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首页> 外文期刊>Water Research >Sequential Modeling Of Fecal Coliform Removals In A Full-scale Activated-sludge Wastewater Treatment Plant Using An Evolutionary Process Model Induction System
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Sequential Modeling Of Fecal Coliform Removals In A Full-scale Activated-sludge Wastewater Treatment Plant Using An Evolutionary Process Model Induction System

机译：进化过程模型诱导系统对大型活性污泥污水处理厂粪便大肠菌群去除的顺序建模

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摘要

We propose an evolutionary process model induction system that is based on the grammar-based genetic programming to automatically discover multivariate dynamic inference models that are able to predict fecal coliform bacteria removals using common process variables instead of directly measuring fecal coliform bacteria concentration in a full-scale municipal activated-sludge wastewater treatment plant. A sequential modeling paradigm is also proposed to derive multivariate dynamic models of fecal coliform removals in the evolutionary process model induction system. It is composed of two parts, the process estimator and the process predictor. The process estimator acts as an intelligent software sensor to achieve a good estimation of fecal coliform bacteria concentration in the influent. Then the process predictor yields sequential prediction of the effluent fecal coliform bacteria concentration based on the estimated fecal coliform bacteria concentration in the influent from the process estimator with other process variables. The results show that the evolutionary process model induction system with a sequential modeling paradigm has successfully evolved multivariate dynamic models of fecal coliform removals in the form of explicit mathematical formulas with high levels of accuracy and good generalization. The evolutionary process model induction system with sequential modeling paradigm proposed here provides a good alternative to develop cost-effective dynamic process models for a full-scale wastewater treatment plant and is readily applicable to a variety of other complex treatment processes.
机译：我们提出了一种进化过程模型归纳系统，该系统基于基于语法的遗传程序来自动发现多元动态推理模型，该模型能够使用常见过程变量来预测粪便大肠菌的去除，而不是直接测量粪便中大肠菌的浓度。规模的市政活性污泥废水处理厂。还提出了一种顺序建模范例，以在进化过程模型诱导系统中得出粪大肠菌群去除的多变量动态模型。它由过程估计器和过程预测器两部分组成。过程估算器用作智能软件传感器，可以很好地估算进水中粪便中大肠菌的浓度。然后，过程预测器会根据过程估算器提供的进水中粪便大肠菌群细菌的估计浓度和其他过程变量，对废水中的粪便大肠菌群细菌浓度进行顺序预测。结果表明，具有顺序建模范式的演化过程模型诱导系统已成功地以明确的数学公式的形式成功地进化了粪便大肠菌群清除的多变量动力学模型，具有较高的准确性和良好的通用性。本文提出的具有顺序建模范式的演化过程模型归纳系统提供了一个很好的选择，可以为大规模废水处理厂开发具有成本效益的动态过程模型，并且很容易应用于各种其他复杂的处理过程。

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