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Enhancing Machinery-Aided Composting Through Multiobjective Optimization
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AI
DOI：10.3390/app151910754.png)
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Abstract
En 中文
This study focuses on optimizing the composting process through advanced multiobjective optimization techniques, aiming to minimize both operational costs and CO2 emissions by efficiently allocating tasks to specialized machinery. It introduces three novel multiobjective models that uniquely integrate cost minimization, CO2 emission reduction, and maximized waste processing, addressing a critical gap in sustainable composting. The first model prioritizes cost reduction, providing a foundational framework for optimizing resource allocation. Building on this, the second model integrates environmental considerations, balancing cost minimization with the reduction of CO2 emissions to achieve a sustainable trade-off. The third model takes a broader approach by maximizing the volume of organic waste processed within a workday while simultaneously minimizing emissions. These models incorporate real-world constraints, such as machinery capacity, operational work hours, and required rest periods for compost piles. The findings underscore the potential of multiobjective optimization to tackle complex industrial challenges. This research offers a practical and sustainable solution that harmonizes economic efficiency with environmental stewardship, demonstrating its applicability to processes as intricate as composting.
Keywords:
composting
multiobjective optimization
evolutionary algorithms
sustainability
Journal
A
IF:
2.5
Papers:
7.3K
Citations:
4