Simultaneous digital twin identification and signal-noise decomposition through modified generalized sparse identification of nonlinear dynamics

Wang, Jingyi; Moreira, Jesús; Cao, Yankai; Gopaluni, R. Bhushan

doi:10.1016/j.compchemeng.2023.108294

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…It exploits the observation that the majority of dynamical systems exhibit a limited number of significant terms. This method utilized in various applications such as deducing biological models (Mangan et al, 2016), simulating and optimizing microalgal and cyanobacterial photo-production processes (Zhang et al, 2020), reconstructing chaotic and stochastic dynamical systems (Nguyen et al, 2020), physicsinformed learning (Corbetta, 2020), modeling a biological reactor (Lisci et al, 2021), identifying the governing model of COVID-19 (Ihsan, 2021), predicting blood glucose levels (Joedicke et al, 2022), modeling air pollutants (Rubio-Herrero et al, 2022), identifying digital twin systems (Wang et al, 2023), determining water distribution systems (Moazeni and Khazaei, 2023), and modeling bacterial zinc response (Sandoz et al, 2023).…”

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confidence: 99%

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

Baydaroğlu,

Yeşilköy,

Dave

et al. 2024

Preprint

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Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public health, recreational services, ecological balance, wildlife, fisheries, microbiota, water quality, and economics. HABs are caused by many sources, such as water pollution based on agricultural activities, wastewater treatment plant discharges, leakages from sewer systems, natural factors like pH and light levels, and climate change impacts. While many causes of HABs are recognized, it is unknown how toxin-producing algae develop as well as the key processes and components that contribute to their weight due to the distinct algal dynamics of each lake and the variety and unpredictability of the conditions influencing these dynamics. Modeling HABs in a changing climate is essential for achieving sustainable development goals regarding clean water and sanitation. However, the lack of consistent and adequate data on HABs is a significant challenge for all these studies. In this study, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines sparse regression and machine learning methods to reconstruct the analytical representation of a dynamical system. Moreover, a model-driven and web-based interactive tool was created to disseminate and develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. Users can easily share images of HABs in lakes on an interactive and user-friendly platform, allowing others to view the status of the lakes.

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