Publication detail

Accounting for regional water recyclability or scarcity using Machine Learning and Pinch Analysis

Chin, H.H., Varbanov, P.S., Klemeš, J.J., Tan, R.R.

Original Title

Accounting for regional water recyclability or scarcity using Machine Learning and Pinch Analysis

Type

journal article in Web of Science

Language

English

Original Abstract

Water stress is becoming a major concern worldwide because of the lack of fresh resources to meet growing water demand in the face of climate change. Resources recycling is a viable option, but the main dilemma is to define a proper water quality grading system. This paper proposes a hybrid framework combining Machine Learning (ML) with Process Integration (PI) tools for assessing the regional water scarcity and recycling potential. The procedure involves defining the quality of water resources using supervised or unsupervised ML. Supervised ML (Classification) is employed when the data samples' origins or quality levels are known. The data can be sampled from an existing recycling system. The unsupervised ML (Clustering) method is used when quality levels are unknown. Data dimensionality reduction or expansion methods are used on the dataset to yield better classification or clustering outcomes. Once the hierarchical quality classes/clusters are revealed, the PI approach of Pinch Analysis is applied with the defined quality categories for planning water exchange systems (e.g., urban water networks or industrial parks). The method not only identifies the quality bottleneck of the system but also reveals the fresh resources deficit or excess of system supplies based on the defined quality clusters. This novel concept is demonstrated with case studies featuring different water sources and scenarios. Results show that the hybrid approach can categorise the water sources effectively, and depending on the number of defined clusters/categories, the water recycling potential can be different (e.g. with 5 clusters, the recyclability rate is 44%, while with 2 clusters, the recyclability rate can increase to 78% for the case study). The framework could serve as a guideline for regional authorities to manage the water resources according to their own water resources and properties database.

Keywords

Industrial symbiosis; Water integration; Water network; Water stress

Authors

Chin, H.H., Varbanov, P.S., Klemeš, J.J., Tan, R.R.

Released

25. 9. 2022

Publisher

Elsevier Ltd

ISBN

0959-6526

Periodical

Journal of Cleaner Production

Number

368

State

United Kingdom of Great Britain and Northern Ireland

Pages from

133260

Pages to

133260

Pages count

18

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0959652622028475

BibTex

@article{BUT179145,
  author="Hon Huin {Chin} and Petar Sabev {Varbanov} and Jiří {Klemeš}",
  title="Accounting for regional water recyclability or scarcity using Machine Learning and Pinch Analysis",
  journal="Journal of Cleaner Production",
  year="2022",
  number="368",
  pages="133260--133260",
  doi="10.1016/j.jclepro.2022.133260",
  issn="0959-6526",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0959652622028475"
}