Lin Y P, Huang G H, Lu H W, He L
Shenzhen Municipal Engineering Design Institute, Shenzhen 518035, PR China.
Waste Manag. 2008;28(8):1375-85. doi: 10.1016/j.wasman.2007.09.016. Epub 2007 Nov 26.
A multi-component modeling system was developed to simulate substrate degradation and oxygen consumption in waste composting processes. Levels of soluble substrate (Ss), insoluble substrate (Si), active biomass (X), inert material, moisture, temperature, and oxygen concentration were considered as state variables. The relationships among these variables were also incorporated within the modeling framework. Three conversion reactions, including growth of aerobic biomass, decay of aerobic biomass, and solubilisation of insoluble substrate, were considered in the simulation system. The modeling inputs included temperature, moisture, oxygen concentration, and initial conditions of the state variables, while the outputs included oxygen uptake accumulation (OUA), oxygen uptake rate (OUR), Ss, Si, and X for representing the substrate degradation and oxygen consumption status. The effectiveness of the developed model was demonstrated through its application to a case study in a 30L vessel over 200h. Through verification-based composting experiments, it was shown that the modeling solutions were consistent with the experimental results with an acceptable accuracy level. Sensitivity analyses of the model showed that an increased maximum microbial growth rate would result in raised OUA, OUR, Ss, and X levels; a decreased biomass decay rate constant would help enhance the composting process. Moreover, variations in the maximum growth rate would affect the composting process more significantly than those of the biomass decay rate constant.
开发了一个多组分建模系统,以模拟废物堆肥过程中底物的降解和氧气消耗。可溶性底物(Ss)、不溶性底物(Si)、活性生物量(X)、惰性物质、水分、温度和氧气浓度水平被视为状态变量。这些变量之间的关系也纳入了建模框架。模拟系统考虑了三个转化反应,包括好氧生物量的生长、好氧生物量的衰减和不溶性底物的溶解。建模输入包括温度、水分、氧气浓度和状态变量的初始条件,而输出包括氧气吸收累积量(OUA)、氧气吸收速率(OUR)、Ss、Si和X,用于表示底物降解和氧气消耗状态。通过将开发的模型应用于一个30L容器中200小时的案例研究,证明了该模型的有效性。通过基于验证的堆肥实验表明,建模结果与实验结果一致,具有可接受的准确度水平。模型的敏感性分析表明,最大微生物生长速率的增加将导致OUA、OUR、Ss和X水平升高;生物量衰减速率常数的降低将有助于增强堆肥过程。此外,最大生长速率的变化对堆肥过程的影响比生物量衰减速率常数的变化更显著。