关键词: DO模拟, SBR, 硝化模型, 参数估计, 软测量, 置信区间

Abstract: A novel integrated optimization methodology of global optimization (genetic algorithm) and local (quasi-Newton algorithm) optimization for robust and rapid parameter estimation in the initial ODEs (ordinary differential equations) systems was proposed, and it is of the advantages of both algorithms. This methodology was used successfully to estimate the parameters for dynamic variation process of dissolved oxygen(DO) in the two-step nitrification model suggested, and a high correlation coefficient was reached 0.9955.Accuracy assessment for the results estimated was realized based on a comparison of the confidence regions with those determined by Fisher information matrix and exact directly search.The assessment results indicated that using this method most of the parameters in the two-step model could be reliably estimated and only two did not, showing that it could be a novel inspection method for parameter estimation of dynamic system.Furthermore, DO simulation results could be employed as a tool of soft measurement, which could provide some process information about rapid degradation of COD, oxygen uptake rate, ammonia, nitrite and nitrate in the two-step nitrification model and related parameters estimated from DO data.

Key words: DO simulation, SBR, nitrification model, parameter estimation, soft measurements, confidence region