Co-combustion, life-cycle circularity, and artificial intelligence-based multi-objective optimization of two plastics...

Area of Science:

• Environmental Science
• Chemical Engineering
• Materials Science

Background:

• Waste plastics are globally abundant, posing environmental challenges.
• Textile dyeing sludge (TDS) presents significant environmental pollution concerns.
• Co-combustion offers a sustainable solution for waste management and energy recovery.

Purpose of the Study:

• To investigate the co-combustion performance of TDS with polypropylene (PP) and polyethylene (PE).
• To analyze the impact of plastic blend ratios on combustion characteristics, gas emissions, and ash properties.
• To optimize co-combustion parameters for enhanced energy production and reduced emissions.

Main Methods:

• Quantification and characterization of combustion performances, gas emissions, and ashes from TDS, PP, and PE co-combustion.
• Analysis of synergistic interactions between TDS and plastics during combustion (200-600 °C).
• Determination of kinetic parameters (activation energy, reaction mechanism) and optimization using artificial neural networks.

Main Results:

• Increased PP and PE ratios improved ignition, burnout, and comprehensive combustion indices.
• Plastics pre-ignited TDS, which in turn promoted TDS combustion, indicating significant synergistic effects.
• Co-combustion resulted in higher CO2 but lower CH4, C-H, and CO emissions; Ca-based minerals in plastics reduced SO2 emissions.
• Activation energy decreased with increasing plastic content, and the reaction mechanism followed f(α) = (1-α)n with reduced reaction order.

Conclusions:

• Co-combustion of TDS with PP and PE is an effective strategy for waste valorization and pollution control.
• Synergistic interactions enhance combustion efficiency and reduce the formation of harmful gases.
• Artificial neural network optimization can achieve simultaneous goals of maximizing energy production and minimizing emissions.