智能预测和优化的废水处理厂方法代表了我们管理废水的突破性转变。通过利用数据驱动的预测建模,自动化,和优化策略,它引入了一个全面的框架,旨在提高废水处理操作的效率和可持续性。这种方法包括各种基本阶段,包括数据收集和培训,集成创新的计算模型,如基于黑猩猩的GoogLeNet(CbG),数据处理,和性能预测,同时微调操作参数。所设计的模型是Chimp优化算法和GoogLeNet的混合。GoogLeNet是一种深度卷积架构,黑猩猩优化是基于黑猩猩行为的生物启发优化模型之一。它优化了运行参数,如pH值,剂量率,出水水质,和能源消耗,污水处理厂,修复GoogLeNet中的最佳设置。所设计的模型包括预处理和特征分析等过程,以对运行参数进行有效预测及其优化。值得注意的是,这种创新方法提供了几个关键优势,包括降低运营成本,改善环境结果,更有效的资源管理。通过不断的适应和完善,这种方法不仅优化了污水处理厂的性能,而且有效地应对不断变化的环境挑战,同时节约资源。它代表了在寻求有效和可持续的废水处理实践方面向前迈出的重要一步。RMSE,MAE,地图,建议技术的R2评分分别为1.103、0.233、0.012和0.002。此外,该模型显示,用电量下降到约1.4%,而温室气体排放量比现有技术显著下降到0.12%。
The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.