Results in Engineering (Sep 2024)
Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants
Abstract
Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air contaminants compared to other photocatalysts due to its inherent inertness, cost-effectiveness, and photostability. To assess its effectiveness, laboratory examinations are frequently employed to measure the photocatalytic degradation rate of TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, and high costs. In literature, ensemble or standalone models are commonly used for assessing the performance of TiO2 photocatalytic degradation of water and air contaminants. Nonetheless, the application of metaheuristic hybrid models has the potential to be more effective in predictive accuracy and efficiency. Accordingly, this research utilized hybrid machine learning (ML) algorithms to estimate the photo-degradation rate constants of organic air pollutants using TiO2 nanoparticles and exposure to ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction optimization (NRO), differential evolution algorithm (DEA), human felicity algorithm (HFA), lightning search algorithm (LSA), Harris hawks algorithm (HHA), and tunicate swarm algorithm (TSA) were combined with random forest (RF) technique to establish the hybrid models. A database of 200 data points was acquired from experimental studies for model training and testing. Furthermore, multiple statistical indicators and 10-fold cross-validation were employed to examine the established hybrid model's accuracy and robustness. The TSA-RF model demonstrated superior prediction accuracy among the six suggested models, achieving an impressive correlation (R) of 0.90 and a lower root mean square error (RMSE) of 0.25. In contrast, the HFA-RF, HHA-RF, and NRO-RF models exhibited a slightly lower R-value of 0.88, with RMSE scores of 0.32. The DEA-RF and LSA-RF models, while effective, showed a marginally lower R-value of 0.85, with RMSE values of 0.45 and 0.44, respectively. Moreover, the SHapley Additive exPlanation (SHAP) results indicated that the degradation rates of air contaminants through photocatalysis were most notably influenced by factors such as the reactor sizes, photocatalyst dosage, humidity, and intensity.
