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Resilient Energy and Manufacturing Supply Chains

Disaster Resilience and Recovery

Disaster-Resilient Design of Manufacturing Facilities Through Process Integration

A team of Energy Institute researchers, along with colleagues from Qatar University, recently published an article in Frontiers in Sustainability’s Specialty Section of Sustainable Chemical Process Design titled “Disaster-Resilient Design of Manufacturing Facilities Through Process Integration: Principal Strategies, Perspectives, and Research Challenges,” which can be read at

The objective of the article is to provide perspectives on the use of process integration for developing disaster-resilient designs of industrial plants with a focus on the process industries (e.g., chemical, petrochemical, oil, gas, specialty chemicals, pharmaceuticals, biorefining).

The research team identified 12 principal strategies for creating disaster-resilient designs in process industries: (1) Fail-safe by design, (2) Redundancy, (3) Reconfigurability, (4) Modularity/Mobility/Distributability, (5) Repurposability, (6) Flexibility, (7) Controllability, (8) Reliability, (9) Recoverability/restorability, (10) Rapidity, (11) Robustness, and (12) Resourcefulness.

Resilience Enhancement and Disaster-Impact Interception (READII) in the Manufacturing Sector

Sponsored by a National Science Foundation Engineering Research Center Planning Grant in 2018, the Texas A&M Energy Institute and participants from five other major universities in the US Gulf Coast Region (The University of Texas at AustinLouisiana State UniversityMississippi State UniversityTuskegee University, and Florida Atlantic University) embarked on an effort, called READII, to anticipate, mitigate, and respond to the devastating impacts of natural disasters on manufacturing value chains while protecting surrounding communities.

This group hosted five workshops in 2019 across the Gulf Coast Region with more than 100 representatives of stakeholder groups to discuss industrial and societal needs, possible strategies, and the potential impacts of READII. Beyond the workshops, the READII team carried out retrospective analyses using data from previous disasters. While estimates varied, the consensus was that about 25% of the economic losses resulting from recent hurricanes were caused by disruptions in production and services, casualties, and spillages associated with manufacturing value chains. Significantly, 40% of these losses could have been averted with the resilient technologies, planning, mitigation, and advanced decision-making methodologies and tools proposed by READII.This group continues to work together to develop a holistic, innovative, convergent, and scientifically-driven platform, one that enables the creation of disaster-forecasting models, resilient technologies, decision-making frameworks, methodologies and tools, and pre-and post-disaster planning and response strategies.

Smart Manufacturing

Clean Energy Smart Manufacturing Innovation Institute (CESMII)

Smart Manufacturing For Chemical Processing: Energy Efficient Operation of Air Separation Unit

This project, led by the Energy Institute, brings together Linde, Process Systems Enterprise, AspenTech, OSISoft, Rensselaer Polytechnic Institute, and the University of Texas at Austin to develop Smart Manufacturing (SM) Platform-ready tools for the reliable, profitable, and energy-efficient operation of a cryogenic air separation unit (ASU). An ASU is a complex and energy-intensive process. Often, these plants perform sub-optimally, resulting in a loss of energy efficiency. For Linde in the US, each 1% in suboptimal operation is worth about ~$10 million per year.

The SM Platform tools under development aim to improve the operation of an ASU and include i) digital twins of the ASU on three different software platforms, ii) a surrogate modeling app to reduce the complexity of the digital twins so they can be embedded in the rest of the apps, iii) an integrated scheduling and model predictive control app to optimize plant operation, and iv) a fault detection app to predict and eliminate plant faults.

As of February 2021, the ASU digital twins are complete and validated against real plant data, and have been used to create surrogate models for both the scheduling and control apps. A first version of the scheduling, control, and fault detection apps has been created. The data infrastructure was also completed, with the paths for data transfer between the plant, the platform, and the apps implemented. In the next quarter, the scheduling and control apps will be integrated and tuned, while the fault detection app will be finalized. The team will also test these apps in a cyber-physical environment through the digital twins, and eventually deploy them to efficiently operate one of Linde’s air separation units. 

Energy Digitization and Data Sciences for Energy

While decarbonization and electrification transform the future of the energy sector, the proliferation of sensors, communication, computing, and predictive & control capabilities, or termed as “digitization,” will undoubtedly change the way we convert and deliver energy services for the future.

The Texas A&M Energy Institute Initiative on Energy Digitization brings together the entire Texas A&M University community across science, technology, policy, and law, to contribute towards the digitization of the energy sector in Texas and beyond. 

We provide education, research, and services to the state of Texas and beyond in addressing the potential opportunities and challenges of energy sector digitization.