Industrial & Engineering Chemistry Research· 2014Q1
Temperature Modeling in a Coke Furnace with an Improved RNA-GA Based RBF Network
- 41citations
- Q1SCImago
- 2014year
Short summary
An improved RBF network, optimized by an RNA-GA approach incorporating pruning and enumeration, achieves more precise and simpler temperature modeling for industrial coke furnaces.
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Key points
- Developed an improved RBF network for coke furnace temperature modeling.
- Utilized an improved RNA-GA (RNA encoding, pruning, enumeration) for optimizing RBF network structure and parameters.
- Achieved enhanced modeling precision and simplified model structure.
- Validated effectiveness and feasibility using industrial data.
AI-generated from the title and abstract; the full text is not read.
Abstract
The temperature modeling of the coke furnace in industrial coke equipment is not very easy due to disturbances, nonlinearity, and switches of coke towers. For characterizing the temperature dynamics in a coke furnace, a more comprehensive RBF modeling method is presented focusing on improving the modeling precision and simplifying the model structure simultaneously. An improved RNA-GA is then utilized to optimize both the structure and parameters of the RBF neural network. RNA encoding and evolution operations, particularly pruning operation and enumerative method, are introduced, and fitness function is designed to obtain the satisfactory modeling performances. The industrial data is used to demonstrate the effectiveness and feasibility of the proposed modeling strategy.
The authors' abstract, as published at the source. Industrial & Engineering Chemistry Research, 2014 · DOI ↗
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Field: Fuel Technology
Fuel TechnologyEnergy