Development of an optimization genetic algorithm method for estimating municipal solid waste composition

The accurate estimation of municipal solid waste composition is crucial for effective waste management and resource recovery. Conventional approaches rely on direct sampling, which is both time-consuming and costly. This study presents an accurate and stable Genetic algorithm-based inverse method for estimating municipal solid waste composition without sampling. The method estimates municipal solid waste composition from measurable parameters, including flue gas, working fluid, ash, and leachate, using a genetic algorithm for accurate and stable estimation without physical sampling. The method's accuracy and stability are validated through numerical simulation experiments involving five distinct municipal solid waste compositions. Data from direct problem simulations, perturbed by random errors, serve as inputs for the genetic algorithm-based inverse solution. Results indicate that the inverse solution is stable. Results indicate that the inverse solution is stable and accurately reproduces the average composition of the five municipal solid waste samples used in the direct method. The results reveal that the estimated composition of municipal solid waste closely matches actual values, demonstrating the feasibility of this genetic algorithm-based approach. The modified methodology is employed at the Aradkooh waste-to-energy power plant in Tehran, Iran. The findings from the Aradkooh power station indicate that the carbon, oxygen, hydrogen, sulfur, moisture, and ash content of municipal solid waste are 27.14, 33.29, 3.16, 0.3, 15.41, and 20.21 percent, respectively. The novelty of this study lies in stabilizing the inverse problem by increasing the number of equations. As a result, the solution achieves higher accuracy and lower estimation errors compared to previous studies.