Effect of biochar addition on the improvement of the quality parameters of compost used for land reclamation

Optimization of composting methods for efficient use of cassava waste, using microbial degradation

With the recent revolution in the green economy, agricultural solid waste resource utilization has become an important project. A small-scale laboratory orthogonal experiment was set up to investigate the effects of C/N ratio, initial moisture content and fill ratio (v_{cassava residue}: v_gravel) on the maturity of cassava residue compost by adding Bacillus subtilis and Azotobacter chroococcum. The highest temperature in the thermophilic phase of the low C/N ratio treatment is significantly lower than the medium and high C/N ratios. The C/N ratio and moisture content have a significant impact on the results of cassava residue composting, while the filling ratio only has a significant impact on the pH value and phosphorus content. Based on comprehensive analysis, the recommended process parameters for pure cassava residue composting are a C/N ratio of 25, an initial moisture content of 60%, and a filling ratio of 5. Under these conditions, the high-temperature conditions can be reached and maintained quickly, the organic matter has been degraded by 36.1%, the pH value has dropped to 7.36, the E4/E6 ratio is 1.61, the conductivity value has dropped to 2.52 mS/cm, and the final germination index increased to 88%. The thermogravimetry, scanning electron microscope, and energy spectrum analysis also showed that the cassava residue was effectively biodegraded. Cassava residue composting with this process parameter has great reference significance for the actual production and application of agriculture.

Insight to maturity during biogas residue from food waste composting in terms of multivariable interaction

This study used biogas residue produced by anaerobic fermentation of food waste as the raw material in large-scale windrow composting. The effects of the addition of a microbial consortium on the physical and chemical properties and stability of composting of biogas residue were studied. The maturity of food waste biogas residue during composting was investigated by multivariate interaction of environmental, maturity, and nutrient parameters, using structural equation modeling (SEM). Results showed that the temperature of T2 compost with the microbial consortium increased more rapidly. The pH ranges of T1 (without the microbial consortium) and T2 were 8.75-9.15 and 8.42-9.27, respectively; the electrical conductivity (EC) ranges of T1 and T2 were 2.74-3.95 mS/cm and 2.81-3.85 mS/cm, respectively; the degradation rates of organic matter (OM) in T1 and T2 were 21.74% and 33.62%, respectively; and the total nitrogen (TN) ranges of T1 and T2 were 1.93-3.10% and 1.80-3.21%, respectively. By the end of composting, the germination indices (GI) of T1 and T2 were 20.57% and 64.24%, respectively. The total oxygen consumption after 4 days (AT₄) was 1.88 mg-O₂/g and 1.2 mg-O₂/g in T1 and T2, respectively. SEM of T1 showed that compost temperature and EC were important factors affecting compost maturity. These factors highly significantly affected OM, which in turn affected AT₄ of the biogas residue composting. SEM of T2 showed that compost temperature, pH, and EC affected OM, which in turn affected compost maturity. Temperature affected compost maturity by affecting AT₄ and GI. Principal component analysis (PCA) showed that the overall score of T2 was higher than that of T1, indicating that the addition of the microbial consortium was beneficial for industrial-scale composting of biogas residue produced by anaerobic digestion of food waste.

Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model

As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval. Optimal starting conditions should be maintained in the mesophilic state (temperature: 30-45℃, moisture content: 55-65%, pH: 6.3-8.0), and nutrients (total nitrogen > 2.3%, total organic carbon > 35%) should be adjusted in the thermophilic state. Experiments revealed that model-based optimization strategies could improve composting maturity because they could optimize compost microbial flora and perform complex carbon cycle functions. In conclusion, this study provides new insights into the enhancement of the composting process.