Given the rapid economic developments, energy-saving and the reduction of carbon dioxide emissions are now recognized as the most important goals worldwide. The most effective energy-saving and emission reduction method is energy efficiency analysis and intelligent optimization, which is widely used in the process industry. Common methods include traditional mechanism methods based on momentum transport, energy transport, quality transport (TT), and reaction engineering (RG) (TT-RG), as well as data-driven artificial intelligence methods. In addition, mechanism methods need clear parameters to complete the modeling, which is a major challenge in the process industry. With the development of artificial intelligence and big data, data-driven artificial intelligence methods have gradually become a widely used modeling tool due to their fast response, without considering internal mechanisms and strong nonlinear approximation ability, which can provide theoretical guidance for energy saving and emission reduction of process industry.
The goal of this research topic aims to build an energy efficiency analysis and optimization model to realize energy savings and the reduction of carbon dioxide emissions by utilizing machine intelligence, deep learning, and other artificial intelligence methods. The advanced AI-based optimization algorithms should be provided to deal with the problem of the optimization process is easy to fall into local optimum and cannot achieve global optimum. The effective data analysis and processing methods, such as dimensionality reduction, matrix completion, and feature extraction are better to propose to solving the problems such as strong coupling, nonlinearity, and missing the data in the process industry. The strong robustness and high generalization prediction model should be built to overcome the problems of low precision and poor generalization in process industry modeling.
We welcome contributions in the form of original research articles and review articles, The topics of interest include, but are not limited to:
1) Energy efficiency analysis and Intelligent optimization in complex industrial processes.
2) Energy efficiency analysis and prediction by using deep learning methods in complex industrial processes.
3) The novel data analysis and processing methods to solve the strong coupling, nonlinearity, and missing the data in the process industry.
Given the rapid economic developments, energy-saving and the reduction of carbon dioxide emissions are now recognized as the most important goals worldwide. The most effective energy-saving and emission reduction method is energy efficiency analysis and intelligent optimization, which is widely used in the process industry. Common methods include traditional mechanism methods based on momentum transport, energy transport, quality transport (TT), and reaction engineering (RG) (TT-RG), as well as data-driven artificial intelligence methods. In addition, mechanism methods need clear parameters to complete the modeling, which is a major challenge in the process industry. With the development of artificial intelligence and big data, data-driven artificial intelligence methods have gradually become a widely used modeling tool due to their fast response, without considering internal mechanisms and strong nonlinear approximation ability, which can provide theoretical guidance for energy saving and emission reduction of process industry.
The goal of this research topic aims to build an energy efficiency analysis and optimization model to realize energy savings and the reduction of carbon dioxide emissions by utilizing machine intelligence, deep learning, and other artificial intelligence methods. The advanced AI-based optimization algorithms should be provided to deal with the problem of the optimization process is easy to fall into local optimum and cannot achieve global optimum. The effective data analysis and processing methods, such as dimensionality reduction, matrix completion, and feature extraction are better to propose to solving the problems such as strong coupling, nonlinearity, and missing the data in the process industry. The strong robustness and high generalization prediction model should be built to overcome the problems of low precision and poor generalization in process industry modeling.
We welcome contributions in the form of original research articles and review articles, The topics of interest include, but are not limited to:
1) Energy efficiency analysis and Intelligent optimization in complex industrial processes.
2) Energy efficiency analysis and prediction by using deep learning methods in complex industrial processes.
3) The novel data analysis and processing methods to solve the strong coupling, nonlinearity, and missing the data in the process industry.
