Earlier this year we had the opportunity to be a part of a strategic transition of a Japanese multinational electrical engineering and software company – Yokogawa. In this interesting virtual press meet, Dr. Hiroaki Kanokogi, GM, Yokogawa Products Headquarters, Yokogawa Electric Corporation | Kohinoor Ghosh, Associate Vice President & Head- Analyser Business & Marketing, Yokogawa India & Sumit Srivastava, Head – PSS Sales, Yokogawa India underlined the company’s move towards critical sectors while unleashing the power of industrial autonomy. The trio in this exclusive interview unveils their technological strength while adopting and innovating the power of AI/ML, the future of IIoT in India as they foresee and Yokogawa’s key strategies on sustainability when it comes to empowering autonomy ahead. Interesting Excerpts Below.
Yokogawa’s advancements in AI, ML, etc., is the company self-innovating in this space or has collaborated with companies helping data scientists build and train AI and machine learning models. Glad to have your comments.
Yokogawa can offer customers several kinds of AI. Some of them are, as you mentioned, delivered through collaboration with other companies. But the Yokogawa Products Headquarters has developed its own AI technologies that also leverage our strong knowledge of operational technology (OT). FKDPP, jointly developed with NAIST, and the kinds of AI I mentioned in my presentation are all developed inside the Yokogawa Products Headquarters. Our current thinking is that we will continue developing our own unique AI in our division so that we can deliver the most appropriate AI for our customers.
Yokogawa’s key focus and investment in Reinforcement Learning (RL) approach to help design automatic control strategies in a large-scale chemical process control scenario?
The field test announced in March was carried out at a former JSR chemical plant, but we are not just focusing on this industry or application. We have already used FKDPP effectively in other simulations and field tests, including at our own factory, and we are confident that FKDPP can deliver value in a wide variety of difficult-tocontrol applications in energy, materials, pharmaceuticals, and many other industries. We are now looking to carry out additional field tests in various industries to show customers that FKDPP can benefit their operations. We are also working on building a stronger team to offer this to customers globally.
How is Yokogawa currently working with critical sectors to think or let’s say make devices, equipment, business and production systems, or entire plants to operate autonomously? Has the prominent focus been on the OT part only or also the IT?
Yokogawa always thinks and co-innovates from the perspective of the customer’s needs and challenges. We strongly believe we have the technology and mindset to help our customers win. From that perspective, we think OT and IT are both required to make the process safer and more productive. We have been deploying the latest technologies and co-innovating solutions that help to address the challenges of our customers. Numerous smart devices and solutions are in use in the industry. We have moved beyond automation and innovated autonomous solutions where human error is minimized, if not eliminated. We are creating opportunities where human assets can be deployed for more meaningful and safer functions. Yokogawa’s autonomous solutions are also helping to optimize the usage of natural resources and to make the growth sustainable. We have our homegrown strength of over a century and we are also acquiring companies with unique technological strength, to step up our capabilities to support our customers’ causes.
Control in the process industries spans a broad range of fields, which sectors are of key focus for Yokogawa when specifically talking about the Indian market?
Yokogawa’s solution is versatile and field-proven. We are present in almost all industry segments which include the conventional ones like Power, Hydrocarbon, Chemicals, Mines and Metals, Paper & Pulp, Cement etc. We do also cater to sunrise industry segments like Water, Waste to Energy, Renewable Energy, Batteries, Pharmaceuticals, Food & Beverages etc.
Various challenges that exist must be addressed for industrial autonomy implementations to be successful in every phase. How is Yokogawa simplifying the journey?
Yokogawa understands the roadblocks in the implementation of Industrial Autonomy. Potential users’ confidence in the robustness and usefulness of the technology is essential. More important is the mindset. Like anything which causes a paradigm shift, it has a slow acceptance rate but the industry does not have much time. The industry needs to grow and need to be mindful of the planet we live on. Striking the balance is of utmost importance. We and our customers understand this. Yokogawa tries to support our customers and build their confidence. We get into extensive discussions with customers, understand their challenges, as-is status etc. We then co-innovate the solutions. To give more comfort to our customers, we offer a Proof of Concept ( POC) approach, Digital Twin technologies etc. Customers get assured of not only the benefits and ROI but also get assured about business continuity, much before the implementation phase.
Also, any key use cases or projects have Yokogawa collaborated with or aligned in the Indian market, how big do you see the possibility of IIoT in India?
I don’t think anybody has any reasonable estimation of the potential IIoT in India or elsewhere. “Unlimited” can be the best guess today. We can draw an analogy, at the best. IIoT is now what the concept of Search Engine in the early 2000s or personal computer in mid-80s and closer to the present, Digital payment gateway in 8~10 years back. Every need in our daily life or every measurement or challenge in the industry is a potential area of IIoT deployment.
Yokogawa has many use cases in India where IIoT has enabled users to address thechallenges regarding real-time,error-free data gathering, analyzing, and preempting actions. It has helped the industry reduce energy usage and make the process cleaner and safer. We have been continuously co-innovating to make the planet smarter.
Yokogawa’s key strategies on sustainability when it comes to empowering autonomy ahead? Can Autonomy make a high impact on greenhouse gas reduction and on energy management, etc?
Yokogawa’s vision for Industrial Autonomy is firmly built on the System of Systems concept. This enables us to tightly couple our solutions ranging from Instrumentation, IIoT sensors & Analyzers like TDLS (Tunable Diode Laser Spectroscopy) with our Digital Twin, Energy Mgmt., Real-Time Optimization & Analytics solutions to not only derive actionable insights but also close the loop by performing the necessary mitigation / remote control actions on the Control Systems at the Plant level. This level of integration builds the platform needed for autonomous operations and not only ensures greater process safety but also impacts GHG emissions because the systems work in tandem in real-time while exchanging pertinent information to offer greater visibility to stakeholders and performing the necessary process control actions to ensure the emission targets/objectives are not compromised while adhering to strategic Production & Operational Efficiency KPI’s.
The diverse ecosystem of the digital twin in IoT, when combined with data analytics, offers an invaluable digital twin solution and resource for predictive maintenance & fault detection. However, advanced features like real-time model responsiveness, flexibility, sensing ability, integration, captivating interaction, and persistent semantics are still developing and seem challenging. Your comments on it.
Digital Twin as a technology is still maturing. As rightly pointed out, we can segregate the entire Digital Twin Solution stack to comprise the following:
- Additional IIoT sensor stocomplement traditional Instrumentation
- Data Integration, extraction, cleansing & transformation for digital representation of the physical asset
- Model to represent functionality & operation of the physical asset (Physics / 1st Principle-based, or purely data-driven model (AI/ML or statistical)
- Real-Time / Near Real Time / Scheduled data ingestion to the digital model and deriving actionable insights (Descriptive / Predictive / Prescriptive) to drive key outcomes.
The most challenging aspect of the Digital Twin is to have the computing resources to run the models and keep the models up to date to reflect the actual physical state of the asset to sustain the benefits over a period. The sensing, data extraction, and compute ecosystem has greatly improved and is supplemented by the power of Cloud computing. The critical aspect of model maintenance is following suit. Technology providers are turning to AI/ ML to ensure the models self-regulate and automatically tune themselves to mimic their real-world counterparts. This critical piece of the puzzle needs to be addressed to ensure the widespread adoption of Digital Twins in the near future.