Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

doi:10.3390/books978-3-0365-6394-7

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Keywords

open-die forging; reinforcement learning; double deep Q-learning; process design; process optimization; RTO; data reconciliation; natural gas processing; economic evaluation; sequential-modular approach; electric vehicle; charging station; photovoltaic; maximum power point tracking; data-driven; state prediction; fault classification; multi-agent system; parameter acquisition; mechanism model; reinforcement learning; forging machine; continuous stirring reactors; fault detection; T-S fuzzy model; channel fading; performance optimization; steam generator level control; knowledge-informed simplex search method; historical iteration information; iteration termination control; TLSCA-SVM; optimization method; fault diagnosis; transfer learning; porous structure; finite element analysis; CAD; centroidal Voronoi diagram; block cutting; data reconstruction; convex polyhedron (CPH); convex polygon (CPG); path optimization; average material removal rate (AMRR); permanent magnet synchronous motor; characteristic model; linear golden-section adaptive control; model-based design; dividing-wall distillation column; genetic algorithm; temperature inferential control; temperature difference; quality estimator; discrete element method; particle modelling; multi-sphere method; simulation analysis; anti-corrosion pill particle; photovoltaic; leakage current; common-mode voltage; model predictive control; transformerless inverter; H8; weak grid; hyperchaotic complex system; tracking control; fractional order; mathematical modeling; production scheduling; process optimization; advanced optimization methodologies; modeling of industrial processes; VSM; durability; logistics 4.0; methodology; automobile; wiring; wind farms; inspection; path planning; chaotic neural network; genetic algorithm; time delay; bidirectional free-riding; differential game; bilateral cost-sharing decisions; artificial intelligence (AI); music generation; natural language processing; transformer; GANs; green growth; influencing factor; factor identification; DEMATEL method; manufacturing enterprises; resilient dynamically triggering controller (DTRC); dynamically triggering mechanisms (DTMs); denial-of-service (DoS); networked nonlinear systems (NNSs); resilient dynamically event-triggering (RDET); n/a

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Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

Edited by

January 2023

440 pages

ISBN 978-3-0365-6395-4 (Hardback)
ISBN 978-3-0365-6394-7 (PDF)

https://doi.org/10.3390/books978-3-0365-6394-7

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This is a Reprint of the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes that was published in

Engineering

Summary

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors.