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Chemical Process

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Showing 1–50 of 58 papers

TitleStatusHype
LLM-guided Chemical Process Optimization with a Multi-Agent Approach—0
Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison—0
AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams—0
Economic data-enabled predictive control using machine learning—0
Online Fault Detection and Classification of Chemical Process Systems Leveraging Statistical Process Control and Riemannian Geometric Analysis—0
Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs—0
On the Implementation of a Bayesian Optimization Framework for Interconnected SystemsCode1
Fuzzy Model Identification and Self Learning with Smooth Compositions—0
Lyapunov-based reinforcement learning for distributed control with stability guarantee—0
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence—0
PC-Gym: Benchmark Environments For Process Control ProblemsCode2
Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient—0
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling—0
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes—0
Approximating arrival costs in distributed moving horizon estimation: A recursive method—0
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed AdaptationCode1
Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network—0
Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain—0
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes—0
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes—0
Reduced-order Koopman modeling and predictive control of nonlinear processes—0
Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process—0
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 StrategiesCode0
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering TasksCode0
Toward autocorrection of chemical process flowsheets using large language models—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory NetworkCode1
Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks—0
Structured Pruning of Neural Networks for Constraints Learning—0
Deep Anomaly Detection on Tennessee Eastman Process Data—0
An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis—0
Data augmentation for machine learning of chemical process flowsheets—0
Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data—0
Quantifying constraints determining independent activation on NMDA receptors mediated currents from evoked and spontaneous synaptic transmission at an individual synapse—0
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical ProcessesCode1
A sensitivity-based approach to optimal sensor selection for process networks—0
SFILES 2.0: An extended text-based flowsheet representation—0
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks—0
Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics AlgorithmsCode1
Spatial-temporal associations representation and application for process monitoring using graph convolution neural network—0
Emergent simulation of cell-like shapes satisfying the conditions of life using lattice-type multiset chemical modelCode0
Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks—0
A Grid-Structured Model of Tubular Reactors—0
Machine Learning-Based Soft Sensors for Vacuum Distillation Unit—0
Artificial Neural Network and Its Application Research Progress in Chemical Process—0
Application of Neural Network in Optimization of Chemical Process—0
Subsystem decomposition and state estimation of nonlinear processes with implicit time-scale multiplicity—0
Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems—0
A review of neural network algorithms and their applications in supercritical extraction—0
A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process—0
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