Machine learning-based modelling of anaerobic digestion stability and productivity optimization by co-digestion - Thesis

Aug 5, 2026

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Anaerobic digestion is presented as an oxygen-free biogas production process driven by a specific bacterial community, whose performance is shaped by synergy and antagonism inside the reactor. The dissertation aims to clarify how these two effects influence process behavior and outcomes. Two stability-related parameters are analyzed: FOS/TAC for alkalinity-focused stability monitoring and OLR as the daily volatile-solids feeding criterion. A machine learning framework then identifies the conjugated substrate that maximizes biomethane yield via co-digestion, using a neural network plus an optimization algorithm.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages108

Reading Time4 h 32 min

AccessFree to download

Contents

Abstract

Abstract in italiano

1 Introduction

1.1. Anaerobic digestion

1.2. State of the art

1.3. Objective of the dissertation

1.4. Available database

2 FOS/TAC

2.1. Meaning and achievement of iso-regions

2.2. Model derivation to calculate FOS/TACmix

2.3. Industrial validation

Machine learning-based modelling of anaerobic digestion stability and productivity optimization by co-digestion TESI DI LAUREA MAGISTRALE IN CHEMICAL ENGINEERING Author: Filippo Alliata Student ID: 10580870 Advisor: Giulia Luisa Bozzano Co-advisor: Federico Moretta Academic Year: 2023-24 Abstract Anaerobic digestion is a reliable technology that, in absence of oxygen and through the action of a specific bacterial community, allows to produce biogas starting from biomasses of different nature. The performances of this process are strongly affected by synergy and antagonism effects formed within the reactor. Their generation depends on the interactions established between the macromolecules of the biomasses and the microorganisms of the system. Because of their importance, the aim of this dissertation is to reach a greater knowledge about the dependence of the anaerobic digestion with respect to these two effects. In such a way as to achieve the previous goal, this work is characterized by two steps: the first one consists of carrying out detailed analysis of two stability parameters to understand their real influence on the performances of anaerobic digestion. The first indicator is the FOS/TAC, that permits to monitor the process stability by focusing on the alkalinity level of the system. The second parameter is the OLR, that defines the amount of volatile solids that must be fed to the reactor every day. Regarding the second step, this dissertation is characterized by the creation of a machine learning to identify the specific conjugated substrate that, as a function of a predefined biomass, can maximize the biomethane yield through a co-digestion process. This machine learning exploits two tools: the first one is a neural network, while the second one is an algorithm that allows to find the optimal composition of the mixture that must be fed to the reactor. Thanks to this work, it has been possible to obtain significant results regarding the role played by the synergy and antagonism effects during the anaerobic digestion. However, there are still several weaknesses to be solved to reach a complete understanding of this topic. Key-words: co-digestion, synergy effects, antagonism effects, stability parameters, FOS/TAC, OLR, neural network, conjugated substrate. Abstract in italiano La digestione anaerobica è una tecnologia affidabile che, in assenza di ossigeno eattraverso l’intervento di una specifica comunità batterica, permette di produrre biogas partendo da biomasse di diversa natura. Le performance di questo processosono fortemente influenzate dagli effetti di sinergia e antagonismo che si formano all’interno del reattore. La loro generazione dipende dalle interazioni che si instaurano tra le macromolecole delle biomasse e i microorganismi dell’ambiente di reazione. A causa della loro importanza, l’obbiettivo di questa tesi è quello di acquisire una migliore conoscenza riguardo alla dipendenza della digestione anaerobica rispetto aquesti due effetti. In maniera tale da poter raggiungere il precedente obbiettivo, questolavoro è caratterizzato da due step: il primo consiste nell’effettuare analisi dettagliate di due parametri di stabilità per poter comprendere la loro reale influenza sulle performance della digestione anaerobica. Il primo indicatore è il FOS/TAC, che permette di monitorare la stabilità del processo focalizzandosi sul livello di alcalinità del sistema. Il secondo parametro è l’OLR, che quantifica la quantità di solidi volatiliche devono essere alimentati al reattore ogni giorno. Per quanto riguarda il secondo step, questa tesi è caratterizzata dalla creazione di una machine learning per poter identificare il corretto substrato coniugato che, in funzione di una biomassapredefinita, è in grado di massimizzare la resa di biometano attraverso un processo di co-digestione. Questa machine learning sfrutta due strumenti: il primo è una rete neurale, mentre il secondo è un algoritmo che permet

FAQs

What is the main goal of the dissertation?

To understand how synergy and antagonism effects depend on anaerobic digestion and how they influence process performance, by analyzing stability parameters and optimizing co-digestion for biomethane yield.

How are stability and operating conditions quantified in the work?

Stability is monitored using FOS/TAC, focusing on the system alkalinity level, while OLR is used to define the amount of volatile solids fed to the reactor each day.

How does the machine learning approach improve productivity in co-digestion?

It uses a neural network and an optimization algorithm to identify the conjugated substrate and mixture composition, maximizing biomethane yield for a predefined biomass.

Aug 5, 2026

Upload by Skyler