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- W2991654436 abstract "Sintering is recognized as one of the most energy-intensive process in the iron and steel enterprise. Energy-aware optimization introduces energy indicators into the sintering proportioning process, so that a reduction in energy consumption could be achieved without affecting normal production on the sintering shop floor. This paper proposes a novel cascade multi-objective optimization model (CMOM) that dynamically produces optimal dosing schemes. Firstly, an improved back-propagation neural network (IBPNN) with appending momentum and adaptive variable learning rate is proposed to better mimic solid energy consumption (SEC) and other indictors of sinter. Then, a cost based proportioning model (CBPM) is established with the consideration of chemical and physical characteristics of the resulting sinter. An improved immune-simulated annealing algorithm (ISAA) is designed to fast converge toward optimal solutions. Subsequently, by achieving feasible dosing scheme with initial chemical and physical requirements, energy-aware indicator is introduced into the cascade optimization framework. The CBPM would be repeatedly optimized by adjusting the expected performance until the predicted SEC and other indicators would have been in accord with the expected intervals. A case study of an actual sintering process in steel enterprise demonstrates the effectiveness of the framework and models. Meanwhile, results show that energy consumption is reduced by 5.28% on an optimal scenario." @default.
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- W2991654436 date "2019-10-01" @default.
- W2991654436 modified "2023-09-24" @default.
- W2991654436 title "Energy-Aware Cascade optimization for Proportioning in the Sintering Process Using Improved Immune-Simulated Annealing Algorithm *" @default.
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- W2991654436 doi "https://doi.org/10.1109/smc.2019.8914564" @default.
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