Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

doi:10.3390/books978-3-0365-0689-0

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Hardback

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Keywords

process control; separation; oil and gas; control chart; fuzzy logic; neutrosophic statistic; incomplete data; belief statistic; gamma distribution; microalgae; raceway; control problem; PID; event-based; flexible flow shop scheduling; multi-queue limited buffers; improved compact genetic algorithm; probability density function of the Gaussian distribution; transfer bees optimizer; reinforcement learning; behavior transfer; state-action chains; reactive power optimization; Holmquist–Johnson–Cook constitutive model of briquette; parameter acquisition; split Hopkinson pressure bar experiment; numerical simulation; pressure swing distillation; full-heat integration; acetonitrile; water; distributed model predictive control; steam power plant; steam/water loop; multi-input and multi-output system; loop design; optimal nonlinear adaptive control; memetic salp swarm algorithm; voltage source converter; perturbation observer; hardware experiment; MIMO temperature control in heating process system; pole-zero cancelation; temperature difference; transient response; dead time; slow-mode-based control; multi-input multi-output (MIMO) temperature system; transient response; temperature differences; non-pillar; gob-side entry retaining by roof cutting; close distance coal seams; goaf; stress distribution; semiconductor bonding equipment-grouping method; graph theory; association matrix; CFSFDP algorithm; self-learning; transient response; variable geometry turbocharger; deep reinforcement learning; deep deterministic policy gradient; flexible flow shop; limited buffer; public buffer; Hopfield neural network; local scheduling; simulated annealing algorithm; load identification; EWT; multiscale fuzzy entropy; PNN; Hybrid Attack Graph; Level-of-Resilience; stability; topology; distributed generation (DG); INSGA-II; multi-objective optimization; potential crowding distance; static and dynamic planning; data-driven methods; reinforcement learning; coarse model; refrigeration; tunnel boring machine; roadway supporting; constitutive model; failure criterion; numerical simulation; in-situ monitoring; power systems; complex network theory; Fast–Newman algorithm; link-addition strategy; cascading failures; flotation process; reagent dosage; time series froth image; cumulative distribution function; fault diagnosis; fault classification; fast Fourier transform (FFT); multi-linear principal component analysis (MPCA); uncorrelated multi-linear principal component analysis (UMPCA); additive white Gaussian noises (AWGN); wind turbine systems; deviation control; drilling machine; nonlinear adaptive backstepping controller disturbance observer; parameter uncertainties; wind turbine; energy conversion systems; condition monitoring; fault diagnosis; fault prognosis; resilient control; n/a

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Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

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July 2021

514 pages

ISBN978-3-0365-0688-3 (Hardback)
ISBN978-3-0365-0689-0 (PDF)

https://doi.org/10.3390/books978-3-0365-0689-0

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

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Chemistry & Materials Science

Computer Science & Mathematics

Engineering

Environmental & Earth Sciences

Summary

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors.

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Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

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