The intricate nature of various textile manufacturing processes introduces colored dyes, surfactants, and toxic chemicals that have been harmful to ecosystems in recent years. Here, a combination ozone-based advanced oxidation process (AOP) is coupled with a nanobubbles generator for the generation of ozone nanobubbles (NB) utilized the same to treat the primary effluent acquired from textile wastewaters. Here we find several key parameters such as chemical oxygen demand ammonia content (NH3), and total suspended solids indicating a substantial recovery in which the respective percentages of 81.1%, 30.81%, and 41.98%, upon 300 min residence time are achieved. On the other hand, the pH is shifted from 7.93 to 7.46, indicating the generation of hydrogen peroxide (H2O2) due to the termination reaction and the self-reaction of reactive oxygen species (ROS). We propose that the reactive oxygen species can be identified from the negative zeta potential measurement (−22.43 ± 0.34 mV) collected in the final state of treatment. The combined method has successfully generated ozone nanobubbles with 99.94% of size distributed in 216.9 nm. This highlights that enhancement of ozone’s reactivity plays a crucial role in improving the water quality of textile wastewater towards being technologically efficient to date.

In this paper, an application of nonlinear principal component analysis for online P300 extraction and classification is proposed. In order to cover the nonlinearity between the variables, a five-layer neural network is applied for feature extraction. The experimental results in this work show that the implementation of the proposed method achieves a very significant statistical improvement in extracting and classifying P300 components. After a short time of practice, most participants could learn to extract and classify the P300 wave with greater than 80% accuracy.

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