Coagulation-Flocculation/Pyrolysis Integrated System for Dye-Laden Wastewater Treatment: A Techno-Economic and Sustainable Approach

Abstract While several studies have employed coagulation-flocculation (CF) for textile wastewater (TW) treatment, conventional process optimization techniques cause insufficient pollution reduction and large sludge volume generation that deteriorate the environmental matrix and elevate the system’s operating cost. To avoid these drawbacks, this study focuses on optimizing an integrated CF/pyrolysis process using artificial intelligence technique and response surface methodology (RSM) for the dual benefit of TW treatment and biochar production. In the CF experiment, water hyacinth (WH) was employed as a bio-coagulant material for TW treatment under different pH, coagulant dosage, mixing speed, and settling time levels. Under the optimum CF conditions yielded by RSM and artificial neural network (ANN) models (initial pH: 5.5 vs. 5.7, WH dosage: 3.76 g/L vs. 3.5 g/L, settling time: 116 min vs. 102 min, and slow mixing speed: 25 rpm vs. 23 rpm), incomparable removal efficiencies for dye (87.3% vs. 91.3%) and turbidity (93.4% vs. 98.2%) were obtained. These removal efficiencies dropped to 83.5% and 87.6%, respectively, for operating the CF process using unoptimized operating factors. The pyrolysis of post-coagulation sludge yielded a carbon-rich biochar material characterized by a porous structure and abundant cationic microelements. The integrated performance of the CF/pyrolysis scheme under ANN-based optimal conditions achieved a shorter payback period of 5.2 years compared to RSM (5.7 years) and unoptimized (7.9 years) conditions. Furthermore, the optimized scheme supported several sustainable development goals that complied with clean water, good health, and climate change mitigation. Graphical Abstract