Chemical Process
11 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Neural Component Analysis for Fault Detection
haitaozhao/Neural-Component-Analysis
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Since PCA-based methods assume that the monitored process is linear, nonlinear PCA models, such as autoencoder models and kernel principal component analysis (KPCA), has been proposed and applied to nonlinear process monitoring.
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Decision trees usefully represent sparse, high dimensional and noisy data.
Emergent simulation of cell-like shapes satisfying the conditions of life using lattice-type multiset chemical model
In this study, a 'multiset chemical lattice model', which allows virtual molecules of multiple types to be placed in each cell on a two-dimensional space, was considered.
Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms
The ever-increasing quantity of multivariate process data is driving a need for skilled engineers to analyze, interpret, and build models from such data.
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes
However, manual annotation of large amounts of data can be difficult in industrial settings.
Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory Network
Neural network-based optimization and control methods, often referred to as black-box approaches, are increasingly gaining attention in energy and manufacturing systems, particularly in situations where first-principles models are either unavailable or inaccurate.
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks
Computational efficiency and robustness are essential in process modeling, optimization, and control for real-world engineering applications.
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
ammar-n-abbas/drl-based-decision-support
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These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time.
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
killingbear999/chemical-reactor-foundation-model
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In this work, we present a novel application of foundation models for chemical reactor modeling.
PC-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.
