Results 201 to 210 of about 695,878 (301)

Hybrid modeling for optimizing cell cultivation in mAb production using a small‐scale automated experimental platform

open access: yesAIChE Journal, EarlyView.
Abstract This work presents the optimization of cell cultivation for monoclonal antibody (mAb) production. We developed a hybrid model describing the effects of multiple process variables on antibody productivity and impurity generation. An automated platform with 12 × 250 mL bioreactors was set up.
Kosuke Nemoto   +6 more
wiley   +1 more source

Stochastic optimization of Power‐to‐Methanol: Production cost sensitivity to process design vs. scheduling

open access: yesAIChE Journal, EarlyView.
Abstract Under time‐varying electricity prices, the production costs of Power‐to‐X processes with intermediate storage can be reduced by simultaneously optimizing the process unit design and size with their scheduling and operation. However, the production cost sensitivity to optimal process design or scheduling is unclear, especially when several ...
Simone Mucci, Dominik Bongartz
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

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai   +8 more
wiley   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

open access: yesAdvanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary   +1 more
wiley   +1 more source

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