Optimization of Wheat Straw and Distiller’s Grains Fertilizer Via Life Cycle Assessment and Genetic Algorithm

This study optimizes wheat straw and distiller's grains bio-fertilizers for improving various soil types (sandy, clay, loam). A multi-objective model integrating Life Cycle Assessment (LCA) and Genetic Algorithm is proposed to maximize soil fertility, crop growth, and environmental benefits. A dynamic soil-fertilizer-crop model is developed to assess the effects of fertilizer application on soil nutrients, pH, and microbial activity. Simulations examine long-term impacts of fertilizer combinations and application methods, with optimization via GA. Results show that the fertilizer improves soil fertility and crop growth, but strategies must consider soil type differences. Loam and clay soils absorb fertilizer better, while sandy soils risk nutrient loss. Sensitivity analysis and model validation confirm accuracy and stability, providing a scientific basis for green agriculture. This study supports optimal fertilization strategies and validates the environmental benefits of wheat straw and distiller's grains fertilizers for sustainable agricultural waste recycling.