Industrial AI & the Self-Optimizing Plant
siliconindia questions Antonio Pieteri on self-optimizing plant and how AI and other tech integration will help the industry grow further.
Discuss the current level of uncertainty for businesses? What are their greatest concerns?
There are several contributing factors to the current uncertainty for businesses. First, with global lockdowns, companies are having to constantly adjust their manufacturing operations to meet supply and demand swings. Second, organizations need to become more agile and digitally productive while many staff work remotely. Third, we see companies rapidly wanting to ramp up the introduction of new processes and technologies to underpin this speed of change and market volatility.
McKinsey has estimated that the pandemic has accelerated digital trends by ten years. We understand that companies in India are facing multiple challenges including increased global competition, evolving demand for products and increased pressure from their stakeholders to reduce their environmental impact. Our aim from the outset of the global pandemic was to support all our customers in meeting these challenges head on. We want to enable our customers to innovate and be part of this acceleration and to be able to capitalize on their use of technology in order to successfully navigate the macro-economic situation.
What is the new normal and how will the Self-Optimizing Plant (SOP) benefit businesses?
We’re hearing a lot about the ‘new normal’ and what that will entail, but the fact remains that no one knows exactly what this next phase will look like. It’s still being defined by global health conditions, international politics, changing regulations and the overall condition of the economy. There is a lot of uncertainty on a massive scale across India fuelled by the pandemic and the dramatic changes in supply and demand balances which have impacted oil and chemicals prices.
Relying on a combination of AI and key domain knowledge, the Self-Optimizing Plant will rapidly assess all available operating data, both within an asset and beyond its boundaries. It will rapidly react to changing conditions to achieve the best possible outcome, considering safety, sustainability, asset health, and operational objectives. Further, it will use AI to anticipate future behavior and provide workers and managers with future operational scenarios.
Why is Industrial AI a value proposition and how does this technology fit into the Self-Optimizing Plant vision?
Industrial AI is a new category of technology solutions that combine data science and AI with the ‘first principles’ (or fundamental basics) of chemistry and physics and domain expertise to deliver comprehensive business outcomes for the specific needs of capital-intensive industries. Using AI to drive asset optimization will separate the leaders from the laggards over the next 10 years.
Indian industries have the power to deploy Industrial AI via our aspenONE V12 software, which embeds AI across our software solutions. These solutions contain the first Industrial AI hybrid model capability that is purpose-built for the process industries and other capital-intensive industries. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech’s domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale.
As Peter Reynolds, Senior Analyst, ARC Advisory Group, said, “AI has the potential to enhance many industrial work processes; however, most companies are not well-equipped to bolt on AI themselves. AspenTech’s industry-specific applications, with embedded AI, will help companies accelerate transformation. While other technology strategies require asset owners to invest in complex platforms and data scientists, with embedded AI, users can immediately start to improve margins and profitability.”
Relying on a combination of AI and key domain knowledge, the Self-Optimizing Plant will rapidly assess all available operating data, both within an asset and beyond its boundaries
How does the SOP drive new levels of plant safety? How can the SOP minimize environmental impact with maximum efficiency?
Capital-intensive industries face pressure to reduce environmental impact while improving safety and ensuring viable long-term business. They face pressure from governments, investors, customers, employees and the general public to achieve sustainability goals.
In the Self-Optimizing Plant of the future, operators and technicians will increasingly be making faster and more agile business decisions, freed up from low value-add, repetitive tasks by the systems that have closed the loop to operate the plant closer to intended limits and automatically react to unforeseen scenarios. Moreover, asset reliability information and operating data will inform the models to achieve safer, more sustainable designs.
How can this technology empower the next generation workforce, while embracing a remote working environment?
Businesses are concerned about having the right number of data scientists available to adopt AI solutions and have doubts about the value of AI versus the cost of implementation. They also have concerns about taking a risk on a new technology. While the chemicals and energy sectors, for example, have been early adopters of many digital tools, they have not yet fully realized the potential of Industrial AI.
However, businesses should be concerned about being left behind by not adopting AI solutions. The combination of the pandemic, an ultra-volatile oil market and global political changes are resulting in Indian companies needing to adapt quickly and our solutions are helping our customers address these concerns and move forward.
For employees, advanced AI technology will empower a new generation of technically savvy workers to transform energy businesses and position them to win in the marketplace of tomorrow. The workforce our customers are depending on demands technology that is intuitive and effective. By augmenting workers with cognitive guidance, automated knowledge and natural language processing, companies can drive optimum decision-making at every level and safely challenge operational limits. By 2025, about 75 percent of the workforce will be millennials (source: Deloitte). The automation of processes, and even knowledge, becomes a critical enabler for this generation of workers.