Advances and opportunities in machine learning for process data analytics

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Reinforcement Learning – Overview of recent progress and implications for process control

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Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures

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A deep reinforcement learning approach for chemical production scheduling

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Big Data Analytics in Chemical Engineering

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Semiconductor manufacturing process control and monitoring: A fab-wide framework

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Nonlinear Autoassociation Is Not Equivalent to PCA

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Process control via artificial neural networks and reinforcement learning

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Concurrent quality and process monitoring with canonical correlation analysis

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Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction

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Dynamic latent variable analytics for process operations and control

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Neural Networks for Intelligent Sensors and Control — Practical Issues and Some Solutions

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Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA

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Output Relevant Fault Reconstruction and Fault Subspace Extraction in Total Projection to Latent Structures Models

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Total PLS Based Contribution Plots for Fault Diagnosis

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Deep learning for pyrolysis reactor monitoring: From thermal imaging toward smart monitoring system

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Total PLS Based Contribution Plots for Fault Diagnosis

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Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks

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Quality-Relevant Monitoring and Diagnosis with Dynamic Concurrent Projection to Latent Structures

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A statistical perspective of neural networks for process modeling and control

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Special issue on big data: Data science for process control and operations

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A Quality-Relevant Monitoring Method for Closed-Loop Industrial Processes with Dual Consideration of Static and Dynamic Analysis

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Stock price prediction using deep learning and frequency decomposition

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Machine learning algorithms for social media analysis: A survey

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Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

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Perspectives on the integration between first-principles and data-driven modeling

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Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

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A hybrid science‐guided machine learning approach for modeling chemical processes: A review

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Applications of machine learning in spectroscopy

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Process topology convolutional network model for chemical process fault diagnosis

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Graph convolutional network soft sensor for process quality prediction

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Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring

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Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review

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Machine learning in process systems engineering: Challenges and opportunities

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A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

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Industrial data science – a review of machine learning applications for chemical and process industries

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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

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Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities

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Semi-supervised learning for industrial fault detection and diagnosis: A systemic review

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Real-time optimization using reinforcement learning

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One step forward for smart chemical process fault detection and diagnosis

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Machine learning for battery research

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Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

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Machine learning in energy storage materials

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Machine learning for industrial sensing and control: A survey and practical perspective

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Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems

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Formulating data-driven surrogate models for process optimization

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Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes

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A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation

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Simplified Granger causality map for data-driven root cause diagnosis of process disturbances

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Investigating the performance of Hadoop and Spark platforms on machine learning algorithms

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A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods

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