Capitalizing on Technology for Businesses in The New Normal

Q1. Competition is intense between technology companies. What advantages does Aspen Technology have over its competition and how does the company ensure consumer confidence?

Answer: Built upon the research developed at the MIT ASPEN Project, in response to the 1970s energy crisis, Aspen Technology was started in 1981. Today, 40 years later, the company has gained significant domain expertise in capital-intensive industries and offers an innovative Industrial AI solution that separates itself from more generic AI approaches.

Industrial AI is just the beginning, this disruptive innovation is well positioned to guide innovation and efficiency improvements in capital-intensive industries. This highly scalable capability enables general engineers to leverage machine learning, without being advanced data specialists or specialist engineers. Customers are confident in choosing AspenTech because of its unmatched domain expertise – we understand the industry from a time-proven perspective.

The horsepower that works comes from the fact that AI algorithms represent only 5% of the software source code, with the remaining critical 95% derived from domain expertise. Process and capital-intensive companies can confidently choose AspenTech, to reap rewards in both profitability and sustainability – via accelerated digitalization.

Q2. What are the highlights and key features in software management now and in the future?

Answer: With aspenONE V12.1 software, AspenTech has extended Industrial AI across its leading solutions to drive higher levels of profitability and sustainability in customer operations. The Industrial AI Workbench enables data scientists to collaborate with domain experts to develop AI apps, based on enterprise-wide data.

With First Principles Driven Hybrid Models, AI is directly embedded into Aspen HYSYS and Aspen Plus process simulations, enabling engineers to easily build operations-ready models calibrated with relevant plant data. In addition, Reduced Order Hybrid Models can be shared across engineering, planning, and dynamic optimization solutions to improve the accuracy and predictability of these applications. Furthermore, Deep Learning APC can deliver more accurate and sustainable models that cover a broad range of operating conditions. Together, these advanced capabilities deliver the next generation of operational excellence.

aspenONE V12.1 offers new software models that enables customers to optimize biomass processing, hydrogen production, carbon capture, and carbon emissions more accurately and systematically, focused on reducing environmental impact. New analysis and visualization capabilities can help reduce measurable waste and energy use, throughout the process from lab to production.

AI-driven 3D conceptual layouts with the introduction of Aspen OptiPlant and Aspen OptiRouter, are now integrated into the AspenTech portfolio. For industries, such as pharmaceuticals (under pressure for a faster time to market), Aspen Unscrambler, Aspen Unscrambler HIS and Aspen Process Pulse, ensures product and process quality by solving complex problems using multivariate analysis to drive more profitable processes, less deviation, and higher yield.

Companies can accelerate their digitalization journey and leverage Industrial AI to make progress toward the Self-Optimizing Plant, while increasing margins, achieving sustainability and reliable, safe operations, as well as reducing capital cost and time in bringing assets online.

Q3. How can software or artificial intelligence systems installed in plants and companies be improved? How much does it cost to upgrade these systems and what is the value derived?

Answer: Companies should focus on the value they derive, or return on investment. For example, AspenTech’s customer, BPCL, an oil and gas giant, uses digital twins to track emissions and the resulting data generated helps identify trends; correct errors; optimize production; as well as reduce carbon emissions. As a result, BPCL saved about USD 600K a year, increasing profitability, and achieving sustainability.

Locally, in Thailand, petrochemical company, Vinythai, has selected Aspen Mtell software, to achieve business sustainability via embedded AI. In doing so, the company can accelerate digital transformation, reduce failure, and improve reliability by deploying predictive and prescriptive maintenance software at its petrochemical plants locally. With this solution, companies can mine historical and real-time data to predict future failures and prescribe detailed actions to mitigate or solve problems via predictive and prescriptive maintenance.

Q4. What software or AI innovation will impact the future of businesses and how they are managed?

Answer: Accelerated digitalization will impact the future, as companies with the ability to balance profitability and sustainability can address the dual challenge more efficiently. The dual challenge mandates the need to address resources for a growing population with increasing standards of living, while balancing the need to meet sustainability goals. For example, in the new normal, supply chain management is critical, as sustainability and resiliency are two sides of the same coin.

FPCO, Japan’s largest manufacturer of food containers, and a logistics supplier, is one company that can achieve this critical balance. FPCO is committed to environmental advancement, avidly recycling used food containers and PET bottles. With more than a billion containers sold each month, selling recycled products needed to be an economically sustainable activity. The company chose aspenONE Supply Chain Management (SCM) to provide stable and responsive food distribution in an efficient, sustainable, as well as environmentally friendly manner.

In the new normal, Industrial AI can help companies navigate increasingly complex supply chain options and decisions. Momentum from rapidly changing global carbon mitigation necessitates focus on energy transition across Asia. Leading process companies are also augmenting production optimization with Industrial AI. A new class of workforce enablement technology is created, as predictive maintenance enables organizations to gain increased flexibility in maintenance.

Hybrid models combining rigorous and AI-driven models are increasingly required to optimize complex operations, more accurately and autonomously, especially for energy transition technology options. For capital projects, estimation and project progress transparency can unlock value. To manage project risk efficiently, it is necessary to visualize, analyze benchmark and share data to increase speed and certainty. The result is a more agile, collaborative, and informed asset design – with a seamless and more predictable execution process.

Q5. What should businesses be most concerned about post-pandemic and how can Aspen Technology support businesses with regards to the outlined pain points?

Answer: Post pandemic, or the new normal, is an excellent time for Thai process and capital-intensive companies to accelerate profitability and sustainability via innovation focused on digital transformation. For example, skills shortage will be a critical issue, as industry downsizing whittles down valuable domain expertise. With most data scientists being relatively new on the job, the use of digital tools and analytics looks to be on the rise, especially solutions that accelerate collaboration between the new wave of data scientists and technical domain experts.

Cost and carbon footprint reduction will remain high on the agenda. A shift in refining production mix towards chemical feedstocks is expected, as growth in chemicals is expected to account for half of the near-term demand growth for oil in Asia. Mega integrated plant projects can address changing market demands efficiently and industry trajectory shifting from oil to gas consumption continues. Natural gas and renewables can address increasing demand for electricity, and the hydrogen economy is an emerging dimension. As these new energy areas gain momentum post pandemic, rapid and powerful early concept design is crucial for the techno-economic analysis to ensure a profitable asset lifecycle.

AspenTech’s vision for the Self-Optimizing Plant is a fully digitally enabled asset that is self-learning, self-adapting, and self-sustaining. Customers want to build more agile organizations. At the enterprise level, companies need to stitch together increasingly intelligent assets into more agile and responsive value chains. This Self-Optimizing Plant capitalizes on data to generate knowledge and Industrial AI provides companies the massive ability to learn, unlearn and relearn. Thus, Industrial AI can be viewed as a strategic business weapon, combining the power of analytics and AI machine learning, with crucial guardrails of domain expertise, to extract value from industrial data.