Activated sludge models (ASMs) are often used in the simulation of the wastewater treatment process to evaluate whether the effluent quality parameters of a wastewater treatment plant meet the standards. The premise of successful simulation is to choose appropriate dynamic parameters for the model. A niche based adaptive invasive weed optimization (NAIWO) algorithm is proposed in this paper to find the appropriate kinetic parameters of activated sludge model 1 (ASM1). The niche idea is used to improve the possibility of convergence to the global optimal solution. In addition, the adaptive mechanism and periodic operator are introduced to improve the convergence speed and accuracy of the algorithm. Finally, NAIWO is used to optimize the parameters of ASM1. Comparison with other intelligent algorithms such as invasive weed optimization (IWO), genetic algorithm (GA), and bat algorithm (BA) showed the higher convergence accuracy and faster convergence speed of NAIWO. The results showed that the ASM1 model results agreed with measured data with smaller errors.

中文翻译：

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基于活性污泥法的废水处理实践中的最优参数估计

活性污泥模型（ASM）常用于模拟废水处理过程，以评估废水处理厂的出水质量参数是否符合标准。仿真成功的前提是为模型选择合适的动态参数。本文提出了一种基于生态位的自适应侵入杂草优化 (NAIWO) 算法，以找到合适的活性污泥模型 1 (ASM1) 动力学参数。利基思想用于提高收敛到全局最优解的可能性。此外，还引入了自适应机制和周期算子，提高了算法的收敛速度和精度。最后使用NAIWO对ASM1的参数进行优化。与侵入性杂草优化（IWO）等其他智能算法的比较，遗传算法（GA）和蝙蝠算法（BA）显示了 NAIWO 更高的收敛精度和更快的收敛速度。结果表明，ASM1模型结果与实测数据一致，误差较小。