Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China

Wen-Ze Wu, Chong Liu, Wanli Xie, Mark Goh and Tao Zhang

Energy & Environment, 2023, vol. 34, issue 5, 1639-1656

Abstract: To estimate the dynamic trend of industrial water-waste-energy (hereinafter referred to as WWE) system, this paper proposes a new method for forecasting specific indicators in such a system. First, the fractional accumulated generation operator, fractional derivative and classic nonlinear grey Bernoulli model are simultaneously coupled to develop an optimised nonlinear grey Bernoulli model that identifies the nonlinear trends in industrial WWE systems. Second, the particle swarm optimization algorithm is employed to determine the optimal model parameters in the newly-designed model. Based on this, simulation studies are conducted to examine the stability of the proposed model. Finally, the model is applied in the industrial WWE system. The results demonstrate that (1) the proposed model outperforms other competitive models in terms of error-value metrics and (2) industrial water use and industrial energy consumption will increase, whereas industrial wastewater discharge will decline. Furthermore, the rationality of the predicted results redis analyzed from a policy perspective.

Keywords: Industrial water-waste-energy system; Nonlinear grey Bernoulli model; Fractional accumulation; Particle swarm optimization; Sustainable development (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0958305X221094666 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:engenv:v:34:y:2023:i:5:p:1639-1656

DOI: 10.1177/0958305X221094666

Access Statistics for this article

More articles in Energy & Environment
Bibliographic data for series maintained by SAGE Publications ().