Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation

Abstract

Concrete wastewater from mixing stations leads to environment contamination due to its high alkalinity. The wastewater can be reused if its solid content is accurately and timely detected. However, investigations into the traditional methods for wastewater reuse have demonstrated that they are time consuming and not efficient. Therefore, the exact acquirement of solid content in concrete wastewater becomes a necessity. Recent studies have shown that deep learning has been successfully applied to detect the concentration of chemical solutions and the particle content of suspending liquid. Moreover, deep learning can also be used to recognize the accurate water level, which facilitates the detection of the solid–liquid separation surface after wastewater sedimentation. Therefore, in this article the feasibility and challenges of applying deep learning to detect the solid content of concrete wastewater were comprehensively evaluated and discussed. Finally, an experimental setup was proposed for future research, and it indicated that transfer learning, data augmentation, hybrid approaches, and multi-sensor integration techniques can be selected to facilitate future experimental performances.

Keywords

• solid content
• mixing station
• concrete wastewater
• deep learning
• detection