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time-series-3

Chemical Process

3260 papers • 126 benchmarks • 313 datasets

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Geochemistry

Most implemented papers

Neural Component Analysis for Fault Detection

Haitao Zhao•Mon Dec 11 2017

A novel nonlinear method, called neural component analysis (NCA), is proposed, which intends to train a feedforward neural work with orthogonal constraints such as those used in PCA, which shows the superiority of NCA in terms of missed detection rate (MDR) and false alarm rate (FAR).

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Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

R. Misener, Miten Mistry, Dimitrios Letsios, G. Krennrich, Robert M. Lee•Thu Mar 01 2018

This study studies a large-scale, industrially-relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk, and develops several heuristic methods to find feasible solutions, and an exact, branch-and-bound algorithm leveraging structural properties of the gradient- boosted trees and penalties.

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SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

Maksim Golyadkin, Vitaliy Pozdnyakov, L. Zhukov, Ilya Makarov•Tue Aug 16 2022

This paper proposes SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring, and demonstrates the model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults.

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Real-time machine-learning-based optimization using Input Convex Long Short-Term Memory network

Zihao Wang, Zhen Wu, Donghan Yu•Sun Nov 12 2023

A novel Input Convex Long Short-Term Memory (IC-LSTM) network is proposed to enhance the computational efficiency of neural network-based optimization and control in practical applications and demonstrates the superior performance of IC-LSTM-based optimization in terms of runtime.

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Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks

Zihao Wang, P. S. Pravin, Zhen Wu•Sun Jan 14 2024

This work introduces a novel network architecture, termed Input Convex Lipschitz Recurrent Neural Networks (ICLRNNs), which seamlessly integrates the benefits of convexity and Lipschitz continuity, enabling fast and robust neural network-based modeling and optimization.

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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, C. Amazu, Joseph Mietkiewicz, Houda Briwa, Andres Alonzo Perez, Gabriele Baldissone, M. Demichela, Georgios C. Chasparis, John D. Kelleher, M. Leva•Mon Feb 19 2024

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 which resulted in a 95.8% prediction accuracy using hidden Markov model.

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Towards foundation model for chemical reactor modeling: Meta-learning with physics-informed adaptation

Zihao Wang, Zhen Wu•Sun May 19 2024

This work introduces a neural network framework that generalizes across diverse reactor types and rapidly adapts to new chemical processes, leveraging meta-learning to pretrain the model on a broad set of reactor dynamics, enabling efficient adaptation to unseen reactions with minimal data.

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PC-Gym: Benchmark Environments For Process Control Problems

Maximilian Bloor, Jos'e Torraca, I. O. Sandoval, Akhil Ahmed, Martha White, Mehmet Mercangöz, Calvin Tsay, E. A. Rio-Chanona, Max Mowbray•Mon Oct 28 2024

Case studies demonstrate the framework's effectiveness in evaluating RL approaches for systems like continuously stirred tank reactors, multistage extraction processes, and crystallization reactors, and reveal performance gaps between RL algorithms and NMPC oracles.

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Chemical Process | State-of-the-Art