Determination of the optimal feed recipe of anaerobic digesters using a mathematical model and a genetic algorithm

[Display omitted] •A Simplified anaerobic co-digestion model is used for multi-objective optimization purpose.•A set methane yield is achieved while reducing total ammonia and maximizing profitability.•Genetic constrained algorithm ensures feasible solutions and rapid computations.•The optimal feed...

Full description

Saved in:
Bibliographic Details
Published in:Bioresource technology 2024-02, Vol.393, p.130091-130091, Article 130091
Main Authors: Awhangbo, L., Schmitt, V., Marcilhac, C., Charnier, C., Latrille, E., Steyer, JP
Format: Article
Language:English
Subjects:
Anaerobic co-digestion
Computer Science
Environmental Engineering
Environmental Sciences
Genetic algorithm
Modeling and Simulation
Multi-objective optimization
Optimal feed recipe
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:[Display omitted] •A Simplified anaerobic co-digestion model is used for multi-objective optimization purpose.•A set methane yield is achieved while reducing total ammonia and maximizing profitability.•Genetic constrained algorithm ensures feasible solutions and rapid computations.•The optimal feed recipes ensure stable operations and enhance profits. Recently, numerous experimental studies have been undertaken to understand the interactions between different feedstocks in anaerobic digestion. They have unveiled the potential of blending substrates in the process. Nevertheless, these experiments are time-intensive, prompting the exploration of various optimization approaches. Notably, genetic algorithms have gained interest due to their population-based structures allowing them to efficiently yield multiple Pareto-optimal solutions in a single run. This study uses a simplified static anaerobic co-digestion model as the fitness function for a multi-objective optimization. The optimization aims to achieve a methane production set-point while reducing the output ammonia nitrogen and increasing the recipe’ profitability. Thus, the study employs genetic algorithms to identify Pareto fronts and constraints confined the solution space within feasible boundaries. It also underscores the influence of economic considerations on the viable solution space. Ultimately, the optimal feed recipe not only ensures stable operations within the digester but also enhances associated profits.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2023.130091