Results 121 to 130 of about 26,300 (238)

Thermodynamic Database for Zirconium Alloys

, 2006
For many decades zirconium alloys have been commonly used in the nuclear power industry as fuel cladding material. Besides their good corrosion resistance and acceptable mechanical properties the main reason for using these alloys is the low neutron absorption. Zirconium alloys are exposed to a very severe environment during the nuclear fission process
openaire   +1 more source

Design and optimization of processes for recovering rare earth elements from end‐of‐life permanent magnets

AIChE Journal, EarlyView.
Abstract Recovery of rare earth elements (REEs) from end‐of‐life (EOL) products represents a strategic opportunity to strengthen the domestic supply chain for rare earth elements. This work presents a superstructure‐based optimization framework for finding the most economical processing pathway for different EOL rare earth permanent magnets (REPMs ...
Chris Laliwala, Oluwamayowa O. Amusat, Akintomiwa Ojo, Alejandro Garciadiego, Thomas Tarka, Debangsu Bhattacharyya, Ana I. Torres   +6 more
wiley   +1 more source

Transient modeling of extraction columns: Parameter estimation, uncertainty analysis, and operation optimization

AIChE Journal, EarlyView.
Abstract Despite extensive modeling efforts in extraction research, transient column models are rarely applied in industry due to concerns regarding parameter identifiability and model reliability. To address this, we analyzed uncertainty propagation from estimated parameters in a previously introduced column model and assessed identifiability via ill ...
Andreas Palmtag, Jannik Dohmen, Andreas Jupke   +2 more
wiley   +1 more source

Graph‐based imitation and reinforcement learning for efficient Benders decomposition

AIChE Journal, EarlyView.
Abstract This work introduces an end‐to‐end graph‐based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network, which takes as input a bipartite graph representation of the master problem and proposes a candidate solution.
Bernard T. Agyeman, Zhe Li, Ilias Mitrai, Prodromos Daoutidis   +3 more
wiley   +1 more source

SUPCRTNE Thermodynamic Database Engine

, 2021
openaire   +2 more sources

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

The Mantle Fe³⁺/ΣFe Ratio Has Doubled Since the Early Archean. [PDF]

Nat Commun
Zhu XX, Duan WY, Gerya T, Zhou X, Tian JC.   +4 more
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Ensemble molecular mimicry correlates with antibody cross-reactivity in proteome-wide studies. [PDF]

Front Immunol
Wrabl JO, Beale J, Fortunato G, Monsalve AVDB, Hilser VJ.   +4 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source