Papers Chemical Process
“Chemical Process” 태그가 달린 논문 58편 · 필터 해제
LLM-guided Chemical Process Optimization with a Multi-Agent Approach
Chemical process optimization is crucial to maximize production efficiency and economic performance. Traditional methods, including gradient-based solvers, evolutionary algorithms, and parameter grid searches, become imp…
Chemical ProcessComputational EfficiencyEvolutionary AlgorithmsLarge Language ModelNonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Computationally cheap yet accurate enough dynamical models are vital for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction t…
Chemical ProcessAutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams
Recent advancements in generative AI have accelerated the discovery of novel chemicals and materials; however, transitioning these discoveries to industrial-scale production remains a critical bottleneck, as it requires …
Chemical ProcessEconomic data-enabled predictive control using machine learning
In this paper, we propose a convex data-based economic predictive control method within the framework of data-enabled predictive control (DeePC). Specifically, we use a neural network to transform the system output into …
Chemical ProcessOnline Fault Detection and Classification of Chemical Process Systems Leveraging Statistical Process Control and Riemannian Geometric Analysis
In this work, we study an integrated fault detection and classification framework called FARM for fast, accurate, and robust online chemical process monitoring. The FARM framework integrates the latest advancements in st…
Chemical ProcessFault DetectionRule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs
A piping and instrumentation diagram (P&ID) is a central reference document in chemical process engineering. Currently, chemical engineers manually review P&IDs through visual inspection to find and rectify errors. Howev…
Chemical ProcessOn the Implementation of a Bayesian Optimization Framework for Interconnected Systems
Bayesian optimization (BO) is an effective paradigm for the optimization of expensive-to-sample systems. Standard BO learns the performance of a system $f(x)$ by using a Gaussian Process (GP) model; this treats the syste…
Bayesian OptimizationChemical ProcessFuzzy Model Identification and Self Learning with Smooth Compositions
This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model…
Chemical ProcessSelf-LearningLyapunov-based reinforcement learning for distributed control with stability guarantee
In this paper, we propose a Lyapunov-based reinforcement learning method for distributed control of nonlinear systems comprising interacting subsystems with guaranteed closed-loop stability. Specifically, we conduct a de…
Chemical Processreinforcement-learningReinforcement LearningGraph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
Control structure design is an important but tedious step in P&ID development. Generative artificial intelligence (AI) promises to reduce P&ID development time by supporting engineers. Previous research on generative AI …
Chemical ProcessGraph Neural NetworkPC-Gym: Benchmark Environments For Process Control Problems
PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemical processes, incorporating nonlinear d…
BenchmarkingChemical ProcessModel Predictive ControlReinforcement Learning (RL)Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
Neural networks (NN) are extensively studied in cutting-edge soft sensor models due to their feature extraction and function approximation capabilities. Current research into network-based methods primarily focuses on mo…
Chemical ProcessregressionSelf-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling
In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koop…
Chemical ProcessPhysics-informed machine learningMachine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly depends on an appropriate nonlinear mappin…
Chemical ProcessModel Predictive ControlApproximating arrival costs in distributed moving horizon estimation: A recursive method
In this paper, we present a new approach to distributed moving horizon estimation for constrained nonlinear processes. The method involves approximating the arrival costs of local estimators through a recursive framework…
Chemical ProcessTowards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
Developing accurate models for chemical reactors is often challenging due to the complexity of reaction kinetics and process dynamics. Traditional approaches require retraining models for each new system, limiting genera…
Chemical ProcessMeta-LearningTransfer LearningGeneration of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network
Structural decoupling has played an essential role in model-based fault isolation and estimation in past decades, which facilitates accurate fault localization and reconstruction thanks to the diagonal transfer matrix de…
Chemical ProcessDiagnosticFault DetectionFault Diagnosis+2Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain
Resolving coreference and bridging relations in chemical patents is important for better understanding the precise chemical process, where chemical domain knowledge is very critical. We proposed an approach incorporating…
Chemical ProcessMulti-Task LearningData-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process models. By exploiting process operation data, both process modeling and state estimation…
Chemical ProcessState EstimationPartition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formulate a linear full-information estimation …
Chemical ProcessState Estimation