Digitalization results in flexible and modular plants for faster reaction times and better outcomes.

Process Industries have traditionally adapted well to automation by using sensors & actuators, and instrumentation & controls in its operations. With growing trends of digitalisation, new opportunities are created with the convergence of IT/OT and integration of cutting-edge technologies like low code, edge, cloud computing and artificial intelligence. Most process industries are capital intensive and legacy systems still operate far too many assets which do not make the process of digitalisation easy. However digital transformation can improve outcomes across the sector, helping to provide solutions to mitigate these risks, improve operational efficiency, evolve business models, and provide better customer experiences. The result is more flexible and modular plants for faster reaction times and better outcomes. But before that, it is necessary to understand how process industries have historically automated with sensors, actuators, instrumentation and control in their operation?

“25 years ago, particularly in life sciences and similar industries, batch operation processes were controlled and recorded manually. Later in the early 2000s, with the introduction of data loggers the critical process parameters were recorded and printed during batch operation. These systems are in compliance with regulatory requirements like FDA. However, all the operations were done manually. As we all know manual operations may have problems related to quality and safety due to complete dependency on humans. With the support of automation industry process controls are being transformed from manual to auto through extensive use of actuators like control valves, sensors and transmitters for flow, level, temperature, pressure, pH, conductivity, etc., by integrating to PLC (programmable logic controllers)/DCS (distributed control systems). This helps organisation to improve efficiency, quality and safety to a great extent,” says Jerome Gnanaprakasam, Global Head – Projects, Engineering, EHS & Sustainability, Dr Reddy's Laboratories, who has over 30 years of industrial experience in leading pharmaceutical operations, capital projects, engineering, safety, environment, ESG & sustainability.

“The process industries, due to the inherent complexity of the process, require built-in controls for basic working and process flows. These are built-in as a design with considerable numbers of the sensors, instrumentation and feedback control loops, and hence ensuring the overall visibility-monitoring-and-control on the process as an inherent feature of the process industry,” says Shirish Kulkarni, Founder & MD, STROTA ConsulTech Pvt Ltd. Shirish, an industry veteran with large corporate experience of 30+ years, is now helping the Small and Medium Businesses as a Business Advisor. “Further efficiency and effectiveness of these basic building blocks are achieved with the latest of the trends in the technology space, which are segmented under the Industry 4.0 or Smart Manufacturing parlance. The objectives, and hence the challenges, for the process industry are completely different and unique against the discrete manufacturing – covering the elements of continuous flow, critical thresholds to prevent system going out-of-control, check-points at all the critical process steps, inter-dependency and hence inter-connectivity of the process parameters takes the technology to be implemented to the next level of complexity and hence usage,” he explains.

Prasanna Lohar, CEO at Block Stack and President at India Blockchain Forum, recalls his time at Tecnimont/ICB, a company with decades of experience in development of projects. “I witnessed the seamless integration of sensors, actuators, instrumentation, and control systems in EPC turnkey projects. This integration enabled process industries to automate repetitive tasks, optimise production processes, minimise human error, and ensure consistent product quality. By harnessing these technologies, companies could achieve greater efficiency, reliability, and precision in their operations, ultimately leading to improved competitiveness and customer satisfaction. Eventually we have seen technological advancement in these areas with artificial intelligence, cloud computing, advanced analytics, IoT integration, wireless connectivity, integration with robotics, remote monitoring, says Prasanna, who has worked globally for banks, fintech, micro-finance, engineering, and multi-national companies for digital & architecture transformation.

What are the examples of specific technologies or methods that process industries have used to enhance automation in their operations?

According to Sharul B A Rashid, Head, Technical Excellence and Group Technical Authority, Instrumentation and Control at PETRONAS, ‘Automation’ describes a wide range of technologies that reduce human intervention in processes, mainly by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines. “Depending on the complexity of the process industries, certain levels of automations are designed, constructed, tested and commissioned to enable excellence asset management and reliable plant operations including managing the supply chain better in response to business and market needs, safeguarding employee safety and ensuring environmental compliance. As customers look into innovation in the products and user experience, leveraging technology and digital as they are maturing and advancing is key for survival,” says Sharul, who has spent over 22 years at the PETRONAS Group in Malaysia and is now Head, Technical Excellence under TDEX, GTS (PD&T) focusing on technical excellence in safeguarding and shaping of PETRONAS Group towards asset operational excellence and growth in decarbonisation.

“There are various technologies adopted in different industries that best suit them. When it comes to the life science industry the quality and safety go hand in hand and it’s a foundation for manufacturing which cannot be compromised. Of course quality and safety is important in all industries whereas in life science it is more stringent and lifeline of the process and non-negotiable as it is directly impacting the patients in a larger way. Process is operated and controlled through batch application as per ISA88 standards hosted in systems like DCS. Also control systems are integrated with the Manufacturing Execution System (MES) for paperless e-batch manufacturing reports and process control,” explains Jerome Gnanaprakasam.

For Shirish Kulkarni, the elements of Industry 4.0 have been making an impact on the process industries as well. Their application and use-cases are specific to the context of the process industry. The elements of Industry 4.0 enable utilising real-time data and advanced analytics, to help process manufacturers to optimise and control their processes and to reduce costs, resulting in higher efficiency and increased profitability. “The key perspectives of Industry 4.0 like  Process Automation, Simulation (Process/Product), Shopfloor to Topfloor connect, IIoT (Industrial Internet of Things), Elements of Security – IT and Cybersecurity, the Digital Forces like Cloud, Mobility, Augmented Reality – Virtual Reality are helping the key scenario like predictive maintenance, Big Data Analytics, etc., bringing in the ability of forecasting of process parameters,” says Shirish.

What role does digitalisation now play in the transformation of process industries with the convergence of IT/OT (Information Technology/Operational Technology)?

“Digitalisation is of late strongly associated with the discrete manufacturing industry. Indeed the allied terms for digitalisation – smart manufacturing, Industry 4.0 – are also used in conjunction with discrete manufacturing. This has occurred from the fact that process industries are highly instrumentated; meaning that there was always a good amount of instrumentation in a process plant. With this foundation, it was relatively easier for these industries to implement automation,” says PV Sivaram, Evangelist for Digital Transformation and Industrial Automation, who retired as the Non-Executive Chairman of B&R Industrial Automation and earlier the Managing Director. Prior to B&R, Sivaram has worked at BARC where he began his career after graduation, and then with Siemens, where he gained considerable experience in Distributed Systems, SCADA, DCS, and microcontroller applications. “Process automation has been until lately a centralised concept. There are sensors all across the plant, all connected to controllers at a central location. Increasing cost of instrument cabling, and associated maintenance problems led to the concept of distributed control systems (DCS). At present, an actual direct intervention of digitalisation into plant operations does not seem to appeal. But how about broadening the view beyond plant control? Then many applications suggest themselves; right from supply-chain to quality, recipe management. Take a step back and you can see design and validation of control algorithms as a worthy topic,” he elaborates.

To Prasanna Lohar, digitalisation plays a pivotal role in transforming process industries through the convergence of Information Technology (IT) and Operational Technology (OT). This convergence blurs the traditional boundaries between IT systems (such as enterprise resource planning and data analytics) and OT systems (such as industrial control systems and sensors), creating new opportunities for efficiency, innovation, and competitiveness. “Digitalisation enables process industries to collect, analyse, and leverage data from across the production chain in real time, facilitating predictive maintenance, process optimisation, and quality control. By integrating IT and OT systems, companies can enhance visibility, agility, and decision-making capabilities, driving operational excellence and unlocking new levels of productivity and profitability. Digitalisation also enables the adoption of advanced technologies such as artificial intelligence, digital twins, and blockchain, further accelerating the transformation of process industries towards smarter, more connected, and resilient operations,” he opines.

“Digitalisation may refer to digital transformation, the adoption of digital tools to create new or modify existing products, services and operations. Operational Technology (OT) is hardware and software that detects or causes a change, through the direct monitoring and/or control of industrial equipment, assets, processes and events whereas Information Technology (IT) is a set of related fields that encompass computer systems, software, programming languages and data and information processing and storage,” says Sharul B A Rashid. “With the unprecedented challenges faced by process industries in the next decade, as the process industries are preparing themselves towards the Energy Transition and Sustainability agenda, the three pronged strategy of People-Process-Technology needs to be quickly adopted,” he adds.

How do low code, edge computing, and cloud computing contribute to the flexibility and modularity of plants in the context of process industries?

According to Jerome Gnanaprakasam, today the global market is very dynamic and industries should quickly adapt to the changes in order to sustain. “I believe that the use of any latest technologies which can give flexibility and modularity always helps industries in many ways to stay updated and respond quickly to such dynamics. In regard to low code, pre-programmed modules or functional blocks help users to develop applications quickly by eliminating the need for special coding skills. Platforms like IoT and cloud computing bring a lot of value to manufacturing in terms of data analysis, data integrity, reliability, scalability, etc. However, the challenge comes from the right skillset and right implementation which needs to be balanced,” he asserts.

“Edge computing enables real-time monitoring and analysis of data generated by machines and other devices in a manufacturing environment, which can help to improve operational efficiency and reduce downtime. While the low code systems are the ones, which require minimal or NO code to be developed for their implementation or any changes in the business rules. Cloud computing enables the storage and processing of large amounts of data without investing in advanced in-house systems,” says Shirish Kulkarni, who is of the view that it is essential for implementing Industry 4.0 technologies like Artificial Intelligence, Machine Learning, and Industrial Internet of Things (IIoT) connectivity. “The process industry generates a huge amount of data (Big Data) in the form of time-series data of various process parameters, set-points, on-the-fly calculations for compound values and forecasting. This data has to be handled for its contextualisation, storage, analysis and AI related learning of the process to be able to help the visibility, monitoring and hence control of the process more and more effectively – through these low-code systems, leveraging Edge & Cloud Computing,” he explains.

“Low code, edge computing, and cloud computing play crucial roles in enhancing the flexibility and modularity of plants in process industries, enabling them to adapt to changing demands and optimise operations. Low code development accelerates application deployment, edge computing enables real-time data processing at the plant level, and cloud computing centralises data management and analytics,” says Prasanna Lohar. “In my view, post Covid-19 and considering Future of Work and current Business Trends, together, these technologies enhance the flexibility and modularity of process plants, allowing them to respond quickly to changes, optimise operations, and drive continuous improvement. Successful organisation should be on cloud and edge computing infrastructure.  Eventually I can see the banking industry also started utilising these tools during development and monitoring branch operations,” he states.

Digitalisation is not complete without cutting-edge technologies, such as digital twins and artificial intelligence, etc. What are the specific benefits that the integration of these technologies bring to process industries?

“Both Digital Twin and Artificial Intelligence are being bandied around so much that it becomes difficult to pin down their meaning and definition. Digital Twin has close cousins – simulation and modelling. In any process of sufficient complexity, modelling is a first serious step. Today, with the tools that are available, digital modelling should be a compulsory first step before design engineering of a plant,” says PV Sivaram. To him, a robust model exposes many crucial design parameters and constraints. Once you have a strong model, the next one can use simulation tools to explore several what-if scenarios. With simulation, one can achieve high efficiency, safety and sustainability at viable cost. “Digital twin aspires to have the same process model at its heart. The model becomes a digital twin when the input measurements and outputs are continuously made available to the Digital Twin. The digital twin can help in maintenance scheduling, in production planning, and innovation of product mix,” he elaborates.

For Shirish Kulkarni, digital twins are used for simulation and operational phases of a product or process lifecycle. “Regardless of how you build a digital twin, the overall outcome is having a digital representation that you can use to gain more knowledge and deeper visibility into your production process. AI provides an avenue for the digital system to define decision making rules through the logic which could be passed through the learning phase based on the decisions arrived at in every cycle. The AI-based digital twin of a process keeps learning and becoming more and more capable, intelligent and self-sufficient to address wider scenarios in the real life of the process plant,” he says. Shirish further draws attention to the fact that digital twins help in simulation to be able to simulate trials of the process, which in reality is not possible at all due to physical limitation, and saving on the costs of each of the physical tests/trials eventually. AI helps automation of the decision making process to be able to have elements of self-controlled process and reduce the continuous manual interventions, by restricting the human acumen to be leveraged for the critical aspects of threshold setting, monitoring for outliers, getting the course corrections in place.

Sharul B A Rashid has tabulated the following possible business benefits that cutting-edge technologies and digital bring about to the table:

Realtime access to new data source (including used to be stranded data):

• High visibility into every aspect of process industries operations
• Insightful data for fast decision-making based on real time information.
• KPI dashboard with rich data visualisation

Data analytics:

• Proactive and preventative maintenance
• Higher wrench time
• Increased asset utilisation     
• Reduced non-productive time

Standardized platform, open standard architecture for wired and wireless infrastructure:

• Flexible implementation of advanced applications, and reduced time to deployment of devices, avoiding expensive cabling
• Better management of resources and assets with digital worker
• Streamlined management and administration.

Pervasive, accurate location-based services and tracking:

• Improved safety for personnel and asset
• Less logistics and cost-effectiveness
• Safe time from manual searching

Cyber and physical security:

• Increase resilient to cyber attack
• Greater regulatory and security compliance

Digital worker/worker mobility:

• Improved turnaround time and cost savings
• Access to remote expert 24-7 round the clock

Remote autonomous operation.

How does the trend towards more flexible and modular plants align with broader industry goals, and what are the potential implications for the future of process industries?

“It is the market which dictates terms. When the demand is continuously changing, production needs to be flexible to track the market. Indeed, it is not even sufficient to track the market with a lag; it is needed to anticipate the market. How does one do this? Today we have very good tools which are able to analyse trends from social media, and come out with reliable forecasts. This is something which process industries need to get good at. In other words, no matter what your line of business and product which you make, you must have digitalisation at your core,” says PV Sivaram. “Future competition is not going to be between companies, it will be between supply chains. That means companies must integrate strongly with their entire supply chain. The topic is already well developed in discrete manufacturing, and the process industry will do well to benefit from the learnings,” he adds, matter of factly.

Jerome Gnanaprakasam is of the opinion that today process industries are shifting towards flexible and modular plants to adapt quickly to dynamic environments. Modular plant supports complex installation and reinstallation with minor reconfiguration based on the product requirements and requires fewer efforts in commissioning. “This helps process industries to stay competitive, adapt easily to changing market environments at lower cost. However, creating a flexible and modular plant needs skilled workforce in designing, automation and digitisation,” he suggests.

“The scalability complemented by the reliability is possible through making the process robust against any of the undulations, disturbances – which is achieved by the self-learning, simulated process through AI and digital twin. The flexibility is offered by the capability of the process to accommodate any changes to any variants of the product or the process to be incorporated due the standardisation and modularity of the process components. This also helps reusability of these established, stable, documented, standardised and approved components of the process or the products themselves to be used for any other processes or for the future expansion to be incorporated,” says Shirish Kulkarni.

According to Prasanna Lohar, by integrating digital twins, AI and other cutting edge technologies like Blockchain, process industries can achieve synergistic benefits such as:

• Enhanced visibility and control over operations through real-time monitoring and predictive analytics.
• Improved efficiency, reliability, and safety of production processes.
• Greater transparency, traceability, and compliance with regulatory requirements.
• Enhanced collaboration and trust among stakeholders across the value chain.
• Accelerated innovation and continuous improvement through data-driven insights and optimisation.

According to Sharul B A Rashid, process industries automation systems have a huge installed-based physical equipment with a 10-year lifetime or longer. Its provision for retrofit of existing systems and their migration to the new architecture overtime to extend the longevity (useful lifecycle) must exhibit the following attributes:

• Portability – Software tools and agents can accept and correctly interpret library elements (software components and system configurations) produced by others.
• Interoperability – Devices can operate together to perform autonomous and/or cooperative functions as defined by international standards.
• Configurability – Device and their software components can be dynamically configured (selected, assigned location, interconnected and parameterised) by multiple software tools and/or software agents.

“Intelligence at the device level will increase functionality, performance and reliability that facilitate its integration into a more complex production system via network connectivity and web services. System orchestration together with system management is the next generation process industries automation that will facilitate flexible manufacturing that enable rapid integration and reconfiguration of assets to be autonomous and intelligent,” he concludes.