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Composting is a promising process for green waste (GW) management. However, reducing the processing time and increasing end-product quality are aspects to be improved. A strategy to optimize GW composting is using bacterial strains and microbial consortia that stimulate lignocellulose degradation. This work systematically analyses the research trends on the use of those bacterial inoculants and microbial consortia. The information was analyzed according to three topics: (i) microbial role in the process; (ii) process optimization; and (iii) end-product quality. Research needs identified by our research include: (i) selection of bacterial consortia and optimization of inoculum concentration; (ii) correlation between enzymatic activity of indigenous and exogenous bacteria and nitrogen availability during the process; and (iii) stimulation of lignocellulose degradation and formation of humic substances considering end-product quality. This research contributes to planning future research endeavors related to the use of microbial inoculants to optimize GW composting. [ABSTRACT FROM AUTHOR]

To improve the financial sustainability and business management of composting facilities, mostly small-scale and manually operated facilities, the simultaneous optimization of the process and quality of the final product must be prioritized. This study used artificial neural networks (ANNs) and particle swarm optimization (PSO) as tools to evaluate the composting of biowaste (BW) with different cosubstrates (i. star grass (SG) and ii. a SG and sugarcane filter cake (SFC) mixture). The simulation aimed to maximize product quality in the shortest processing time by varying the mixing ratio and turning frequency. The simulation showed optimal conditions with a turning frequency of twice per week with the following mixtures: (i) BW:SG (72.9:27.1), 76 days of processing; and (ii) BW:SFC:SG (60:16:24), 88 days of processing. The results showed the effect of the type of carbon source in the cosubstrates on the retention time, which may imply the need for a larger area in composting facilities. On the other hand, the findings show that a minimum time is required to achieve a product that meets quality standards, although a longer processing time reduces the agricultural value of the compost. This model can be used to define design criteria and operating conditions and select a cosubstrate that can contribute to improving the agricultural quality of the final product. [ABSTRACT FROM AUTHOR]

A novel approach to optimize the composting process of biowaste (BW) mixed with sugarcane filter cake (SFC) and the product quality was attempted in the present study by adopting Artificial Neuronal Network (ANN) and the particle swarm optimization (PSO) algorithm. The effectiveness of the co-composting process depends on operational parameters such as Mixing Ratio (MR) and Turning Frequency (TF). Using the optimization of these factors, the process time can be reduced while product quality can be maximized. This study includes the simultaneous evaluation of both operational parameters, with SFC being the amendment material (BW:SFC MR of 90:10, 80:20 and 70:30) and a TF of 1, 2 and 3 turnings per week. The simultaneous effect of the two operational parameters was evaluated using a central composite design. ANN was used to predict the behaviour of the response parameters, and the PSO algorithm was used to optimize the process and the final product quality. The results of the simulations with ANN suggest that the BW:SFC ratios of 81:19 and 75:25, with a TF of two times/week and an estimated operation time of 76–94 days, correspond to a final product with the most adequate physicochemical quality for agricultural use. The optimization with PSO showed the optimal local at a BW:SFC MR of 76.9:23.1 with a turning frequency of two times weekly. An 80-day process is recommended to optimize the final product quality. The model can be useful to define design criteria and operational conditions during biowaste composting. [ABSTRACT FROM AUTHOR]

Although composting is an effective option for the use of biowaste (BW), its physicochemical quality has limiting conditions (e.g., low C/N ratio, phosphorus and high moisture) that affect the process and the quality of the product. The incorporation of amendment materials (AM) and the variation in the turning frequency (TF) are two of the most widely used operational strategies to improve these limiting conditions. However, the effect of the simultaneous application of these two strategies on the BW composting process has been few studied. In this article, the inclusion of sugarcane filter-cake (SFC) as an AM in BW at several mixing ratios (BW:SFC (w/w); 100:00, 90:10, 80:20 and 70:30) and three TFs (1, 2 and 3 turnings/week) was evaluated at a pilot scale. A Box-Behnken experimental design was used to analyse the simultaneous effects of the two strategies. Results showed that the application of both operational strategies simultaneously has a significant effect (p ≤ 0.05) on process parameters [i.e., stabilization time, temperature, pH, oxygen concentration, Total Organic Carbon (TOC), Total Nitrogen (TN) and Total Phosphorus (TP)] and on product quality. A mixture ratio of 80:20% (BW:SFC) and a TF of 2 reduced processing time by 13 days compared to the control treatment, maintained temperatures above 65 °C and increased the TOC (> 15%), TN (2.4%) and TP (1.6%) contents at the end of the composting process, thus improving the quality of the final product. [ABSTRACT FROM AUTHOR]