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Practical approach to parameter estimation for ASM3+bio-P module applied to five-stage step-feed EBPR process

机译:应用于五阶段分步进料EBPR过程的ASM3 + bio-P模块参数估计的实用方法

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摘要

Various parameter optimization approaches to a five-stage step-feed EBPR process modeled using the ASM3+bio-P module were examined. Five stoichiometric (Y-STO,(NO), Y-H.O2, Y-H.NO, Y-PAO.O2, Y-PO4) and seven kinetic parameters (k(STO), eta(NO), b(H), mu(max),(PAO), q(PHA), q(PP), mu(max,A)) were estimated. The optimization approaches could be classified based on the data sources (batch experiments or CSTR operation data) and the number of target variables used in calculating the objective function. Optimized parameter values obtained by each approach were validated with CSTR operation data that were not used for parameter optimization. The results showed that the parameter optimization only with batch experimental results could not be directly applied to CSTR operation data. ASM3+bio-P module parameters could be finely optimized only with CSTR operation data when sufficient target variables for objective function calculation were applied. When the number of target variables was increased, prediction performance was significantly improved. Once optimized, the model was able to predict the characteristic features of the five-stage step-feed process; namely, a high PAO yield, fast PAO growth, fast X-PP storage, Slow X-STO and X-PHA storage.
机译:研究了使用ASM3 + bio-P模块建模的五阶段分步进料EBPR工艺的各种参数优化方法。五个化学计量比(Y-STO,(NO),YH.O2,YH.NO,Y-PAO.O2,Y-PO4)和七个动力学参数(k(STO),eta(NO),b(H),mu估计(max),(PAO),q(PHA),q(PP),mu(max,A))。可以基于数据源(批生产实验或CSTR操作数据)和用于计算目标函数的目标变量的数量对优化方法进行分类。使用未用于参数优化的CSTR操作数据验证通过每种方法获得的优化参数值。结果表明,仅具有批处理实验结果的参数优化不能直接应用于CSTR操作数据。当应用了足够的目标变量进行目标函数计算时,只有使用CSTR操作数据才能对ASM3 + bio-P模块参数进行优化。当目标变量的数量增加时,预测性能将显着提高。一旦优化,该模型就可以预测五阶段分步进料过程的特征。即,高PAO产量,快速的PAO生长,快速的X-PP存储,慢的X-STO和X-PHA存储。

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