The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

中文翻译：

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使用人工神经网络（ANN）预测废水处理厂排放的气味浓度

废水处理厂（WWTP）排放的气味是一个重要的环境问题。气味排放速率的估计难以检测和量化。为了解决这个问题，已经采用了各种方法，包括开发排放因子和使用密闭腔室进行测量。然而，气味排放的评估涉及巨大的人力，时间和成本。人工神经网络（ANN）被认为是在非线性数据和基于这些相关性的未来数据预测之间找到相关性的有效方法。由于其有用性，ANN用于解决科学和工程学各个学科中的复杂问题。在这项研究中，开发了一种使用ANN预测污水处理厂中气味浓度的方法。ANN基于水质数据（例如生物需氧量，溶解氧和pH）预测了污水处理厂排放的气味浓度。来自污水处理厂的水质和气味浓度数据是在春季，夏季和秋季按季节测量的，这些数据被用作ANN模型的输入变量。将ANN模型预测的气味与实测数据进行比较，以评估预测的准确性。提出了提高预测准确性的建议。将ANN模型预测的气味与实测数据进行比较，以评估预测的准确性。提出了提高预测准确性的建议。将ANN模型预测的气味与实测数据进行比较，以评估预测的准确性。提出了提高预测准确性的建议。