Bolstering Methane Production: Biochar Optimization in Anaerobic Digestion of Cheese Whey

This research explores enhancing methane generation in anaerobic digestion of cheese whey by adding biochar (BC). Significant improvements include a 29.58% increase in methane yield and a 27% reduction in lag phase time at 10% total solids concentration with 50 g/L BC loading. Modeling with Gompertz and ANN algorithms validates BC’s efficacy.

March 4, 2024

1–2 minutes

Bella, Pilli, et al (2024) Predictive modelling of methane yield in biochar-amended cheese whey and septage co-digestion: Exploring synergistic effects using Gompertz and neural networks. *Chemosphere.* https://doi.org/10.1016/j.chemosphere.2024.141558

Biomethane, a promising renewable energy source, has gained attention as an alternative to traditional fossil fuels. Anaerobic digestion (AD) is a proven method for biomethane production, but challenges like C/N ratio imbalance and ammonia inhibition persist. This research explores the use of biocharBiochar is a carbon-rich material created from biomass decomposition in low-oxygen conditions. It has important applications in environmental remediation, soil improvement, agriculture, carbon sequestration, energy storage, and sustainable materials, promoting efficiency and reducing waste in various contexts while addressing climate change challenges. More (BC) as an additive in the AD of cheese whey and septage, aiming to improve process stability and enhance methane yield.

Bench-scale studies were conducted with varying BC dosages and total solids (TS) concentrations. Notably, the addition of BC resulted in a significant 29.58% increase in methane yield and a 27% reduction in lag phase time at 10% TS concentration with 50 g/L BC loading. Mechanistic investigations highlighted BC’s buffering capacity and its role in mitigating ammonia inhibition. Statistical analysis emphasized the pronounced impact of BC dosage on the process.

The study employed the Gompertz model (GM) and artificial neural network (ANN) algorithm for modeling. The optimal ANN model outperformed GM with a Mean Squared Error (MSE) of 17.96 and an R2 value of 0.9942. This emphasized the practicality of employing a high BC dosage in semi-solid anaerobic digestion conditions.

The research sheds light on optimizing factors such as BC dosage, feedstockFeedstock refers to the raw organic material used to produce biochar. This can include a wide range of materials, such as wood chips, agricultural residues, and animal manure. More, and TS content for enhanced AD performance. The findings contribute to addressing challenges in AD processes, emphasizing the potential of BC in reducing lag phase time and increasing methane production. Additionally, the combined use of GM and ANN provides a comprehensive understanding of the interaction between TS concentrations and BC loading.

The study concludes that biochar addition significantly improves methane generation in anaerobic digestion of cheese whey. The research underscores the importance of BC dosage and its positive impact on process stability, offering valuable insights for optimizing AD systems. The findings contribute to advancing sustainable waste-to-energy alternatives and further research in the field.