The global pandemic of 2020 upended the world and reset the parameters for digital transformation. 2020 saw global disruption of the rapid pace of technological innovation. Industry observers estimate that due to the pandemic, digital trends have accelerated by as much as a decade. This will support industry growth in Asia, help achieve sustainability goals and improve the resiliency of shifting supply chains. Looking ahead to 2021 it is expected that there will be a continued focus on accelerated digitalization with artificial intelligence (AI) playing an increasingly important role in boosting operating and business performance for operators.

 

Economic Projections for 2021

Leading forecasters of global GDP growth and energy demand have collectively moderated their short-term and long-term forecasts for next year.

Regional and global GDP growth projections depend on the persistency of the worldwide pandemic. Most projections see Asian markets, such as India and Southeast Asia, recovering and growing faster than the rest of the world. This view depends on the extent to which energy companies and individual governments proceed with sustainability and circular economy goals. Considering the current situation, Southeast Asia will likely prioritize economic recovery with fiscal activities and moderate their focus on sustainability. European and US economies are well-positioned with fiscal reserves to apply government resources to both economic recovery and the energy transition movement in play.

 

Cost Efficiency, Shifting Demands and Other Key Drivers for the Process Industry in Asia

A sharp focus on increasing resource efficiency by energy and chemical companies can be expected, as it reduces costs and carbon footprint. There will also likely be a shift in the production mix in refining towards chemical feedstocks, as chemicals growth is expected to account for half of oil demand growth in coming years. As economies and the middle-class growth regain momentum, projects such as the proposed Indian RRPCL mega project integrating refining and chemicals will address growing and shifting market demands.

There will be a continued evolution from oil to gas – especially with respect to chemical feedstocks and power generation. Demand for electricity will grow and natural gas will fulfill a critical need here.

For EPC and owner operators, concept design is paramount as early design is the most critical and important phase of a plant’s entire lifecycle. Leveraging AI and high-performance computing, designers can now rely on a significantly broader set of data to fine-tune their designs. These decisions have a significant impact on the plant’s capital and operating costs, as well as the overall fit for its intended purpose.

There will also be a need for hybrid models that can bridge real and theoretical information to solve complex problems more accurately and easily. These combined models are easier to create and sustain for longer periods of time. This increases their value for optimizing a variety of operating conditions from catalyst replacement intervals in reactors to separation efficiencies in columns.

For capital projects, estimating transparency across the project team and into the C-Suite is critical to unlock value. It is vital to visualize, analyze, benchmark and share data to increase speed and certainty while managing project risk more effectively. The result is a more agile, collaborative and informed estimating process with fewer surprises for executives.

 

Sustainability Challenge and Opportunity

Across the process industry and EPC players that support it, there will be a transition to more sustainable production and processes. Societal drive towards resource efficiency in general and plastic waste reduction in particular will drive innovation and investment. The largest regional energy players will closely follow the “early movers” in Europe and the Middle East for the most promising technology approaches to carbon capture and reduction in the energy value chain.

Sustainability targets may also be a growth opportunity. A case in point would be chemical companies as they design new products and processes that generate less waste and emissions. Industrial AI will be an important tool in this endeavor, enabling better understanding of how process conditions influence product quality by helping engineers predict emissions and reduce waste. Solutions such as AI-enabled hybrid models can be used to optimize operations, create soft sensors, design new equipment or integrate asset-wide processes like crude-to-chemicals. Such insights not only help companies make progress in their sustainability targets, they also aid the development of new high-performance products.

By adopting the latest innovations, chemical companies can progress toward operations where human and autonomous decision-making work in tandem to achieve the most profitable and sustainability-focused outcome. Companies can respond more effectively to market changes and reposition for new opportunities demanded by the circular economy.

Overall, our observations indicate that customers are seeking technology that will give them greater flexibility and resilience to respond to market conditions. Embedding Industrial AI in our solutions helps our customers compete in the increasingly competitive energy and chemical markets. Industrial AI combines data science and AI with the “first principles” of chemistry and physics and domain expertise to deliver comprehensive business outcomes for the specific needs of the process industry.

 

The Self-Optimizing Plant

At AspenTech, we work closely with our customers to help accelerate the digital transformations that are necessary to thrive in the environment of the future. We deliver the advanced technology that is becoming integral to sustainability, competitive and corporate strategies and unlocks the potential of new business models.

To do this, we are creating industrial software solutions that span functional silos and that are increasingly self-learning, self-adapting and self-sustaining - powered by Industrial AI. This enables new levels of insight and operational guidance, elevates the scope of agility and automation that is possible and is moving the industry towards the vision of the Self-Optimizing Plant, a facility that can automatically respond to changing conditions during operation.

 

To find out more, please read Industrial AI Accelerates Digital Transformation for Capital-Intensive Industries.