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2024 | Book

Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization


About this book

This book examines biomass mixture modeling and optimization.

The book discusses anaerobic digestion and related fermentative processes and explains their compositional dynamics. Early chapter examine macromolecules, elemental fractions, and their direct influence on methane production. Supported by an extensive data bank of substrates obtained from research, the book points out correlations that enable the estimation of global methane production for diverse biomass mixtures. Furthermore, it provides valuable insights into discerning the optimal composition capable of yielding the utmost methane output.

The book integrates cutting-edge machine learning techniques and shows how the programming language Python and Julia can be used for analysis and to optimize processes. It has many graphs, figures, and visuals.

Table of Contents

Chapter 1. Substrate General Characteristics
A key point in fermentative processes is the Feedstock composition. It can be divided into two categories: biomass as inoculum or fresh inflow to the process. The inoculum is essential for a stable and productive process, since, if well-tailored, it provides the right amount of microorganisms to run the process (Rajput and Sheikh in Sustain Environ Res 29(1):4, 2019; Yarberry et al. in Waste Manag 87:62-70, 2019). Consequently, setting up a proper biomass is a crucial factor in a productive scenario. It is then necessary to understand and look for its best characteristics for the purpose.
Federico Moretta, Giulia Bozzano
Chapter 2. Database Introduction
Statistical methods should always be applied in the presence of enough amount of data. In this book, the data used have been taken gathering a conspicuous amount of information from the literature, and resumed in the work of Moretta et al. (J Clean Prod 375:134140, 2022). Then, these have been collected in a single database, which structure and content are described in the next sections.
Federico Moretta, Giulia Bozzano
Chapter 3. Statistical Analysis
It is important to discover the connections undergoing between data in order to asses complete and robust mathematical relations (Quinn et al. in Bioinformatics 34(16):2870–2878, 2018).
Federico Moretta, Giulia Bozzano
Chapter 4. Synergisms and Antagonisms of Biomasses
Anaerobic Co-Digestion consists of the simultaneous digestion of two or more substrates. This technology has gained a lot of attention nowadays because it gives the possibility to significantly improve process performances (Croce et al. in Biotechnol Adv 34(8):1289–1304, 2016; Xie et al. in Bioresour Technol 222:498–512, 2016). Indeed, the digestion of a single substrate might lead to poor outcomes in terms of substrate utilization and methane yield due to the lack of some nutrients or non-optimal parameters. By co-digesting different substrates together, that show “complementary” characteristics, instead, methane yield and process stability can be significantly improved, and synergistic effects may be observed too. On the other hand, an improper choice of co-substrates could lead to a system imbalance and create antagonistic effects, reducing the methane generation to mono-digestion (Jain et al. in Renew Sustain Energy Rev 52:142–154, 2015; Siddique and Wahid in J Clean Prod 194(1):359–371, 2018).
Federico Moretta, Giulia Bozzano
Chapter 5. Stability Parameters
The FOS/TAC parameter is used to monitor process stability by focusing on the pH value. FOS/TAC is defined as the ratio of the amount of volatile fatty acids that accumulate during anaerobic digestion to the alkalinity present in the system (Lili et al. in Analele Univ Din Oradea Fasc Protecția Mediu 17:713–718, 2011). Since volatile fatty acids accumulate during the acidogenesis phase, they can lead to a consumption of alkalinity and consequently a decrease in pH. For this reason, it is of fundamental importance to have a buffer solution that can counteract the pH decrease bringing it back to neutral values, as the optimal pH value for an anaerobic digester is around 7 (Pontoni et al. in Chem Eng Trans 43:2089–2094, 2015).
Federico Moretta, Giulia Bozzano
Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization
Federico Moretta
Giulia Bozzano
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