A Robust Nonlinear Estimator for a Yeast Fermentation Biochemical Reactor

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

“…Additionally, it demonstrates robustness in handling data noise features and is well-suited for discrete data. These advantages have been highlighted in research conducted by Kaiser et al (2018), Champion et al (2019), Lisci et al (2021), França et al (2022, and Moazeni and Khazaei (2023). As a result of modeling experiments, microcystin (a toxic substance produced by harmful algae), dissolved oxygen (a water pollution parameter), and evaporation (a meteorological variable containing temperature and precipitation information) were selected as the three variables that gave the sparsest equation to be used in the study.…”

“…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).…”