Abstract
Abstract Coke ratio for a sintering process is often determined by experience because models of calculating a coke ratio are very complicated, and are hard to be used in practice. This paper presents a three-step optimization method to find a coke ratio that meets the requirements for commercial operations. First, a back-propagation neural network (BPNN) for temperature field of the material layer (TFML) is built to calculate a mass of sinter cake of a sintering process. Then, the energy flow in a sintering process is analyzed, and a theoretical value of the coke ratio is calculated. Finally, the optimization problem for the second portioning phase is formulated that takes into consideration of the conventional constraints, such as material balance, chemical composition, required quality, etc., and a coke ratio constraint based on the theoretical value. This benefits the reduction of CO 2 for the sintering process. Numerical verification has shown the validity of the method.
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