Continuous Freezer Control for Precise Ice Cream Weight and Volume
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Solution Overview
Problem
Existing continuous freezers in ice cream production lines struggle to consistently control the weight and/or volume of the output ice cream, leading to inefficiencies and potential overfilling.
Innovation Solution
A machine learning model is used to generate weight and volume functions based on sensor data, combined with an optimization function to identify candidate parameter values that meet specific targets and constraints, allowing precise control of the continuous freezer operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the production line operates with multiple parameter adjustments, then the ice cream quality can be optimized, but the complexity of controlling the continuous freezer increases
Solution Approach 1:
A machine learning model acts as an intermediary between sensor data and control decisions, processing multiple parameter inputs and translating them into optimized control recommendations, thereby simplifying the overall control architecture
Data Source
Figure 1~2
AI summary
A computer-implemented method (200) for supporting an operation of a production line (100) for producing ice cream (IC) is disclosed. The production line (100) comprises a continuous freezer arrangement (104). The method comprises receiving (202) sensor data collected during the operation of the continuous freezer arrangement (104), wherein the sensor data reflects parameter values of the production line (100), generating (204) a weight function and/or a volume function reflecting the output weight (W) and/or the output volume (V) as a function of the parameter values defined by a machine learning model (114), wherein the machine learning model (114) has been trained by means of reference sensor data indicative of weight (W) and/or volume (V) of ice cream (IC) produced by the production line (100) associated with the reference sensor data, and applying (206) an optimization function (115) to identify candidate parameter values of the processing line (100) that is optimized with respect to the weight function and/or volume function in view of a mean target of the weight (W) and/or a mean target of the volume (V), and/or a deviation target of the weight (W) and/or a deviation target of the volume (V).