Abstract
Industrial symbiosis is a promising approach for energy conservation and emission reduction in the global industrial sector. The objective of this research is to optimize technology implementation within the industrial symbiosis system to achieve enhanced environmental and economic performance. The case study in this research has five objectives, making this a many-objective optimization problem (MaOP). MaOPs are a type of multi-objective optimization problems (MOPs), with the key difference being that MaOPs have four or more optimization objectives . While traditional intelligent algorithms are suitable for solving MOPs with no more than three objectives, they encounter serious difficulties when applied to MaOPs (high risk of falling into a local optimum, high computational complexity in algorithm performance evaluation, and difficulty in visualizing and analyzing high-dimensional optimal solutions). To fill this gap, this article establishes a novel methodological framework for solving many-objective environmental management problems. The framework includes a modified NSGA-III model, the Hypervolume by Slicing Objectives algorithm, the Heatmap method, and the fitted curve method. This framework provides model optimization, algorithm performance evaluation, results visualization, and decision-making analysis capabilities for optimizing technology implementation within the industrial symbiosis system. The study tests the effectiveness and reliability of this methodology. The result shows that industrial symbiosis can greatly reduce energy consumption, pollutant emissions , and economic costs in the industrial sector. Optimal technologies for effective industrial symbiosis in China from 2020 to 2030 are identified. • A cost effective & environmental friendly industrial symbiosis system is established. • A methodological framework is built to solve many-objective optimization problems. • Environmental & economic performance of nationwide industrial symbiosis is evaluated. • Optimal symbiotic technologies for effective industrial symbiosis are identified.
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