New Framework Balances Water Conservation and Carbon Reduction in Chinese Industry
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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A new research framework developed by Chinese scientists offers a systematic approach to addressing the complex challenge of balancing water conservation, carbon emission reduction, and aquatic ecosystem preservation in China's industrial sector. The mechanism-data dual-driven framework, proposed by researchers Yuehong Zhao and Hongbin Cao from the Institute of Process Engineering of Chinese Academy of Sciences, provides industrial parks with a methodology to achieve environmental goals while maintaining economic feasibility.
The framework involves developing hybrid models that characterize water-use and treatment processes along with their associated carbon emissions. According to Zhao, the lead author of the study published in Water & Ecology, solving the optimization model identifies the optimal technical pathway for simultaneous water conservation and carbon emission reduction at minimum water-use cost. This approach provides valuable information to support decision-making about water network optimization within industrial parks, which is crucial for China's industrial sector facing increasing environmental pressures.
The hybrid modeling approach integrates mechanistic understanding with data-driven techniques, enhancing model interpretability and generalization even with limited training datasets. This represents an effective approach to promoting the application of machine learning and AI technologies in the industrial sector, though researchers note that systematic theory and methodology for hybrid modeling remain underdeveloped. The key challenges include selecting appropriate mechanisms and their expression for integration with machine learning.
A superstructure optimization model was constructed based on unit models and domain knowledge, encompassing feasible unit technologies, their interconnections, and relevant constraints to identify optimal solutions. Deterministic optimization algorithms were applied to achieve global optimum solutions with minimal water-use cost. In case studies, a multi-scale optimization methodology for water conservation in industrial parks was established, leading to the development of a practical software tool successfully applied in steel companies.
The framework's importance lies in its ability to provide solutions that balance local and overall benefits, as well as economic benefits and environmental impacts. This is particularly significant for China's industrial sector, which must navigate the competing demands of environmental sustainability and economic growth. The research, supported by a grant from the key Program of National Natural Science Foundation of China (51934006), demonstrates how systematic approaches can help industries meet multiple environmental objectives simultaneously while maintaining cost-effectiveness.
The successful application in steel companies indicates the framework's practical utility and potential for broader implementation across various industrial sectors. As industries worldwide face increasing pressure to reduce their environmental footprint while remaining competitive, this research provides a valuable methodology for achieving these dual objectives through optimized resource management and technological integration.
Curated from 24-7 Press Release
