Applying machine learning random forest (RF) method in predicting the cement products with a co-processing of input materials: Optimizing the hyperparameters.

Co-processing recycled waste during cement production, i.e., using alternative materials such as secondary raw materials or secondary raw fuels, is widely practiced in developed countries. Alternative raw materials or fuels contain high concentrations of heavy metals and other hazardous chemicals, which might lead to the potential for dangerous heavy metals and hazardous chemicals to be transferred to clinker or cement products, resulting in exposure and emissions to people or the environment. Managing input materials and predicting which inputs affect the final concentration is essential to prevent potential hazards. We used the data of six heavy metals by input raw materials and input fuels of cement manufacturers in 2016-2017. The concentrations of Pb and Cu in cement were about 10-200 times and 4 to 200 times higher than other heavy metals (Cr, As, Cd, Hg), respectively. We profiled the influence of heavy metal concentration of each input material using the principal component analysis (PCA), which analyzed the leading causes of each heavy metal. The Random Forest (RF) ensemble model predicted cement heavy metal concentrations according to input materials. In the case of Cu, Cd, and Cr, the training performance showed R square values of 0.71, 0.71, and 0.92, respectively, as a result of predicting the cement heavy metal concentration according to the heavy metal concentration of each cement input material using the RF model, which is a machine learning model. The results of this study show that the RF model can be used to predict the amount and concentration of alternative raw materials and alternative fuels by controlling the concentration of heavy metals in cement through the concentration of heavy metals in the input materials.

Photocatalytic removal of gaseous nitrogen oxides using WO3/TiO2 particles under visible light irradiation: Effect of surface modification.

Photocatalytic nanoparticles have been receiving considerable attention for their potential use in many environmental management applications, including urban air quality control. This paper investigates the performance of surface modified WO3/TiO2 composite particles in removing gaseous nitrogen oxides (NOx) under visible light irradiation. The WO3/TiO2 composite particles were synthesized using a modified wet chemical method with different concentrations of NaOH solution used as a surface modification agent for the host TiO2 particles. The NOx removal efficiency of the WO3/TiO2 particles was evaluated using a lab-scale continuous gas flow photo-reactor with a gas contact time of 1 min. Results showed that surface modification using NaOH can enhance the photocatalytic activity of the WO3/TiO2 particles. The NOx removal efficiency of the surface modified WO3/TiO2 was greater than 90%, while that of WO3/TiO2 particles prepared by the conventional wet chemical method was ∼75%. The enhanced removal efficiency might be attributed to the formation of oxygen vacancies on the TiO2 surface, providing sites for WO3 particles to effectively bind with TiO2. However, excess amount of NaOH >3 M deteriorated the photocatalytic performance due to the increased agglomeration of the host TiO2 particles.

Structural improvements on hydrodynamic separators: a computational fluid dynamics approach.

Hydrodynamic separators (HDSs) have been used extensively to reduce stormwater pollutants from urbanized areas before entering the receiving water bodies. They primarily remove particulates and associated pollutants using gravity settling. Two types of HDSs with different structural configurations of the inner vortex-inducing components were presented in this study. One configuration consisted of a dip cylindrical plate with a center shaft while the other one has a hollow screen inside. With the help of computational fluid dynamics, the performance of these different types of HDSs have been evaluated and comparatively analyzed. The results showed that the particle removal efficiency was better with the cylindrical plate type HDSs than the screen type HDSs because of the larger swirling flow regime formed inside the device. Plate type HDSs were found more effective in removing fine particles (∼50 µm) than the screen type HDSs that were only efficient in removing large particles (≥250 µm). Structural improvements in a HDS such as increase in diameter and angle of the inlet pipe can enhance the removal efficiencies by up to 20% for plate type HDS while increase in the screen diameter can increase removal efficiencies of the screen type HDS.