Machine learning-assisted industrial symbiosis: Testing the ability of word vectors to estimate similarity for material substitutions

A challenge of facilitating industrial symbiosis involves identifying novel uses of waste streams that can satisfy the demands of other industries. For these efforts, a variety of characteristics must often be considered. A mine of relevant knowledge has been gathered in resources such as academic journals and patent databases. However, in looking to harness the potential of such data to support facilitation, compiling information on expansive ranges of material properties and technical requirements from a variety of unstructured sources can pose a significant manual effort. To ameliorate this, we demonstrate and evaluate an automated system that, given a large collection of patents and academic articles related to waste valorization, is able to assist with the process of identifying which waste streams could potentially be used as substitute feedstocks. Instead of aiming to measure (potentially thousands of) material properties directly, we use word correlations as a proxy to reflect “common knowledge.” Novel in furthering this approach is the application of word vectors, which have emerged as a promising natural language processing tool. The process employs a machine learning approach where words are represented as high-dimensional vectors which encode latent features related to words that often appear around it. When this approach is assessed by comparing its suggestions to documented cases, the use of vectors shows potential to incorporate latent information in data-based explorations. Further research into how this approach compares, and could be integrated with, established symbiosis development practices will be key to understanding its full potential and drawbacks.

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