Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S0A2, Canada.
Sci Total Environ. 2011 Mar 1;409(7):1243-54. doi: 10.1016/j.scitotenv.2010.12.023. Epub 2011 Jan 22.
It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH₄+-N concentration>Moisture content>Ash Content>Mean Temperature>Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes.
众所周知，C/N 比的变化取决于堆肥过程中的许多状态变量。本研究试图开发一种遗传算法辅助逐步聚类分析（GASCA）方法来描述食品废物堆肥中选定状态变量与 C/N 比之间的非线性关系。使用来自六个台式堆肥反应器的实验数据来证明 GASCA 的适用性。在 GASCA 框架内，GA 搜索指定状态变量和 SCA 内部参数的最佳组合；SCA 建立状态变量与 C/N 比之间的统计非线性关系；为避免不必要和耗时的计算，引入了代理表，节省了约 70%的计算工作量。与传统的 SCA 树相比，获得的 GASCA 聚类树具有更小的尺寸和更高的预测准确性。基于最佳 GASCA 树，GA 选择的状态变量对 C/N 比的影响按降序排列为：NH₄+-N 浓度>水分含量>灰分含量>平均温度>嗜温细菌生物量。这种等级意味着铵氮浓度的变化、相关温度和水分条件、有机物和有效矿物质成分的总损失以及嗜温细菌的活性，是影响所研究的食品废物堆肥中 C/N 比的关键因素。这是首次将 GASCA 应用于堆肥建模，表明更直接的搜索算法可以与 SCA 或其他多元分析方法结合使用，以分析堆肥和许多其他环境过程中的复杂关系。
