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FAQ: Mechanism-Data Dual-Driven Framework for Industrial Water Conservation and Carbon Emission Reduction
TL;DR
Industrial parks can gain cost advantages by implementing this framework that minimizes water-use costs while achieving water conservation and carbon emission reduction goals.
The framework combines mechanistic understanding with data-driven techniques to develop hybrid models and optimization algorithms that identify optimal water network configurations.
This approach helps balance economic growth with environmental protection, creating a more sustainable future by preserving aquatic ecosystems while reducing industrial carbon emissions.
Researchers integrated AI with traditional engineering methods to create a practical software tool that optimizes water use in steel companies and other industrial applications.
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The framework aims to address the challenge of balancing water conservation, carbon emission reduction, and aquatic ecosystem preservation in China's industrial sector at minimal cost by identifying optimal technical pathways for simultaneous water saving and carbon mitigation.
The framework was developed by Yuehong Zhao and Hongbin Cao from the Institute of Process Engineering of Chinese Academy of Sciences.
It involves developing hybrid models that integrate mechanistic understanding with data-driven techniques to characterize water-use and treatment processes, then constructing a superstructure optimization model to identify optimal solutions with minimal water-use cost using deterministic optimization algorithms.
The framework provides cost-effective decision support for water network optimization, balances economic and environmental benefits, and can provide solutions that balance local and overall benefits as well as economic benefits and environmental impacts.
Case studies confirm the framework's effectiveness, and it has been successfully applied in steel companies through a practical software tool developed from the multi-scale optimization methodology.
The study was published in Water & Ecology and can be accessed via DOI: 10.1016/j.wateco.2025.100003.
A systematic theory and methodology for hybrid modeling remain underdeveloped, with key challenges including how to select the appropriate mechanism and its expression for integration with machine learning.
The hybrid modeling approach represents an effective approach to promoting the application of machine learning/AI technologies in the industrial sector by integrating mechanistic understanding with data-driven techniques.
Deterministic optimization algorithms were applied to achieve global optimum solutions with minimal water-use cost, encompassing feasible unit technologies, their interconnections, and relevant constraints.
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