Integrating Membrane Distillation and AI for Circular Water Systems in Industry
DOI:
https://doi.org/10.15662/IJRAI.2023.0605007Keywords:
Membrane distillation, artificial intelligence, machine learning, circular water systems, industrial wastewater, zero liquid discharge, predictive maintenance, process optimizationAbstract
Membrane distillation (MD) is emerging as a robust, thermally driven separation process capable of producing high-quality water from industrial effluents and saline brines, particularly when integrated with low-grade or waste heat sources. Recent advances in artificial intelligence (AI) and machine learning (ML) have enhanced process monitoring, predictive control, and optimization capabilities across industrial water systems. This study synthesizes developments in membrane distillation configurations, pilot-scale demonstrations, and AI-based predictive modeling approaches. It proposes an integrated AI–MD framework that leverages digital twins, data-driven control, and multi-objective optimization to achieve circular water management in industrial operations. Results from literature and pilot applications show that such integration improves water recovery (by 10–15%), reduces energy costs (by up to 30%), and lowers CO₂ emissions (by ~35%) compared with conventional setups. The paper concludes with implementation strategies and research priorities for scaling AI-enabled MD across industrial sectors.
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