Accounting for erroneous model structures in biokinetic process models
2020
Villez, k. | Del Giudice, D. | Neumann, M.B. | Rieckermann, J.
In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. In this work, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design. © 2020 Elsevier Ltd
Показать больше [+] Меньше [-]Marc B. Neumann acknowledges financial support provided by the Spanish Government through the BC3 María de Maeztu excellence accreditation 2018–2022 (MDM-2017-0714) and the Ramón y Cajal grant (RYC-2013-13628); and by the Basque Government through the BERC 2018-2021 program.
Показать больше [+] Меньше [-]Ключевые слова АГРОВОК
Библиографическая информация
Эту запись предоставил Universidad del País Vasco
