Results 161 to 170 of about 11,444,850 (192)

Process‐informed co‐optimization of liquid air energy storage and data centers under off‐design thermodynamic operation

AIChE Journal, EarlyView.
Abstract The rapid growth of AI data centers (AIDCs) is intensifying stress on power systems and exposing operators to volatile electricity markets. This work develops a process‐informed co‐optimization framework that integrates liquid air energy storage (LAES) with AIDC operation. Unlike black‐box storage models, the proposed formulation captures LAES
Zhicong Fang, Xiaopo Cheng, Hui Geng, Jiaze Ma   +3 more
wiley   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

AIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin, Jules Thibault, Clémence Fauteux‐Lefebvre   +2 more
wiley   +1 more source

Development and experimental validation of a predictive resilient framework for cyber‐physical systems

AIChE Journal, EarlyView.
Abstract Cyber‐physical process systems integrate process dynamics, control, sensing, actuation, and communication, making them vulnerable to attacks that falsify data, alter control actions, or disrupt networks and drive operations toward unsafe states.
Luyang Liu, Ritu Ranjan, Zaman Sajid, Benjamin Wilhite, Costas Kravaris, Faisal Khan   +5 more
wiley   +1 more source

Experimental and numerical study of liquid–liquid systems using the Euler–Lagrange/VOF approach

AIChE Journal, EarlyView.
Abstract Achieving stable liquid–liquid dispersions with controlled droplet size remains a challenge due to the complex interplay of dispersed phase viscosity, hydrodynamic conditions and interfacial interactions. This work presents a validated computational framework for droplet breakage and coalescence in liquid–liquid dispersions with variable ...
Pavel Krýsa, Vojtěch Koutník, Simon Kuhn, Miroslav Šoóš   +3 more
wiley   +1 more source

Enhanced biogas production via dual‐layer model predictive control for thermophilic anaerobic digesters

AIChE Journal, EarlyView.
Abstract Thermophilic anaerobic digestion (AD) is an attractive process due to its higher biogas yield compared to mesophilic digestion. To mitigate its higher heating demand and potential process instability, a dual‐layer control architecture comprising a decision‐making layer and an adaptive model predictive control (MPC) system is proposed.
Ali Moradvandi, Mohammad Amin Ghanavati, Merle de Kreuk, Ralph E. F. Lindeboom   +3 more
wiley   +1 more source

Embedded Morgan Fingerprints for More Efficient Molecular Property Predictions With Machine Learning

Advanced Intelligent Discovery, EarlyView.
High dimensionality of Morgan fingerprints causes overfitting and slow training. Embedded Morgan Fingerprint (eMFP) compresses this encoding while keeping key structural information intact. Tested on five regression models across three databases for HOMO–LUMO gap prediction, eMFP outperforms standard fingerprints, training faster on large, high ...
Emilio Nuñez‐Andrade, Isaac Vidal‐Daza, Rafael Gómez‐Bombarelli, James W. Ryan, Francisco J. Martin‐Martinez   +4 more
wiley   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source