Neural Models for Optimizing Lignocellulosic Residues Composting Process

Application of machine learning algorithms in municipal solid waste management: A mini review

“…The historical length of the waste data and its quality are critical for reliable ML model performance ( Chen and Lin, 2008 ; Masebinu et al, 2017 ). However, most current MSWM research deals with small datasets ( Díaz et al, 2012 ; Hosseinzadeh et al, 2020 ; Ozkan et al., 2015 ), and this may be attributed to waste management infrastructure and practices ( Ayeleru et al, 2021 ). Waste-related data are managed by different channels involving several stakeholders, making data collection, and compilation difficult ( Abbasi and El Hanandeh, 2016 ).…”

“…During waste composting, some toxic and harmful gases such as CH 4 and N 2 O are released, which can cause environmental pollution and may adversely affect human health. To control and optimize the composting process, some researchers have proposed ML methods to predict the response parameters and model gas emissions ( Díaz et al, 2012 ). Generally, the application of ML in composting is in the exploratory stage, and almost all the algorithms currently used are ANN, and the data are difficult to obtain, mainly from experimental tests, so some development is necessary before practical application.…”

Neural image analysis for maturity classification of sewage sludge composted with maize straw

“…Artificial neural networks were used for the prediction of ammonia emission from the composted biomass (Boniecki et al, 2012a), the prediction of heat loss and the classification of the degree of maturity on the basis of selected physical and microbiological parameters (Gao et al, 2007). They were also used for the optimisation of the composting process based on the achievement of appropriate courses of the curves of variance in pH, temperature and CO 2 concentration (Díaz et al, 2012). The application of the methods of computer image analysis in such studies is even more limited.…”