modelEAU, Université Laval, 1065, Avenue de la Médecine, Québec, QC, G1 V 0A6, Canada E-mail:
Water Sci Technol. 2022 May;85(9):2722-2736. doi: 10.2166/wst.2022.095.
Modelling, automation, and control are widely used for water resource recovery facility (WRRF) optimization. An influent generator (IG) is a model, aiming to provide the flowrate and pollutant concentration dynamics at the inlet of a WRRF for a range of modelling applications. In this study, a new data-driven IG model is proposed, only using routine data and weather information, and without need for any additional data collection. The model is constructed by an artificial neural network (ANN) and completed with a multivariate regression to generate time series for certain pollutants. The model is able to generate flowrate and quality data (TSS, COD, and nutrients) at different time scales and resolutions (daily or hourly), depending on various user objectives. The model performance is analyzed by a series of statistical criteria. It is shown that the model can generate a very reliable dataset for different model applications.
建模、自动化和控制广泛应用于水资源回收设施（WRRF）的优化。进水生成器（IG）是一种模型，旨在为一系列建模应用提供 WRRF 入口处的流量和污染物浓度动态。在本研究中，提出了一种新的数据驱动的 IG 模型，仅使用常规数据和天气信息，无需任何额外的数据收集。该模型由人工神经网络（ANN）构建，并通过多元回归完成，以生成某些污染物的时间序列。该模型能够根据各种用户目标，在不同的时间尺度和分辨率（每日或每小时）下生成流量和质量数据（TSS、COD 和养分）。通过一系列统计标准分析了模型性能。结果表明，该模型可以为不同的模型应用生成非常可靠的数据集。
