Waste-to-energy conversion through P-Fuzzy simulation of methane production

Area of Science:

• Environmental Science
• Chemical Engineering
• Artificial Intelligence

Background:

• Landfills are vital for urban solid waste management but face challenges in bioenergy recovery and environmental impact.
• Optimizing anaerobic decomposition and mitigating ecological harm are critical for sustainable landfill operations.

Purpose of the Study:

• To present a novel P-Fuzzy modeling approach, integrating hierarchical fuzzy logic with environmental and biogenic modeling for landfill waste management.
• To optimize anaerobic decomposition processes for enhanced energy efficiency and reduced ecological impact in landfills.

Main Methods:

• Developed a P-Fuzzy model grounded in the Tabasaran framework, integrating mathematical, chemical, and kinetic modeling.
• Incorporated environmental, biogenic (Monod equation), and process dynamics (modified Michaelis-Menten) to capture operational uncertainties.
• Simulated various landfill scales and environmental parameters to assess biogas production and process stability.

Main Results:

• Small-scale landfills exhibit greater operational stability; medium-scale landfills peak during the acidogenic-methanogenic transition.
• Large-scale landfills present challenges like instability and leachate accumulation, requiring advanced automation.
• Targeted adjustments in temperature, humidity, and waste composition can increase biogas output by up to 35%.

Conclusions:

• The novel hybrid model offers a scalable and adaptive tool for solid waste bioenergy systems.
• The P-Fuzzy model significantly enhances landfill sustainability through more efficient and environmentally responsible waste management.
• Integration of environmental sensors and AI is crucial for continuous process monitoring and optimization.