@book{2025:syed:hybride_mo,
title = {Hybride Modellierung für die Vorhersage und Überwachung des Wachstums von Mikroalgen},
year = {2025},
note = {Microalgae hold significant potential for biofuel, biomaterial, and
bio-based chemical production, necessitating advanced modeling
approaches for optimizing cultivation. This dissertation evaluates
machine learning (ML) models, specifically Long Short-Term Memory
(LSTM) and Support Vector Regression (SVR), against traditional Monod
and Haldane models for predicting microalgae growth under varying
light conditions in outdoor flat-panel airlift photobioreactors.
Contents Abbreviations IX 1. Introduction 1 1.1. Motivation . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Objectives . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.
Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 1.4. Overview of the dissertation . . . . . . . . . . . . . . . . .
. . . 6 2. Literature Review 9 2.1. Factor affecting the microalgae
growth . . . . . . . . . . . . . . 9 2.1.1. Light . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 10 2.1.2. Nutrients . . . . . . .
. . . . . . . . . . . . . . . . . . . 10 2.1.3. Temperature . . . . .
. . . . . . . . . . . . . . . . . . . 11 2.1.4. pH . . . . . . . . . .
. . . . . . . . . . . . . ...},
edition = {1},
publisher = {VDI Verlag},
address = {Düsseldorf},
series = {Rechnerunterstützte Verfahren},
volume = {482},
author = {Syed, Tehreem}
}