A novel algorithm for optimizing hydrocyclone operations in the decontamination of potentially toxic elements in soils

We present the Three-Parameter Penalized Attributive Analysis for Upgrading (3PPAA-U) method as a tool for selecting the Best Upgrading Condition (BUC) in process engineering. Conventional approaches tend to consider only maximizing recovery (ε) and minimizing yield (γc); in contrast, the proposed 3PPAA-U introduces and seeks to maximize a third parameter, the grade (λ). This multi-parameter approach has not yet been explored in existing literature. In addition to controlling multiple parameters, the method is also superior to others as it includes inverse standard deviation weighting to avoid the distortion of results due to data dispersion. This reduces the possibility of drawing conclusions based on extreme values. Furthermore, the method can be used with a target-to-distance correction to optimize separation for multi-component feeds. To illustrate our method, we present a practical application of 3PPAA-U. Soil contaminated with potentially toxic elements (PTEs) was subject to hydrocycloning under 12 different experimental conditions. Results of these 12 experiments were compared using 3PPAA-U and conventional methods to identify the best upgrading conditions (BUC). Analysis reveals that the 3PPAA-U approach offers a simple and effective criterion for selecting BUC. Furthermore, 3PPAA-U has uses beyond soil remediation. It offers a versatile tool for optimizing operations across various processing and manufacturing environments offering a way to manage factors such as concentration, temperature, pressure, pH, Eh, grain size, and even broader environmental and economic considerations.

​We present the Three-Parameter Penalized Attributive Analysis for Upgrading (3PPAA-U) method as a tool for selecting the Best Upgrading Condition (BUC) in process engineering. Conventional approaches tend to consider only maximizing recovery (ε) and minimizing yield (γc); in contrast, the proposed 3PPAA-U introduces and seeks to maximize a third parameter, the grade (λ). This multi-parameter approach has not yet been explored in existing literature. In addition to controlling multiple parameters, the method is also superior to others as it includes inverse standard deviation weighting to avoid the distortion of results due to data dispersion. This reduces the possibility of drawing conclusions based on extreme values. Furthermore, the method can be used with a target-to-distance correction to optimize separation for multi-component feeds. To illustrate our method, we present a practical application of 3PPAA-U. Soil contaminated with potentially toxic elements (PTEs) was subject to hydrocycloning under 12 different experimental conditions. Results of these 12 experiments were compared using 3PPAA-U and conventional methods to identify the best upgrading conditions (BUC). Analysis reveals that the 3PPAA-U approach offers a simple and effective criterion for selecting BUC. Furthermore, 3PPAA-U has uses beyond soil remediation. It offers a versatile tool for optimizing operations across various processing and manufacturing environments offering a way to manage factors such as concentration, temperature, pressure, pH, Eh, grain size, and even broader environmental and economic considerations. Read More