Efficient Photocatalytic Reduction of Cr(VI) on N-Doped Sludge/Cellulose-Derived Porous Carbon Using Synergistic Experimental–Deep Learning Approach

The photocatalytic reduction of hexavalent chromium (Cr(VI)) in wastewater represents a critical environmental challenge, given its high toxicity and mobility in wastewater. Conventional optimization methods relying on repetitive batch experiments with excessive reagent consumption. Herein, we proposed a synergistic experimental–deep learning approach to realize efficient photocatalytic reduction of Cr(VI). Sludge and cellulose precursors were used to prepare N-doped porous carbon through a green fabrication method at low temperature. After optimizing the composition and experimental factors, the removal efficiency of Cr(VI) could reach 92.7% after visible light irradiation in an acidic environment. The high efficiency originated from the coupled adsorption and photocatalytic mechanism. Innovatively, after comparing three algorithms, TCN and Transformer were employed to predict the desirable removal efficiency with <5% prediction error under different reaction conditions. This work highlights the novel experimental–deep learning method for heavy metal remediation using N-doped waste-derived porous carbon.