Application of Multi-Objective Optimization for Pollutants Emission Control in an Oil-Fired Furnace

應用多目標最佳化於燃油爐內汙染物排放之控制

AM. Ghasemi ； A. Saeedi ； M. Moghiman

《中國機械工程學刊》 32卷4期 (2011/08) Pp. 303-313

https://doi.org/10.29979/JCSME.201108.0004

摘要

這篇論文主要目的在探討當維持合理的溫度下，煤煙與NOx排放量的減少行為。文中採用Sprint CFD code建立此問題的計算模型，並結合遺傳算則（GA）來求解此多目標最佳化問題。利用Sprint CFD code分析軸對稱圓柱型熔爐中汙染物的排放量、溫度以及化學組成，而一種名為”非優勢排序遺傳演算法”（NSGA-Π）的延伸性遺傳算則被用來做為求解此問題的最佳化工具，藉由此工具，可得到問題之高精度解。訂定目標函數為出口溫度、NOx與煤煙的排放量，設計參數為入口空氣的軸向速度、切向速度、液滴的直徑以及入口空氣的預熱，藉由Sprint NSGA-Π可求得最佳的操作條件。而從結果可得知，當出口溫度在1890～1990K的範圍之間，NOx與煤煙的排放量可保持在標準值之下。

並列摘要

This paper is aimed at the reduction of soot and NOx emissions, while maintaining reasonable temperature. For this goal, a computational model, Sprint CFD code, is incorporated with genetic algorithm (GA) to solve multi-objective optimization problem. Sprint CFD code analyzes the pollutants emissions, temperature and chemical species of the axisymmetric cylindrical furnace. An extended Genetic Algorithm called the ”Non-dominating Sorting Genetic Algorithm” (NSGA-П) is used as an optimizer thanks to its ability to derive high accurate solutions. The target purpose functions are exit temperature, NOx, and soot emissions. The design variables are air inlet axial velocity, air inlet tangential velocity, diameter of droplets and air inlet preheating. The Pareto optimum solutions obtained from Sprint-NSGA-П are very useful to obtain optimal operational conditions. The solution shows the amount of NOx and soot emissions being kept under regulated values while the exit temperature is in the range of 1890 to 1990k.

並列關鍵字

Genetic Algorithm ； Furnace ； NOx ； soot