Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years,...

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Vydáno v:Processes Ročník 12; číslo 1; s. 189
Hlavní autoři: Tian, Huixin, Zhao, Chenning, Xie, Jueping, Li, Kun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2024
Témata:
Air quality management
Algorithms
Artificial intelligence
Big Data
Control theory
Correlation
Decision making
Drift
Efficiency
Energy consumption
Environmental changes
Forecasts and trends
Industrial equipment
Industrial production
Mathematical optimization
Multiple objective analysis
Multivariate analysis
Nonlinear systems
Nonlinearity
Objectives
Optimization
Optimization models
Research methodology
State-of-the-art reviews
Time series
New York
ISSN:2227-9717, 2227-9717
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Abstract As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.
AbstractList As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.
Audience Academic
Author Li, Kun
Zhao, Chenning
Tian, Huixin
Xie, Jueping
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  givenname: Jueping
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  fullname: Xie, Jueping
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  givenname: Kun
  surname: Li
  fullname: Li, Kun
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Snippet As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and...
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SubjectTerms Air quality management
Algorithms
Artificial intelligence
Big Data
Control theory
Correlation
Decision making
Drift
Efficiency
Energy consumption
Environmental changes
Forecasts and trends
Industrial equipment
Industrial production
Mathematical optimization
Multiple objective analysis
Multivariate analysis
Nonlinear systems
Nonlinearity
Objectives
Optimization
Optimization models
Research methodology
State-of-the-art reviews
Time series
Title Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy
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Volume 12
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