OPTIMIZATION OF DOMESTIC AND INDUSTRIAL BIODIGESTORS BASED ON MACHINE LEARNING TECHNIQUES

Purpose: Development of an application for determining the technical and economic feasibility of implementing and operating domestic biodigesters using rigorous mathematical modeling of the anaerobic digestion process in conjunction with Machine Learning techniques to obtain reduced metamodels. Theo...

Full description

Saved in:
Bibliographic Details
Published in:RGSA : Revista de Gestão Social e Ambiental 2023-01, Vol.17 (10), p.1-18
Main Authors: Leite, Marcos Sousa, Silva, Sarah Lilian De Lima, Fernandes, Thalita Cristine Ribeiro Lucas, da Silva, Sidinei Kleber, de Araujo, Antonio Carlos Brandão
Format: Article
Language:English
Subjects:
Anaerobic digestion
Anaerobic processes
Anaerobic treatment
Biodegradable wastes
Biodegradation
Biofertilizers
Biogas
Biomass
Carbon dioxide
Compilers
Digesters
Digestion
Economic feasibility
Electricity generation
Energy consumption
Environmental impact
Feasibility
Fermentation
Fertilizers
Learning algorithms
Machine learning
Mathematical models
Matlab
Metamodels
Nitrogen
Optimization
Packaging
Phosphorus removal
Restaurants
Software
Water treatment
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose: Development of an application for determining the technical and economic feasibility of implementing and operating domestic biodigesters using rigorous mathematical modeling of the anaerobic digestion process in conjunction with Machine Learning techniques to obtain reduced metamodels. Theoretical Framework: The generation of biodegradable waste results from human activities and has detrimental environmental impacts. To mitigate this problem, anaerobic digestion in biodigesters emerges as a viable solution, promoting the production of biogas and biofertilizers, generating economic and environmental benefits. However, implementing and operating this system requires significant investments. Method/Design/Approach: The combination of the ADM1 model with Machine Learning techniques is used to create simplified metamodels, allowing for more feasible simulations and optimizations, thereby developing an application to assess the technical and economic feasibility of biodigesters. This application is obtained by packaging the reduced metamodel using the MATLAB Compiler, which will be made available as an Excel addin. Results and Conclusion: The reduced metamodel effectively represented the rigorous Simulink model, and the optimization of the process proved satisfactory. Furthermore, the add-in generated through the MATLAB Compiler met expectations. Research Implications: Enhanced understanding of the waste biodigestion process, demonstrating the economic and environmental returns achieved when focusing more on this area. Originality/Value: Development of a tool that enables the simulation and evaluation of a biodigestion process without the need to purchase expensive software.
ISSN:1981-982X
DOI:10.24857/rgsa.vl7nl0-041