AI-Assisted Operating Window Screening for Microwave Thin-Layer Drying of Dewatered Municipal Sewage Sludge: Drying Kinetics, Hygienisation, and an Energy-Use Proxy

Municipal sewage sludge is an environmental liability but also an energy-rich biomass that can support circular economy resource recovery. Here, we benchmark thin-layer drying of dewatered municipal sewage sludge (sludge cake) (40 g; layer thickness ≤ 5 mm) under open-air, convective hot air (40–150 °C), and microwave (70–1200 W) conditions to quantify drying kinetics, hygienisation indicators, and a screening-level energy-use proxy. High-power microwave drying reduced the time to constant mass from 32 h (open air) and 25 h 05 min (40 °C convection) to 20 min (900 W) and 14 min 05 s (1200 W). Faecal indicators (total/thermotolerant coliforms and presumptive Escherichia coli) were below detection after ≥100 °C convection or ≥300 W microwave treatment, while mesophilic aerobes and sulfite-reducing Clostridium spp. decreased by ~3–4 log10 with increasing exposure. A dragonfly-optimised ε-support vector regression model (DA–SVR) predicted drying trajectories across modes (overall RMSE ≈ 0.79 percentage points; held-out RMSE ≈ 1.47; R2 ≥ 0.99). Overall, microwave thin-layer drying coupled with DA–SVR decision support enables constraint-based screening of sewage–sludge conditioning windows for logistics and thermal valorisation pathways; the framework can be extended to incorporate additional analytical endpoints where available.