon,” explains Berk.
PV Sivaram is of the view that Big Data is a much bandied term, without a clear understanding. Big data, according to him, is not just a lot of data. It is more about how this data is stored, how it is retrieved, and for what purpose it is used. Data Historian serves a specific purpose in traditional control of process plants. Big Data is not aimed at such purpose. Therefore, the management of process plants might wish to use the Historian and the Big Data engines for their purposes. Historical data such as trend values could also be stored on the Cloud if it makes economic sense, and a use-case can be made out for the same. “For achieving operational excellence, one good method is to run simulation studies of the process. In the context of process plants, this simulation is part of a design validation process. In a plant of serious size, such studies are mandatory even in the planning and design phase,” he emphasises.
“Certainly, the emergence of Big Data and IIoT have blurred the role of the historian, but with the corresponding growth in the volume of process data, Data Historian is likely to continue to play a major role,” says Prasad Kulkarni, who feels recent advancements and technology adoption has evolved and improved the process historians in more sophisticated way. Methods such as data compression enabled ever larger amounts of information to be collected. Supported data types have expanded to include complex data types. Finally, the continuous advancement in computing performance has vastly increased both the volume and resolution of data collected. “In larger facilities, it is common to collect tens of thousands of data elements, with scan frequencies as high as a thousand times per second. Today’s data historians can collect derived and calculated information, contextualise, and store data to provide workers with timely insights on easy-to-understand digital dashboards,” he explains.
Taking a more nuanced view, Jasbir Singh states that a large amount of Big Data generation by the Industrial Internet of Things (IIoT) is obviously due to the massive use of sensors and data from other IoT devices. The collection of big data and its processing poses a challenge due to limited computational capability, networking and storage of intermittent results at the IoT device end. “Data analytics is required to provide operational and customer level alertness to implement in processes/systems. Big data analytic technologies, specific algorithms and techniques facilitate the precise development and application of intelligent IIoT systems. The requirement here is for classifying and categorising important parameters (data sources, right analytics, tools & analytics techniques, requirement-based use of analytics types and applications) and the computation to derive the right results,” he says.
How effective is the digital twin in process automation vis-à-vis discrete manufacturing? Is the process industry making use of digital twin technology?
“The phrase ‘digital twin’ means different things in the process industry and in discrete manufacturing. In discrete manufacturing, it refers to a CAD model of an individual product. In process automation, it's where you build a virtual copy of a process (not a product), which can then be used to run the factory better,” says Berk Birand, and adds that digital twins are really effective. “They can help engineers optimise processes without wasting time, energy, and resources on plant tests. One of our customers found that they were able to skip 98% of tests by using a digital twin to predict the quality of the final product – translating to huge savings, in terms of both emissions and financials,” he explains.
“Digital twin is more misused term than Big Data,” says PV Sivaram. “Depending on the context it can mean a simple model consisting of transfer functions and chemical or biological equations. It could extend to a good and close approximation to the functionality, with assumptions about materials and process parameters. It could give possibility to run different what-if scenarios. Surely for products which are still in experimental design phase, etc., digital twin in one of the above forms could be useful.”
To Prasad Kulkarni, the Digital Twin concept builds on traditional simulation techniques and continues to evolve and perform real-time simulations. The process industry is one of the early adopters of Digital Twin as it covers a wide range of complex manufacturing processes, from continuous facilities in the petrochemical industry to large-batch manufacturing in the glass and steel industries to small-batch manufacturing in the pharmaceutical and food industries. “Digital Twins are becoming increasingly important in the process industry and has realised significant benefits recently. Digital Twins in process industry enable process improvements through advanced data analytics and operational insight with collected data, informed decision making and ensure actual performance meets planned performance,” he opines.
Standards are important in any industry for smooth and error-free operations, and in automation this is critical to ensure efficiency, ease of management and reliability. Standards are followed right from building systems, and testing them before putting into use and maintaining them. Developed and issued by different groups, the number and complexity of these standards often present challenges for both end users and suppliers. How serous is this in practice?
The number of standards is challenging, agrees Berk Birand, as it adds cost and complexity to the whole system. “It would be preferable to have open standards. There are solutions that translate between proprietary standards, thereby making integration possible. However, these solutions still come at a cost in both price and increased system complexity. Open standards are crucial for the advancement of modern technology. Currently there aren't many, which adds cost and complexity to the system,” says Berk.
“Certainly, the number and complexity of standards presenting challenges for both end users and suppliers,” concurs Prasad Kulkarni, who believes in the context of digital transformation, timely and harmonised adoption of standards can play a pivotal role in shaping the digital transformation process, complementing regulations and contributing to digital transformation governance. It also ensures the successful scale-up of solutions to be implemented globally. “Standards can facilitate the ongoing digitalisation of industry by enhancing productivity and efficiency, promoting compatibility and interoperability between products and processes through common language, while minimising risk, improving safety, and supporting policy and legislation. Furthermore, standards can serve as accelerators of change as they promote innovation and the uptake of new digital technologies and spread knowledge through codification,” Prasad elaborates.
In 2016 ExxonMobil issued a call for an open process control architecture that is modular, interoperable and scalable, in order to deal with the various issues related to closed proprietary systems of different vendors. The company was basically asking for suppliers to move toward standards-based, open, secure and interoperable process automation architectures. The Open Process Automation Forum (OPAF) was founded in response to this. What is the present status of OPAF?
“Development and induction of massive IoT devices, robotics applications, cyber-physical systems, artificial intelligence (AI) and machine learning lead to the requirement of a unified operating system/software platform and common standard. The present need is for the many proprietary process automation standards to translate into a single common standard, which is a much more open, interoperable, secure and modular approach. This is the demand by system end users, system integrators and suppliers. It means a Standards-based, secure, interoperable, open automation architecture to replace closed proprietary devices of the past,” says Jasbir Singh. The Open Process Automation™ Forum (OPAF) has a good number of members from firms and organisations consisting of end users, system integrators, suppliers and support groups, and has worked persistently since November 2016 to develop a common process automation standard with a guideline that The Open Group will manage the standard, publish guidelines and the companies shall develop products with established OPA software.
“Process Industry is adopting Open Process Automation very rapidly and Mitsubishi Electric is ready with standard-based, open, secure and interoperable process automation architecture. Mitsubishi Electric’s MELSEC iQ-R Series process CPU/redundant systems are ideal for various industrial process control applications requiring highly reliable process control solutions that can be easily integrated,” says Prasad Kulkarni. “Most components are based on the standard range of MELSEC iQ-R Series modules, enabling total cost of ownership to be reduced through utilisation of its extensive functions and features. Its extensive PID instructions that are embedded in the CPU can be used for maintaining stringent process parameters,” he adds.
There have been serious cyberattacks on process industries in recent months. How strong are the safeguards? “The safeguards are not strong at all, and there probably will be more high-profile cyberattacks until the industrial sector at large realises how important it is to secure your factory. We take these concerns quite seriously at Fero Labs. Our industrial customers can connect remotely to factories through their browser, but we do it in a way that creates no security threats,” says Berk Birand.
“It is unfortunate that there have been cyberattacks on process industries in recent months. No industrial operation is free of risk, and different industrial enterprises may legitimately have different ‘appetites’ for certain types of risks. Evaluating cyber risk in industrial control system (ICS) networks is difficult, considering their complex nature, says Prasad Kulkarni. Individual business needs to assess potential cyber threats to their own industrial sites across a wide range of circumstances, consequences and sophistication. A security program/posture can only be evaluated if we have a clear understanding of the kinds of attacks that might target the protected industrial site,” he suggests.
“No organisation is completely immune from cyberattacks. Cybersecurity to be considered by business leaders as a primary organisational risk. Cybersecurity is by virtue a vital operational risk and must be a main focus area for every organisation’s enterprise risk management. All companies are growing reliant on large data and network bus connectivity, and regularly increasing number of cyberattacks,” says Jasbir Singh. “Cybersecurity will continue to gain business attention. Leaders are prompted to react to recent cyber incidents, and what they are doing to address these and come up with anticipated regulatory requirements. Cybersecurity is becoming a top priority for every company,” he says in conclusion.
(Note: The responses of various experts featured in this story are their personal views and not necessarily of the companies or organisations they represent. The full interviews are hosted online at https://www.iedcommunications.com/interviews)