Two complementary methods for the computational modeling of cleaning processes in food industry Abstract   Insufficient cleaning in the food industry can create serious hygienic risks. However, when attempting to avoid these risks, foodprocessing 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, insitu 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.

Extracting Nonstationary Features for Process Data Analytics and Application in Fouling Detection Abstract   With advances in database and machine learning, datadriven methods are becoming more feasible for process monitoring. Among these methods, latent variable modeling approaches, which extract representative latent features to capture the essential behavior of process data, are more favorable. In this study, understanding the cumulating behavior in chemical processes (the nonstationary trend) is considered as the desired latent feature. Based on an emerging data analysis technique, we propose a novel feature extraction method. Instead of focusing on the stationary features, the proposed method focuses on the nonstationary trend using cointegration analysis. Its learning procedure is illustrated through maximum likelihood estimation, and its effectiveness is investigated through numerical simulations. Furthermore, by using the proposed approach, an application of monitoring fouling buildup is discussed, where the principal component analysis is appended for improving robustness. Practicality of this application is demonstrated through applications on three operating steam generators.