Integrating mixed microbial population dynamics into modeling energy transport during the initial stages of the aerobic composting of a switchgrass mixture

Application of Optimization and Modeling for the Enhancement of Composting Processes

Composting is a more environmentally friendly and cost-effective alternative to digesting organic waste and turning it into organic fertilizer. It is a biological process in which polymeric waste materials contained in organic waste are biodegraded by fungi and bacteria. Temperature, pH, moisture content, C/N ratio, particle size, nutrient content and oxygen supply all have an impact on the efficiency of the composting process. To achieve optimal composting efficiency, all of these variables and their interactions must be considered. To this end, statistical optimization techniques and mathematical modeling approaches have been developed over the years. In this paper, an overview of optimization and mathematical modeling approaches in the field of composting processes is presented. The advantages and limitations of optimization and mathematical modeling for improving composting processes are also addressed.

A review of mathematical models for composting

Composting is a valuable method to treat and valorize organic waste. However, the process is defined by its dynamic nature and governed by a multitude of operating parameters. As such, mathematical modelling of the process offers a powerful tool to simulate and predict the variable outcomes of the process, allowing for its optimization. This can include improving efficiency, lowering costs and reducing environmental impact. To aid with the development of future models, we provide an up to date review and assessment on the state of the art of composting modelling. By reviewing 40 years of literature, this review paints the most complete picture of the field to date. This includes an analysis of trends in composting modelling: looking at the type of systems that are targeted, the aim of the models and the approaches to kinetics and mass and heat transfer. Regarding modelling approaches, we explore the fractionation of both substrates and microorganisms, the biological processes that can be included (disintegration, hydrolysis, uptake and death) and their kinetics (first-order, Monod-type), energy balances (biological generation, convection, conduction) and mass balances. We also provide a review of the results of sensitivity analyses performed on composting models, finding that models are most sensitive to microbial growth and death rates, as well as consumption rates and product yields. In the final portion of the review, we identify, explore, and provide guiding recommendations for work on emerging areas and areas requiring development in composting modelling (volume change, pH, maturation, artificial intelligence, etc.).

A microbial community snapshot of windrows from a commercial composting facility

The effect of depth on compost microbial communities is unclear but could be relevant to the management of windrows at commercial facilities. DNA extracted from 64 compost samples from seven windrows at a commercial facility were analyzed via deep 16S rRNA gene sequencing. The relative abundance of eight to nine genera was affected by depth during the transition from cooling to maturation phases between 4 and 6 months, whereas very few genera (0-1) showed a depth dependence in young, actively managed windrows or in mature windrows older than 10 months. Seven novel bacterial operational taxonomic units (OTUs) were detected in compost DNA and also in publicly available compost metagenomes. A compost metagenome was used to construct a metagenome-assembled genome for most of the abundant uncharacterized OTU in our samples and suggests its involvement in carbon cycling.

Model of the sewage sludge-straw composting process integrating different heat generation capacities of mesophilic and thermophilic microorganisms

A mathematical model integrating 11 first-order differential equations describing the dynamics of the aerobic composting process of sewage sludge was proposed. The model incorporates two microbial groups (mesophiles and thermophiles) characterized by different capacities of heat generation. Microbial growth rates, heat and mass transfer and degradation kinetics of the sewage sludge containing straw were modeled over a period of 36days. The coefficients of metabolic heat generation for mesophiles were 4.32×10(6) and 6.93×10(6)J/kg, for winter and summer seasons, respectively. However, for thermophiles, they were comparable for both seasons reaching 10.91×10(6) and 10.51×10(6)J/kg. In the model, significant parameters for microbial growth control were temperature and the content of easily hydrolysable substrate. The proposed model provided a satisfactory fit to experimental data captured for cuboid-shaped bioreactors with forced aeration. Model predictions of specific microbial populations and substrate decomposition were crucial for accurate description and understanding of sewage sludge composting.