ABSTRACT
Maximizing the network of chemical symbiosis can enhance economic benefits and reduce life cycle environmental impacts, which are pivotal for achieving sustainability in the chemical industry. This study designs two innovative symbiosis algorithms, the Longest Path Algorithm and the Maximum Symbiosis Algorithm, specifically for chemical industrial clusters (CICs). The algorithms are applied to a prototypical CIC encompassing 569 distinct raw materials and yielding 435 unique products alongside 55 byproducts. First, the study provides an exhaustive overview of the assorted chemicals and their intrinsic traits, flow patterns, and conventional relationships within the cluster. On that basis, the former algorithm reveals that the longest path constitutes 5 enterprises, embodying the entire disperse dyestuff industry chain; the latter algorithm identifies 218 pairs of symbiotic relationships, leading to an additional 0.91 million tonnes of symbiotic chemicals. These interrelations also yield substantial cost savings of 1.25 billion CNY (0.17 billion US dollar) and enhance life cycle benefits by 0.62 to 11.87 times compared to the present status. The efficacious application of these algorithms to the cluster reaffirms their capacity to meet the designated objectives. This study introduces a fresh interdisciplinary standpoint to optimize chemical manufacturing processes and contributes essential theoretical underpinning for implementing pollution and carbon reduction strategies in similar CICs.
