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Chemical Process
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams
Economic data-enabled predictive control using machine learning
The Emergence of Chirality from Metabolism
Online Fault Detection and Classification of Chemical Process Systems Leveraging Statistical Process Control and Riemannian Geometric Analysis
Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs
On the Implementation of a Bayesian Optimization Framework for Interconnected Systems
Fuzzy Model Identification and Self Learning with Smooth Compositions
Lyapunov-based reinforcement learning for distributed control with stability guarantee
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
PC-Gym: Benchmark Environments For Process Control Problems
MAX: Masked Autoencoder for X-ray Fluorescence in Geological Investigation
Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
Approximating arrival costs in distributed moving horizon estimation: A recursive method
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network
Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
Reduced-order Koopman modeling and predictive control of nonlinear processes
Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process
Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
cecilia: A Machine Learning-Based Pipeline for Measuring Metal Abundances of Helium-rich Polluted White Dwarfs
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks
Toward autocorrection of chemical process flowsheets using large language models
BOIS: Bayesian Optimization of Interconnected Systems
Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory Network
Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks
Structured Pruning of Neural Networks for Constraints Learning
Deep Anomaly Detection on Tennessee Eastman Process Data
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Data augmentation for machine learning of chemical process flowsheets
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