Prediction of heel build-up on activated carbon using machine learning.

Determining the long-term performance of adsorbents is crucial for the design of air treatment systems. Heel buildup i.e., the accumulation of non-desorbed/ non-desorbable adsorbates and their reaction byproducts, on the surface/pores of the adsorbent is a primary cause of adsorption performance deterioration. However, due to the complexity of heel buildup mechanisms, theoretical models have yet to be developed to map the extent of heel buildup to the adsorption/desorption parameters. In this work, two machine learning (ML) algorithms (XGBoost and neural network (NN)) were applied to predict volatile organic compounds (VOCs) cyclic heel buildup on activated carbons (ACs) by considering the adsorbent characteristics, adsorbate properties and regeneration conditions. The NN algorithm showed better performance in prediction of cyclic heel buildup (R2 = 0.94) than XGBoost (R2 = 0.81). To analyze interaction between heel buildup and adsorbent characteristics, adsorbate properties, and regeneration conditions, partial dependency plots were generated. The proposed ML-based heel prediction methods can be ultimately used to: (i) optimize adsorption/desorption operating conditions to minimize heel buildup on activated carbon in cyclic adsorption/desorption processes and (ii) quickly screen various adsorbents for efficient adsorption/desorption of a particular family of VOCs by excluding adsorbents prone to high heel formation.

Hydrothermal/solvothermal synthesis and treatment of TiO2 for photocatalytic degradation of air pollutants: Preparation, characterization, properties, and performance.

Photocatalytic oxidation (PCO) is a well-known technology for air purification and has been extensively studied for removal of many air pollutants. Titanium dioxide (TiO2) is the most investigated photocatalyst in the field of environmental remediation owed to its chemical stability, non-toxicity, and suitable positions of valence and conduction bands. Various preparation techniques including sol-gel, flame hydrolysis, water-in-oil microemulsion, chemical vapour deposition, solvothermal, and hydrothermal have been employed to obtain TiO2 materials. Hydro-/Solvothermal (HST) synthesis, focus of the present work, can be defined as a preparation method in which crystal growth occurs in a solvent at relatively low temperature (<200 °C) and above atmospheric pressure. This paper aims to provide a comprehensive and critical review of current knowledge regarding the application of HST synthesis for fabrication of TiO2 nanostructures for indoor air purification. TiO2 nanostructures are categorized from the morphological standpoint (e.g. nanoparticles, nanotubes, nanosheets, and hierarchically porous) and discussed in detail. The influence of preparation parameters including hydrothermal time, temperature, pH of the reaction medium, solvent, and calcination temperature on physical, chemical, and optical properties of TiO2 is reviewed. Considering the complex interplay among catalyst properties, a special emphasis is placed on elucidating the interconnection between various photocatalyst features and their impacts on photocatalytic activity.