A hybrid modeling, monitoring and control approach for wastewater treatment plants

目前，废水处理厂的设计使用相当详细的第一原理模型，这些模型能够捕捉过程的主导动态，但不一定非常准确地预测变量。为新厂址校准这些模型仍然是一个重大挑战。缺乏精确的模型意味着这些工厂的可靠监测和最佳运行不容易实现。工厂操作数据变得越来越可用，并且已经努力使用完全数据驱动的方法来对工厂行为进行建模。然而，在数据驱动方法中缺乏第一原理信息使得数据驱动模型易于过拟合并且不适于外推。出于这些考虑，本提案将开发混合模型，将数据驱动方法与第一原理模型无缝协同，以利用第一原理模型中存在的信息。这个项目是一个后续的成功参与赠款的概念证明提出的想法被证明。本建议将建立在真实的案例研究，并推广到显着扩大的应用程序的监测和反馈控制。工业合作伙伴Hydromantis的直接利益是极大地扩大了客户群。鉴于正在开发的工具的广泛性，通过将这一方法适用于从石油化工到制药再到钢铁制造等广泛的生产设施，将给加拿大经济带来额外的好处。

Wastewater treatment plants are currently designed using fairly detailed first principles models that are able to capture the dominant dynamics of the process, but not necessarily predict the variables very accurately. The calibration of these models for a new plant site remains a significant challenge. The lack of precise models means that reliable monitoring and optimum operation of these plants is not readily achieved. Plant operation data is becoming increasingly more available, and efforts have been made to use completely data driven approaches to model the plant behaviour. The lack of first principles information in the data driven approaches, however, makes the data driven models susceptible to overfitting and not amenable to extrapolation. Motivated by these considerations, the present proposal will develop hybrid models that seamlessly synergies data driven approaches with first principles models in a way to leverage the information present in the first principles model. This project is a followup to a successful engage grant where a proof of concept for the proposed idea was demonstrated. The present proposal will build on that with real case studies, and generalize to significantly expanded applications of monitoring and feedback control. The direct benefit to the industrial partner Hydromantis is an immensely broadened clientele. Given the broad nature of the tool being developed, additional benefit to Canadian economy will be realized through adaptation of the approach to a broad ranges of production facilities ranging from petrochemicals to pharmaceuticals to steel manufacturing.