Results 181 to 190 of about 38,487 (302)

Consolidation of multiple binary distillation columns for large heat duty savings

open access: yesAIChE Journal, EarlyView.
ABSTRACT The enormous scales of chemical and petrochemical plants present significant challenges in separating and purifying numerous mixed streams generated within a facility, as well as in achieving effective energy utilization and process intensification.
Parikshit S. Kadu, Rakesh Agrawal
wiley   +1 more source

AI in chemical engineering: From promise to practice

open access: yesAIChE 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   +4 more
wiley   +1 more source

Model‐based fault diagnosis and fault tolerant control in safety‐critical chemical reactors: An experimental study

open access: yesAIChE Journal, EarlyView.
Abstract This study investigates a fault‐tolerant control (FTC) approach for continuous stirred‐tank reactors (CSTR), emphasizing the importance of timely interventions to ensure operational safety under fault conditions. A systematic methodology combining residual‐based fault estimation and Dynamic Safety Margin (DSM) monitoring is developed to guide ...
Pu Du   +3 more
wiley   +1 more source

From continuous to interruptible distillation: Flexible electric heating column architecture with fast start‐up

open access: yesAIChE Journal, EarlyView.
Abstract Electrification of distillation offers a promising route to reducing scope‐1 emissions from one of the chemical industry's most energy‐intensive unit operations. However, conventional adiabatic columns are dynamically inflexible: Long, energy‐intensive start‐ups make shutdown and restart impractical under variable electricity prices and ...
Samuel Mercer, Michael Baldea
wiley   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

open access: yesAdvanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas   +4 more
wiley   +1 more source

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