Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process |
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| Affiliation: | 1. School of Automation, China University of Geosciences, Wuhan 430074, China;2. School of Information Science and Engineering, Central South University, Changsha 410083, China;3. School of Engineering, Tokyo University of Technology, Hachioji, Tokyo 192-0982, Japan;1. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. School of Metallurgical Engineering. Anhui University of Technology, Ma’anshan 243002, China;3. Institute for Chemicals and Fuels from Alternative Resources (ICFAR), Department of Chemical and Biochemical Engineering, Western University, Ontario, Canada N6A 5B9;4. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China;5. Baosteel Research Institute, Baoshan Iron & Steel Co., Ltd., Shanghai 201900, China;1. School of Minerals Processing & Bioengineering, Central South University, PR China;2. MCC Changtian International Engineering Co. LTD), PR China |
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| Abstract: | Iron ore sintering is one of the most energy-consuming processes in steelmaking. Since its main source of energy is the combustion of carbon, it is important to improve the carbon efficiency to save energy and to reduce undesired emissions. A modeling and optimization method based on the characteristics of the sintering process has been developed to do that. It features multiple operating modes and employs the comprehensive carbon ratio (CCR) as a measure of carbon efficiency. The method has two parts. The first part is the modeling of multiple operating modes of the sintering process. K-means clustering is used to identify the operating modes; and for each mode, a predictive model is built that contains two submodels, one for predicting the state parameters and one for predicting the CCR. The submodels are built using back-propagation neural networks (BPNNs). An analysis of material and energy flow, and correlation analyses of process data and the CCR, are used to determine the most appropriate inputs for the submodels. The second part of the method is optimization based on a determination of the optimal operating mode. The problem of how to reduce the CCR is formulated as a two-step optimization problem, and particle swarm optimization is used to solve it. Finally, verification of the modeling and optimization method based on actual process data shows that it improves the carbon efficiency of iron ore sintering. |
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| Keywords: | Back-propagation neural network (BPNN) Carbon efficiency Industrial-process modeling Industrial-process optimization Particle swarm optimization (PSO) |
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