A Python-Based Tool for Industrial Water Integration

The development and implementation of measures for pollution control and resource optimization play a crucial role in environmental resilience. This work is an attempt to create a standardized Python-based testing environment, which contributes to the challenges related to water conservation. The model is focused on diluting supplied freshwater by sending reused and regenerated water to a particular industrial unit. While existing approaches consider stationary concentrations of contaminants during mixing, our approach overcomes this drawback by the iterative calculation of a newly introduced parameter of “the conditional concentration”. This concept pertains to the efficiency of contaminant elimination within a regeneration unit quantified by the removal ratio. The model consists of two parts: first, it reveals the fixed value for the conditional concentration considering another novel parameter of “the coefficient of conditional concentration”. The coefficient depends on the quality of the reused or regenerated water and determines the appropriateness of a selected stream for the dilution. The second step aims to achieve maximal freshwater minimization by mixing optimal ratios of the selected streams. The model is tested on a real-world case study of the oil refinery in Kazakhstan using the single-contaminant approach.