AI-Optimized Green Hydrogen Production from Whisky Distilling Waste: A Comprehensive Review

Whisky production generates large volumes of organic co-products—primarily pot ale (liquid) and draff (solids)—which represent both an environmental disposal challenge and an opportunity as feedstock for renewable fuels. This review synthesizes the literature on converting whisky distilling wastes into green hydrogen, covering feedstock characterization, biological, thermochemical and electrochemical hydrogen pathways, and techno-economic and life-cycle perspectives. We place special emphasis on how artificial intelligence (AI) and machine learning (ML) methods are being applied (and can be further applied) to optimize yields, reduce costs, and enable robust real-time control. We identify promising hybrid routes (e.g., anaerobic pretreatment → reforming or novel two-stage electrolysis), review case studies and pilots, evaluate barriers (scale, water content, nutrient balance, impurity management), and outline a research roadmap linking process data, digital twins, and AI-driven optimization to accelerate deployment. Key recommendations include (1) integrated process design combining biological and electrochemical stages for small-/medium-scale distilleries, (2) systematic collection of sensor and performance data to train predictive models, and (3) life-cycle and techno-economic standardization to compare pathways.