The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R² = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.

中文翻译：

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预测城市污水处理厂进水废水中FOG含量的随机森林模型

在家中以及在不同的商业和工业活动中，由于食物的制备，废水中油脂，油脂的含量已成为一个日益严重的问题。除了卫生网络中产生的堵塞之外，它还代表了废水处理厂（WWTP）的运行困难，能源和维护成本增加以及下游处理过程的性能恶化。这些设施的预处理阶段负责消除大部分FOG，以避免这些问题。但是，到目前为止，优化仅限于正确的设计和初始安装尺寸。当废水入口的特性发生变化时，操作人员将有经验来适当调整此初始阶段，以调整过程。主要困难是影响这些变化的因素很多。在这项工作中，提出了进水中FOG含量的预测模型。该模型能够正确预测训练中98.45％的案例和测试中72.73％的案例，相对误差为10％。它是使用随机森林（RF）开发的，并获得了良好的效果（R ² = 0.9348和RMSE = 0.089（测试中）将有可能改善此初始阶段的操作。到目前为止，尚未在预处理参数的建模中使用此机器学习算法的良好功能。这种新颖的方法将在全球范围内提高此类设备的性能，从而允许及早采用预处理工艺的调整措施，以消除最大量的FOG。