The explosive waste materials used in military weapon systems are disposed by incineration through a fluidized bed reactor. In this process, pollutants such as nitrogen oxide (NOx) are inevitably generated. In particular, the reduction of NOx in the atmosphere is essential because it causes acid rain, global warming due to ozone destruction, and smog. Consequently, it is necessary to find the optimal operating conditions that can minimize the NOx emissions in the actual process in which large amounts of NOx are emitted. However, because various uncertainties exist in the actual process, deterministic optimization is difficult. Here, we introduce a robust optimization framework that finds the optimal operating conditions for parametric uncertainties through data-driven polynomial chaos expansion. By operating the incinerator under the optimal operating conditions obtained through this optimization framework, NOx emission was stably reduced despite uncertainties of explosive waste particle conditions; compared to the nominal optimum, the mean of NOx production rate decreased by 13.6–13.9% and the variance decreased by 36.1–36.3%.

中文翻译：

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数据驱动的稳健优化，在过程不确定性下实现最小氮氧化物排放

军事武器系统中使用的爆炸性废料通过流化床反应器进行焚烧处理。在此过程中，不可避免地会产生氮氧化物（NOx）等污染物。特别是，减少大气中的氮氧化物至关重要，因为它会导致酸雨、臭氧破坏导致的全球变暖和烟雾。因此，在大量NOx排放的实际过程中，有必要找到能够最大限度减少NOx排放的最佳运行条件。然而，由于实际过程中存在各种不确定性，确定性优化是困难的。在这里，我们引入了一个强大的优化框架，该框架通过数据驱动的多项式混沌扩展找到参数不确定性的最佳操作条件。通过在该优化框架获得的最佳运行条件下运行焚烧炉，尽管爆炸性废物颗粒条件存在不确定性，但NOx排放量仍稳定降低；与名义最佳值相比，NOx生成率均值下降了13.6%~13.9%，方差下降了36.1%~36.3%。