Make Digital Twins an Integral Part of Your Sustainability Program

Summary

Sustainability is gaining in importance for many companies across a wide range of industries. Digital twins and hybrid models are a particularly useful tool to help companies make progress toward their sustainability goals, for operational excellence and integrity and supply chain optimization and planning.

Asset-intensive companies typically address sustainability challenges through three primary levers: resource efficiency, circular economy and energy transition. Short-term projects are often focused toward resource efficiency, working to optimize use of energy, water and feedstock with existing assets. Longer-term efforts are directed toward fundamental changes in process and product technology and new energy sources. The unique demands of the circular economy, where economic activity is decoupled from the consumption of finite resources and waste is re-integrated into processes, is particularly challenging for manufacturers. Many companies are developing innovation strategies to redesign processes and products to eliminate waste and emissions. In addition, the high energy intensity of some industrial processes is forcing a transition toward alternative energies, and investment in new technologies and renewable feedstocks. These shifts are driving a fundamental rethinking of current business models as well as the tools and capabilities required to meet new objectives for success.

The balance between profitability, sustainability and product quality

The complexity of tradeoffs between business profitability, sustainability and product quality is a daunting task in today’s volatile and uncertain markets. The sheer volume of information to be analyzed and the need for agility and scenario development create a unique challenge.

Digital solutions provide the visibility, analysis and insight needed to address the challenges inherent in sustainability goals. A digital twin strategy as part of an overall digitalization plan can be a crucial capability for asset intensive industries such as refining and chemicals. A digital twin needs to encompass the entire asset lifecycle and value chain from design and operations through maintenance and strategic business planning.

Achieving sustainability goals with digitalization

Sabic, one of the largest chemical producers in the Middle East, is focused on gaining manufacturing operating improvement by target-oriented approach for optimization. Sabic launched a sustainability program in 2009 to achieve 25% reduction in greenhouse gas emissions, energy and water consumption and reduce the material loss by 50% by 2025. Digitalization is playing an important role in helping Sabic accelerate their sustainability journey.

One focus is visibility and optimization on utility use, enabling Sabic to observe and then regulate energy and utility losses at the equipment level before it appears at the unit or site level. For example, to optimize a multi-level steam system, the initial identification and improvement was done at boiler feed water turbines, high pressure boilers, pump and air compressor modules. They continue to apply digitalization to achieve their sustainability targets by 2025.

Another chemical company, Brazilian company Oxiteno used a digital twin to improve operating performance to produce a specialty product. The company applied a plant-wide digital twin model to increase efficiencies across the site, integrating disparate models of reactors, columns, strippers, and absorbers to find collective improvement. The company was able to cut steam consumption by 15% while also increasing production capacity by adopting digital solutions.

Supply chain efficiencies gained through digitalization were highlighted as a key tool to improve sustainability progress in a recent report published by the European Petrochemical Association. The report, ‘Digitalisation as an Enabler for a Sustainable Future’, states that “Digital technologies improves supply chain visibility, which offers the opportunity to improve scheduling, to eliminate activities that are not adding value, and to optimize the use of the assets and the resources.”

Perstorp, a European specialty chemical company, has gained significant efficiencies by employing digital solutions to its supply chain activities. The company created an award-winning integrated business planning model that combined demand, inventory and production in a single platform. Such integration gives better visibility and coordination across the supply chain and leads to improved optimization of manufacturing assets.

Sustainability options & transparency with digital twins

Digital twins are thermodynamic first principles-based simulation models that also work as an alternative to sensor and testing approach. Companies can select the best process scheme and equipment to maximize energy efficiency and track emissions of CO₂ and other pollutants and greenhouse gasses while using digital simulations of operational processes.

Bharat Petroleum Corporation Limited (BPCL), an Indian refining company, developed a digital twin emission model for their Kochi refinery to improve monitoring transparency and stay in compliance under dynamic emission rules. Digital Twin Emission Model also helped choose a better fuel mix that can reduce emission keeping a balance with profitability. The refinery team created steady state emission prediction models, which were then validated and connected to data historians for online real-time data collection. Production and operations data were collected and analyzed to create customized visualization dashboards to monitor CO₂, NO_x, CO, SO₂ and other pollutant emissions generated from several refinery sources. Digital dashboards provide enhanced visibility of sustainability metrics for easy charting, thus accelerating progress on sustainability. This reporting increased employee awareness toward sustainability practices and enabled better transparency to outside stakeholders.

Additionally, digital twins provide insights into sustainability options allowing companies to compare existing operations with future alternatives. German multinational BASF has set a target to achieve neutral carbon emissions growth by 2030 even as it continues to grow its sales volume. The company used offline digital twins to help redesign its methanol production process to cut CO₂ emissions. The complex model included three separate steps–partial oxidation of methane; methanol synthesis and distillation; oxyfuel boiler and OASE blue. BASF applied a partial oxidation (POx) process to avoid usage of fired heater and avoided severe process technology risks by using offline digital twins to streamline the process. The digital twin was also used to get a holistic view of the whole process including, POx, MeOH synthesis & distillation, OxyFuel boiler, OASE blue process, BASF’s patented process used to convert carbon components in flue gasses to CO₂, and utility systems.

The role of first principles, AI and hybrid models

Comprehensive sustainability solutions are stretching the capabilities of thermodynamic first principle-based digital twins and driving the need for the next generation of solutions. Reduced order hybrid models offer a critical capability to achieve digitalization, sustainability and business goals faster. Reduced-order models can abstract models to enterprise views which inform executive awareness and strategic decision-making. Site-wide models can run faster and more intuitively to drive agile decision-making and optimize assets to achieve safety, sustainability and profit.

Hybrid models combine artificial intelligence (AI) and first principles to deliver a comprehensive, accurate model more quickly without requiring significant expertise. Machine learning is used to create the model leveraging simulation or plant data, while using domain knowledge including first principles and engineering constraints to build an enriched model–without requiring the user to have deep process expertise or become an AI expert. The accuracy of empirical models and the strength of first principles models, leveraging the power of AI along with domain expertise, creates a more predictive model.

Dow faced challenges with delayed model convergence leading to interruptions in determining optimal operating conditions, defining specifications and overall model validation​ for effective plant performance. Dow was able to achieve 36% faster model convergence and >99% accuracy of rigorous models by implementing reduced order hybrid models as surrogate models.

Digital twins can provide companies with a comprehensive solution that can optimize asset performance across multiple dimensions of sustainability, safety and profitability through adaptive models, shared data and advanced visualization.

The circular economy and shifting business priorities

Global efforts to move toward new energy sources and the circular economy will drive a strategic shift in business metrics and the practices that will enable future success. Digital twins are an important tool in this process, enabling improved process design, greater manufacturing insight, and better operational integrity. Many forward-looking companies have already begun this process, investing to build new capabilities and developing innovative technologies and business models to achieve new targets.

The integration of sustainability targets with business goals will be transformational for energy and chemical companies, especially as these topics are being raised in shareholder meetings, by large institutional investors and by the media. Furthermore, the target-oriented approach also positions businesses toward renewed growth and long-term business success. The Ellen MacArthur Foundation highlights that products developed for the circular economy offer as much as $1 trillion in new business opportunity for companies that meet these objectives.

Achieving the fragile balance of sustainability goals—equally considering people, planet and profit—is a considerable challenge, but one that must be addressed to be competitive in the energy and chemical markets of today and tomorrow. Digital technologies and Industrial AI will take center stage during this transition, enabling the capabilities that will separate the leaders from niche players.