LAUSR

Abstract Mathematical models have been developed over the past 40 years to describe the composting process, seeking to ease its implementation, control, and optimization. Due to the complex nature of composting, the ability to simulate the processes kinetics in a simple and generalizable manner has proven to be elusive, acting as a significant limitation to effective environmental, model-assisted, decision-making. Current simple models are ungeneralizable, while generalizable models lack simplicity, requiring information on many operating variables, such as temperature, moisture content, and oxygen content. The aim of this work is therefore to explore the use of novel modelling approaches to produce generalizable and simple composting models that do not require any of this data, while providing a more accurate representation of degradation than current simple models. Four modelling methods are assessed in this study, all based on a first-order kinetic expression. These novel modelling approaches split the degradation into three separate phases and only require two parameters: a degradation rate and an estimation of the ratio between the duration of the mesophilic and thermophilic phases. The models were assessed through their normalized root mean square error and validated over three independent datasets sourced from the literature, covering a wide range of waste types and characteristics. The novel methods achieved errors varying between 1.13% and 6.32% and outperformed a traditional first-order model in every case, as well as more complex models in certain cases. Scenario analyses also demonstrated the resilience of the proposed approaches to uncertainty.