Facile Synthesis of ZnO-CeO2 Heterojunction by Mixture Design and Its Application in Triclosan Degradation: Effect of Urea.

In this study, simplex centroid mixture design was employed to determine the effect of urea on ZnO-CeO. The heterojunction materials were synthesized using a solid-state combustion method, and the physicochemical properties were evaluated using X-ray diffraction, nitrogen adsorption/desorption, and UV-Vis spectroscopy. Photocatalytic activity was determined by a triclosan degradation reaction under UV irradiation. According to the results, the crystal size of zinc oxide decreases in the presence of urea, whereas a reverse effect was observed for cerium oxide. A similar trend was observed for ternary samples, i.e., the higher the proportion of urea, the larger the crystallite cerium size. In brief, urea facilitated the co-existence of crystallites of CeO and ZnO. On the other hand, UV spectra indicate that urea shifts the absorption edge to a longer wavelength. Studies of the photocatalytic activity of TCS degradation show that the increase in the proportion of urea favorably influenced the percentage of mineralization.

Application of machine learning in anaerobic digestion: Perspectives and challenges.

Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.