Techno-Economic Assessment of On-Farm Anaerobic Digestion System Using Attached-Biofilm Reactor in the Dairy Industry

Determination of the optimal feed recipe of anaerobic digesters using a mathematical model and a genetic algorithm

Recently, numerous experimental studies have been undertaken to understand the interactions between different feedstocks in anaerobic digestion. They have unveiled the potential of blending substrates in the process. Nevertheless, these experiments are time-intensive, prompting the exploration of various optimization approaches. Notably, genetic algorithms have gained interest due to their population-based structures allowing them to efficiently yield multiple Pareto-optimal solutions in a single run. This study uses a simplified static anaerobic co-digestion model as the fitness function for a multi-objective optimization. The optimization aims to achieve a methane production set-point while reducing the output ammonia nitrogen and increasing the recipe' profitability. Thus, the study employs genetic algorithms to identify Pareto fronts and constraints confined the solution space within feasible boundaries. It also underscores the influence of economic considerations on the viable solution space. Ultimately, the optimal feed recipe not only ensures stable operations within the digester but also enhances associated profits.

Effect of organic loading on anaerobic digestion of cow dung: Methane production and kinetic study

Organic loading influences the effectiveness of producing biogas through anaerobic digestion. This study set out to investigate the effect of organic loading on the anaerobic mesophilic digestion of cow dung, the parameters involved in the digestion process and to evaluate the kinetics. Anaerobic digestion of cow dung at different organic loading (gVS/L) of 14 gVS/L, 18gVS/L, 22 gVS/L, 26 gVS/L and 30 gVS/L were investigated. Increasing the organic loading increased the methane yield of the cow dung. The highest cumulative methane yield was observed at 30 gVS/L with 63.42 mL CH₄/gVS while the highest biogas yield was reported at 192.53 mL/gVS with the highest methane content of 89%. In addition, the modified Gompertz model equation with an R² of 0.9980 demonstrated strong consistency and a good fit between predicted and experimental data. The high number of substrates added to the systems when increasing the organic loading increased the λ and slow down the nutrient transport and hydrolysis. This study provides current information on the effects of organic loading on the anaerobic digestion of cow dung in batch mode, including experimental conditions and operational parameters.

A review on anaerobic digestion with focus on the role of biomass co-digestion, modelling and optimisation on biogas production and enhancement

The status, recent trends and future perspectives in modelling and optimisation of anaerobic co-digestion is investigated. Areas that can be focused on and those which need further research towards enhancing biogas production are pointed out. Co-digestion, modelling and optimisation of anaerobic digestion as well as techno-economic aspects are reviewed in this paper. It was noted that co-digestion requires more research into a variety of bio-resources and their specific blend proportions. Modelling and optimisation of co-digestion with substrate seasonal fluctuations has not been addressed in previous studies. Controlling key process factors including temperature, pH, and carbon to nitrogen ratio is critical in improving biogas yield. Biogas hybridisation is yet to be explored in depth. The majority of researches are focused on mono-digestion, feedstock co-digestion, modelling, and optimisation of anaerobic digestion needs significant further investigations. A multi-objective approach taking all technical and economic parameters in the modelling and optimization is essential.