Multi-objective optimization of cooling air distribution of grate cooler with different inlet temperatures by using genetic algorithm
The paper discussed a multi-objective optimization model of the cooling air distribution of a grate cooler according to the analogy of a cross-flow heat exchanger and entropy-generation minimization analysis. The modified entropy generation numbers caused by heat transfer and flowing resistance are regarded as objective functions, and are simultaneously optimized by a genetic algorithm. A test is conducted to validate the model. The final optimal cooling air distribution is decided by energy consumed through cooling fans minimization. Next, two schemes are investigated for inducing exhaust air of 70°C–100°C in a chimney to cool the middle and front parts of the grate cooler. Based on each scheme, sensitive analyses of cooling air inlet temperatures are conducted. The results show that the minimum energy consumption of cooling fans increases by 121.04%, and the thermal efficiencies of the grate cooler vary no more than 1.66% when inducing the exhaust air into the middle part of the grate cooler. The minimum energy consumption of cooling fans slightly varies and thermal efficiencies of grate coolers vary no more than 0.51% when inducing exhaust air to the front part of the grate cooler. It is more effective and economical to induce exhaust air at any temperature to the front part of the grate cooler.
Keywordscooling air distribution grate cooler multi-objective genetic algorithm entropy generation
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