Insufficient cleaning in the food industry can create serious hygienic risks. However, when attempting to avoid these risks, food-processing plants frequently tend to clean for too long, at extremely high temperatures, or with too many chemicals, resulting in high cleaning costs and severe environmental impacts. Therefore, the optimization of cleaning processes in the food industry has significant economic and ecological potential. Unfortunately, in-situ assessments of cleaning processes are difficult, and the multitude of different cleaning situations complicates the definition of a comprehensive approach.

In this study, two methodological approaches for the comprehensive modeling of cleaning processes are introduced. The resulting models facilitate comparisons of different cleaning processes and they can be scaled up for processes with similar conditions, using cleaning time as a response. A dimensional analysis is performed to obtain general results and to allow transfer of the approaches to other cleaning situations. The models are established according to the statistical rules for the deduction of multiple regression equations for the prediction of the response based on the input parameters. The terms of the model equation are confirmed with a significance analysis. A machine learning approach is also used to create model equations with symbolic regression. Both methods and the obtained model equations are validated.

The two applied approaches reveal similar significant terms and models. Significant dimensionless numbers are the Reynolds number, the density number that describes the ratio of the density of the soil to the density of the cleaning agent, and the soil number, which is a new dimensionless number that characterizes the properties of food soils. The methodology of both approaches is transparent; therefore, the resulting equations can be compared and similarities are found. Both methods are deemed applicable for the computational modeling of cleaning processes in food industry.

食品行业清洁不足会造成严重的卫生风险。然而，当试图避免这些风险时，食品加工厂经常倾向于在极高的温度下或用太多的化学药品清洗太长时间，导致清洗成本高昂，并对环境造成严重影响。因此，食品工业清洁工艺的优化具有巨大的经济和生态潜力。不幸的是，清洁过程的现场评估是困难的，并且多种不同的清洁情况使综合方法的定义变得复杂。

在这项研究中，介绍了两种用于清洁过程综合建模的方法学方法。生成的模型便于比较不同的清洁过程，并且可以使用清洁时间作为响应，针对具有类似条件的过程按比例放大它们。执行尺寸分析以获得总体结果，并允许将方法转移到其他清洁情况。根据统计规则建立模型，以根据输入参数推导用于预测响应的多个回归方程。通过显着性分析确认模型方程的各项。机器学习方法也用于创建具有符号回归的模型方程。两种方法以及所获得的模型方程均得到验证。

两种应用的方法揭示了相似的重要术语和模型。重要的无量纲数是雷诺数，描述土壤密度与清洁剂密度之比的密度数和土壤数，这是表征食用土壤特性的新的无量纲数。两种方法的方法都是透明的。因此，可以对所得方程进行比较并找到相似之处。两种方法均适用于食品行业清洁过程的计算模型。