The recalcitrant nature of the industrial dyes poses a significant challenge to existing treatment technologies due to the stringent environmental regulations. This combined with the inefficiency of a single treatment method has led to the implementation of the combination of primary, secondary, and tertiary treatment processes, which fails during complex secondary aeration processes due to variable pH loads of industrial effluent wastewater. This article presents a modified design methodology of a pilot-scale micro-pre-treatment unit using a solar-triggered advanced oxidation process reactor that both effectively controls the influent variability at the source and mitigates textile effluents for making the discharge reusable for different industrial purposes. The proposed modified combination technique of controlled serial processes inclusive of primary, secondary, and tertiary treatment steps with ZnO/ZnO-GO NanoMat-based advanced oxidation process demonstrates complete remediation of industrial grade effluent with effective reuse of the discharge. Further, a reliable prediction model for estimating water quality parameter using machine learning models are proposed. Multi-linear regression and Artificial Neural network modeling provide simple, accurate, and robust prediction capabilities, which are evaluated for the efficiency of the processes. The generated prediction models capture the output parameters within an acceptable level of accuracy \(({{\boldsymbol{R}}}_{{adj}}^{{\bf{2}}}\, >\, 0.90)\) and allow compliance with the discharge Inland Water Discharge Standards (IWDS).

由于严格的环境法规，工业染料的顽固性质对现有的处理技术提出了重大挑战。再加上单一处理方法效率低下，导致需要实施一级、二级和三级处理工艺的组合，但由于工业废水的 pH 负荷变化，在复杂的二级曝气工艺中这种方法会失败。本文提出了一种使用太阳能触发高级氧化反应器的中试规模微预处理装置的改进设计方法，该方法既能有效控制源头的进水变化，又能减少纺织废水的排放，使排放物可重复用于不同的工业用途。所提出的受控串行工艺的改进组合技术，包括初级、二级和三级处理步骤与基于 ZnO/ZnO-GO NanoMat 的高级氧化工艺，证明了工业级废水的完全修复和排放物的有效再利用。此外，提出了一种使用机器学习模型估计水质参数的可靠预测模型。多元线性回归和人工神经网络建模提供简单、准确和强大的预测功能，可评估流程的效率。生成的预测模型在可接受的精度水平内捕获输出参数\(({{\boldsymbol{R}}}_{{adj}}^{{\bf{2}}}\, >\, 0.90)\ ）并允许遵守内陆水域排放标准（IWDS）。