Investigation and modeling of experimental parameters and catalyst effect in gasification of tannery industry treatment sludges

The increasing global demand for energy, the environmental impacts of fossil fuels, and climate change have heightened the need for sustainable energy solutions. In this context, biomass gasification technology is critically important for waste disposal, energy recovery, and reducing carbon emissions. In this doctoral study, two types of wastes from the leather industry (tannery sludge cake and leather trimmings) were gasified in an updraft fixed-bed reactor, and the process outputs were analyzed using artificial intelligence (AI) techniques. Physical and chemical analyses showed moisture contents of 10% for sludge cake and 8% for leather trimmings. Leather trimmings exhibited higher volatile matter (95%), calorific value (4960 kcal/kg), carbon (48.8%), and hydrogen (7.2%) contents compared to sludge cake, making them more suitable for synthesis gas production. Thermogravimetric analysis (TGA) supported these findings, demonstrating an 88% mass loss in leather trimmings. Scanning electron microscopy (SEM) indicated that the microstructures of both wastes enhanced gasification efficiency. Fourier-transform infrared spectroscopy (FTIR) analysis confirmed that both materials were rich in organic content. In the second phase of the study, gas yields were investigated using marble powder and Al-Ni catalysts. Maximum gas yield (74%) was achieved with 5% marble powder at 1000°C, whereas higher catalyst concentrations adversely affected yield. High-temperature conditions (1000°C) improved hydrogen production (36%), while the use of pure oxygen increased CO (28%) and H₂ (37%) yields. AI-based modeling (using Random Forest, SVR, KNN, and CNN-LSTM) revealed that KNN achieved the highest accuracy in hydrogen prediction (R²=0.987). The CNN-LSTM model excelled in predicting CO, achieving an R² of 0.986. Consequently, this research demonstrated that converting leather industry wastes into energy through gasification is both technically and environmentally viable, highlighting the effectiveness of AI models in process optimization.