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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU145542" target="_blank" >RIV/00216305:26210/22:PU145542 - isvavai.cz</a>

Result on the web

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0959652622028475" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0959652622028475</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jclepro.2022.133260" target="_blank" >10.1016/j.jclepro.2022.133260</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20704 - Energy and fuels

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Volume of the periodical

neuveden

Issue of the periodical within the volume

368

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

133260-133260

UT code for WoS article

000840978200005

EID of the result in the Scopus database

2-s2.0-85135566150