CIP Cycle Quality Prediction Using Step-Specific ML Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing CIP systems lack robust mechanisms for evaluating the quality of cleaning cycles, failing to provide detailed information on the cleaning effectiveness of industrial production lines and specific equipment, leading to potential operational inefficiencies and production issues.
Innovation Solution
A computer-implemented method using trained process-specific and equipment-specific machine learning models to predict the quality of CIP cycles by analyzing CIP station and equipment parameters, respectively, with outputs aggregated to generate a quality indicator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are implemented to predict CIP cycle quality, then measurement precision and cleaning quality evaluation are improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that process existing CIP station parameters and equipment parameters to generate quality predictions. These models act as mediators between the physical cleaning process and the evaluation system, transforming raw sensor data into meaningful quality indicators without requiring direct modification of the cleaning process itself.
Solution Approach 2:
The patent replaces traditional mechanical or chemical measurement methods for evaluating cleaning quality with computational machine learning models. Instead of using complex physical measurement devices to directly assess cleaning effectiveness, the system uses software-based models that analyze parameter patterns to predict quality, substituting physical measurement complexity with computational analysis.
2Manufacturing precision
If multiple machine learning models are used for different CIP steps and equipment, then manufacturing precision of cleaning quality prediction is improved, but device complexity increases
Solution Approach 1:
The patent divides the CIP cycle into distinct steps (pre-flush, caustic wash, acid wash, rinse) and applies specific machine learning models to each step. Additionally, separate models are created for different equipment types (heat exchangers, separators, tanks). This segmentation allows each model to be optimized for its specific function, improving overall prediction accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent creates a universal framework where machine learning models can be applied across multiple CIP steps and equipment types. The same technical approach and model architecture can handle different cleaning steps and equipment, providing multi-functionality. This universality reduces the need for completely separate systems for each scenario, managing complexity while maintaining precision.
3Loss of information
If CIP station parameters and equipment parameters are both analyzed, then information completeness is improved, but device complexity increases
Solution Approach 1:
The patent merges two data sources - CIP station parameters (from the cleaning system itself) and equipment parameters (from the industrial equipment being cleaned) - into a unified analysis framework. The machine learning models process both parameter sets simultaneously, combining information about the cleaning process with information about the equipment state to provide comprehensive quality assessment. This merging captures complete information while using integrated models to manage processing complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure relates to a computer-implemented method for predicting the quality of a Cleaning-in-Place (CIP) cycle of an industrial production line comprising at least one equipment and at least one CIP station, the method comprising the steps of: executing, by the CIP station, the CIP cycle; identifying one or more CIP steps of the CIP cycle; selecting, for each of the identified one or more CIP steps, a trained process-specific machine learning model associated with the CIP station according to the identified one or more CIP steps; passing one or more CIP station-specific parameters to the trained process-specific machine learning model; and predicting the quality of the CIP cycle based on the one or more CIP station-specific parameters, wherein the quality is reflected by an output of each of the trained process-specific machine learning models. In addition, the present disclosure relates to a corresponding control computing system, to an industrial production line comprising or communicatively coupled to at least one control computing system, to computer-implemented methods for training a process-specific and an equipment-specific machine learning model, a trained machine learning model as well as to corresponding computer programs.