Optimization Design of the Grate Cooler Based on the Power Flow Method and Genetic Algorithms

As an important process during the cement production, grate cooler plays significance roles on clinker cooling and waste heat recovery. In this paper, we measured experimentally the heat balance of the grate cooler, which provided initial operating parameters for optimization. Then, the grate cooler was simplified into a series-connected heat exchanger network by power flow method. Constructing the equivalent thermal resistance network provided the global constraints by Kirchhoff's law. On this basis, with the objectives of the minimum entropy generation numbers caused by heat transfer and viscous dissipation, solving a multi-objective optimization model achieved the Pareto Front by genetic algorithm. Then selecting the scheme of the lowest fan power consumption obtained the optimal operating parameters of the grate cooler. The results showed that the total mass flow of the optimized scheme did not change significantly compared with the original scheme, but the fan power consumption was 25.44% lower, and the heat recovery efficiency was 88.43%, which was improved by 11.35%. Furthermore, the analysis showed that the optimal operating parameters were affected by the local heat load. After optimizing the diameter of clinker particles within the allowable industrial range, the clinker with particle diameter of 0.02 m had the optimal performance.