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Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes

The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization and to drive industrial syste...

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Formato: Online
Idioma:inglês
Publicado em: MDPI - Multidisciplinary Digital Publishing Institute 2025
Assuntos:
hot strip rolling
looper
EPAE
production stability
root cause traceability
work sampling
observations
analysis
proportions
correlations
interdependence between activities
process choreography
IOBP
data-driven
process mining
discovery
maritime emergency rescue
intelligent navigation
path planning
A* algorithm
B-spline interpolation
regional search
modeling finite elements (EF)
NHC core
RUM technology
UCK cutting tool
surface quality
stress and strain distribution
chip size
control chart
ATTRIVAR
variability
variance
application
interface
R
Shiny package
load frequency control
switching system
event-triggered
model-free adaptive control
combined economic emission dispatch (CEED)
load shifting
demand side management
crow search algorithm
arithmetic optimization algorithm
hybrid energy storage system
adaptive sliding-mode controller
battery degradation
supercapacitor
Zeta converter
vibration
intelligent control
piezoelectric structures
H2criterion
H-infinity criterion
ideal solution
sequential three-way decisions
VIKOR method
domain
muti-attribute decision making
sliding mode control
data-driven techniques
systematic literature review
intelligent algorithms
applications
data-driven modeling
industrial processes
machine learning algorithms
optimization algorithms
adaptive learning
thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology
Acesso em linha:ONIX_20250220_9783725829859_515
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Resumo:The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas within this scope include data-driven modeling, intelligent data representation, integrated/hybrid modeling, machine learning and optimization, advanced machine learning algorithms, hybrid models with optimization algorithms, adaptive learning algorithms, intelligent process monitoring, real-time data monitoring and analysis, soft sensing technologies, operation mode perception and recognition, decision support systems, intelligent decision support systems, the integration of optimization algorithms, and human–machine collaboration for improved decision making. These powerful intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this Reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field.

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