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June 23, 2025, 1:36 p.m.
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(Last updated: June 23, 2025, 1:37 p.m.)
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| ['Li M. High-Performance Computing and AI in Process Engineering 2025.pdf'] | 0 bytes |
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SOURCE: Li M. High-Performance Computing and AI in Process Engineering 2025
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COVER
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MEDIAINFO
Textbook in PDF format High-performance computing (HPC) and Artificial Intelligence (AI) in process engineering involve complex system modelling, data analysis, optimization design, and real-time monitoring. Key methods include data integration, model construction, optimization algorithms, Machine Learning, Deep Learning, parallel computing, and real-time analytics. These techniques significantly enhance production efficiency, reduce costs, and improve system stability. They also promote industrial intelligence, creating new opportunities and challenges in process engineering. This integration supports the advancement of Industry 4.0 and smart manufacturing. Machine Learning (ML) has shown great potential in the process systems engineering (PSE) domain due to its ability to analyse data and make predictions for complex systems. Therefore, the integration of ML with PSE has shown tremendous potential, offering intelligent and efficient solutions to the challenges associated with industrial manufacturing processes. This chapter focuses on the development of Reinforcement Learning (RL) schemes for optimal control of nonlinear chemical processes. Specifically, we start with a brief introduction of three popular ML methods—supervised learning, unsupervised learning, and reinforcement learning—in PSE, and provide a review of RL algorithms. We will focus on RL and discuss safety and process modeling issues in the context of RL. Key Features Provides a systematic review of state-of-the-art Artificial Intelligence technologies and high-performance computing, and their applications in process engineering Introduces the development of traditional process simulators in process engineering and new numerical solvers based on data-driven and physics-informed neural networks approaches Provides perspectives of high-performance computing and Artificial Intelligence from industrial leaders in software and hardware Aimed at researchers and industrial practitioners in process engineering, manufacturing, Data Science, Artificial Intelligence and high-performance computing. Preface Artificial intelligence and the future of process engineering Machine learning in optimal control and process modeling Graph-based control invariant set approximation and its applications Machine learning-based multiscale modeling and control of Quantum dot manufacturing and their applications The rise of time-travelers: are transformer-based models the key to unlocking a new paradigm in surrogate modeling for dynamic systems? Optimization-based algorithms for solving inverse problems of parabolic PDEs Deep learning-based approach for solving forward and inverse partial differential equation problems An active subspace based swarm intelligence method with its application in optimal design problem Supercomputing and machine-learning-aided optimal design of high permeability seawater reverse osmosis membrane systems Supercomputing-based inverse identification of high-resolution atmospheric pollutant source intensity distributions Enhancing boiling heat transfer via model-based experimental analysis
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