Scaling up new
energy technologies is often where they end up failing, failing to cross the
so-called “valley of death.” While many of these new technologies seem promising,
it is typically scale-up costs and logistics that end up sinking them. Despite
government support and “moon shots” to speed up tech development, the bottom
line is if they can’t scale up in a way that makes them economically
competitive with existing technologies, then they will likely fail.
Scientists at
Lawrence Livermore National Laboratory (LLNL) and collaborators noted in a new paper
in Nature Chemical Engineering, that early de-risking can be improved. According
to TechXplore, the researchers concluded that modeling by multidisciplinary
teams can improve scale-up.
“…early assessments of technology–market fit and how the
physics governing system performance evolves with scale can de-risk technology
development and accelerate deployment.”
"By bringing together technical risk assessments,
scaled physics modeling, data analysis and in situ experimentation within
multidisciplinary teams, new technologies can be invented, developed and
deployed on a shorter timetable with greater probability of success," said
LLNL scientist Andrew Wong, a co-first author of the paper.
They developed
the Laboratory Risk Assessment and Mitigation Protocol (L-RAMP) to assess risks
for technologies that have achieved proof-of-concept demonstrations. The model
addresses barriers along the chain of research, development, demonstration, and
deployment (RDD&D). Wong also noted that better early modeling can help to
avoid later pitfalls that perhaps should have been predicted:
"Advancing through technology scale-up can happen
much faster and more reliably when all of the potential pitfalls have been
evaluated up front."
The goal is faster and more reliable RDD&D and more
successful deployments. The team utilized L-RAMP for LLNL projects in
electrolyzer, membrane, capsule, battery and characterization technologies,
with external commercial partners. A diverse and multidisciplinary approach
aided by AI/machine learning is the emerging paradigm of the modeling, as TechXplore
notes:
"AI is useful in process monitoring and control,
defect detection and mitigation, accelerating complementary physics-based
simulations and modeling via surrogate models, and multimodal data processing
that can be integrated into the techno-economic evaluation," Giera said.
"I anticipate more of these AI-centric capabilities are possible with
increasing technical maturity of the physical technologies."
The paper's authors
also emphasize that along with technical challenges, there are market challenges
that must be addressed since those are what cause many of the endeavors to ultimately
fail. Thus, more and better integration with existing industries and markets would
be beneficial to improving technology scale-up. Climate technology and
industrial decarbonization are the main areas where this is needed. The authors
emphasize that market fit is a key factor in whether these technologies will
succeed or not.
The abstract of
the paper and some modeling graphics are shown below.
Abstract
Avoiding the worst effects of climate change depends on
our ability to scale and deploy technologies faster than ever before. Scale-up
has largely been the domain of industrial research and development teams, but
advances in modeling and experimental techniques increasingly allow early-stage
researchers to contribute to the process. Here we argue that early assessments
of technology market fit and how the physics governing system performance
evolves with scale can de-risk technology development and accelerate
deployment. We highlight tools and processes that can be used to assess both
these factors at an early stage. By bringing together technical risk
assessments, scaled physics modeling, data analysis and in situ experimentation
within multidisciplinary teams, new technologies can be invented, developed and
deployed on a shorter timetable with greater probability of success.
References:
Accelerating
climate technologies through the science of scale-up. Thomas Moore, Andrew A.
Wong, Brian Giera, Diego I. Oyarzun, Aldair E. Gongora, Tiras Y. Lin, Wenqin
Li, Tracie Owens, Du Nguyen, Victoria M. Ehlinger, Aditya Prajapati, Seung Whan
Chung, Pratanu Roy, Joshua DeOtte, Nicholas R. Cross, Alvina Aui, Youngsoo
Choi, Maxwell Goldman, Hui-Yun Jeong, Congwang Ye, Amitava Sarkar, Eric B.
Duoss, Christopher Hahn & Sarah E. Baker. Nature Chemical Engineering
volume 1, pages731–740 (2024). Accelerating climate
technologies through the science of scale-up | Nature Chemical Engineering
Ramping
up the scale of climate and energy technology: Experts recommend technical risk
assessment strategies. Ashley Piccone. TechXplore. January 7, 2035. Ramping
up the scale of climate and energy technology: Experts recommend technical risk
assessment strategies
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