# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 3 Discussion
### The Challenges of Detecting Solid Content in Concrete Wastewater Using Deep Learning  
Deep learning has the power to intelligently learn features and make accurate predictions from a large amount of image data, which is feasible and promising for offering significant improvements in accuracy and speed in the solid-content detection of concrete wastewater. However, challenges also exist:  
* Data availability: The quality and quantity of data required for training deep learning models are critical to achieving a high accuracy. But, obtaining adequate datasets for solid-content detection in concrete wastewater can be challenging due to the variability in composition and particle size distribution [28]. [PERSON] et al. [29] investigated the particle size distribution of concrete wastewater and demonstrated that the particle size distribution of concrete wastewater is bimodal with a fine mode, which is dominated by particles less than 10 \(\upmu\)m in diameter, and a coarse mode, which is dominated by particles greater than 100 \(\upmu\)m in diameter. The non-uniformity of the particle size may lead to difficulty in extracting image features. Consequently, collecting an abundant amount of available data is critical to ensure the model's reliability. * Model selection: The selection of an appropriate deep learning model for solid-content detection in concrete wastewater is critical due to the varying functions of numerous available deep learning architectures. The model should have the capability to capture the heterogeneity of the particles, the variability in particle size and density, and the interferences in the wastewater [28]. [PERSON] et al. [30] proposed a data-driven robust adaptive control system with deep learning (DRAC-DL) for enhancing the model adaptability, which used a robust controller to construct the closed-loop control scheme. [PERSON] et al. [31] established an event-driven model of predictive control with deep learning (EMPC-DL), which is defined to trigger the action control to efficiently improve the running performance of wastewater treatment processes (WWTPs). [PERSON] et al. [32] applied a novel hybrid model integrated with a first-principal deep learning model to sufficiently adapt to the wastewater system and accurately predict the nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs). To reach an optimum processing speed, the selective model and the corresponding parameters become a necessity. * Hardware requirements: Specialized hardware and significant computational power are a solid background when applying deep learning to detect solid content in concrete wastewater because deep learning models possess multiple layers and parameters that need to be optimized during training [33]. Therefore, high-end graphic processing units (GPUs) or tensor processing units (TPUs) are key components to efficiently and timely train the models, in addition to needing to meet the hardware requirements of high-end CPUs or GPUs, and having large amounts of memory (RAM) and fast storage devices such as solid-state drives (SSDs) [34]. * Generalization: The generalization of the deep learning model assures us that the algorithm is compatible with various sources of concrete wastewater due to the heterogeneity and variation in the wastewater. An investigation into the heterogeneity of concrete and its failure behavior presented an equivalent probabilistic model for the study of concrete failure [35]. [PERSON] et al. [36] developed several transient shock models to profile the variation in three critical water quality parameters (conductivity, temperature, and pH) in a real-time mode with flat, thin milli-electrode array (MEA) sensors, which demonstrated that the MEA sensors possess the capability of efficiently profiling the corresponding parameters from different sources of wastewater. Moreover, [PERSON] et al.