ML Controller for Food Processing Chain Parameter Interactions
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Solution Overview
Problem
Current control systems in food factories struggle to optimize processing chain operations due to the complexity of interactions between various parameters, making it difficult for human-designed rules to effectively model and adapt to these interactions, leading to inefficiencies and safety concerns.
Innovation Solution
A machine learning-based controller using a neural network inference engine that collects data from sensors to determine product characteristic values and generates commands for processing appliances, optimizing operations by predicting yield, quality, and CO2 footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a set of rules is used to control processing appliances, then the control system is simple and easy to understand, but it cannot adequately model the complexity of interactions between parameters influencing processing chain operations
Solution Approach 1:
The patent replaces the mechanical rule-based control system with a machine learning model (neural network) that processes sensor data to generate control commands. The machine learning model learns complex parameter interactions from training data without requiring explicit programming of interaction rules, thereby maintaining system simplicity while dramatically improving adaptability to model complex relationships between processing parameters.
2Adaptability or versatility
If machine learning is used to optimize processing chain operations, then the ability to model complex parameter interactions improves, but the device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it processes diverse sensor inputs, models complex parameter interactions, generates optimized control commands, and adapts to different processing scenarios. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified controller, improving adaptability while limiting the increase in overall device complexity.
Solution Approach 2:
The machine learning model is trained offline on historical data and then deployed to autonomously make control decisions without requiring continuous human intervention or complex real-time rule updates. The system self-adjusts to changing conditions by processing sensor data and generating appropriate commands, reducing the operational complexity despite the underlying model complexity.
3Ease of manufacture
If traditional control algorithms are used, then the system is easier to implement and maintain, but it leads to inefficiencies and safety concerns in optimizing processing chain operations
Solution Approach 1:
The machine learning model is trained in advance on comprehensive datasets that capture various processing scenarios and parameter interactions. This preliminary training phase allows the model to learn optimal control strategies before deployment, enabling it to efficiently handle real-time processing optimization without requiring complex runtime algorithms or frequent maintenance updates.
Solution Approach 2:
The system continuously monitors sensor data from processing appliances and uses this feedback to adjust control commands generated by the machine learning model. This closed-loop feedback mechanism enables the system to adapt to changing conditions and optimize processing efficiency dynamically, addressing the inefficiencies of traditional open-loop or simple feedback control algorithms.
4Use of energy by moving object
If rules-based control is used, then the system requires less computational resources, but it cannot adapt to changing conditions and optimize operations effectively
Solution Approach 1:
The control system is segmented into offline training phase and online inference phase. The computationally intensive machine learning model training occurs offline when computational resources are abundant, while the deployed model performs lightweight inference in real-time with minimal computational overhead. This segmentation allows the system to achieve high adaptability during deployment without consuming excessive computational resources during operation.
Data Source
AI summary
Computing device and method using machine learning to optimize operations of a processing chain of a food factory. The computing device collects data representative of characteristics of a product processed by the processing chain. At least some of the collected data are received from one or more sensor monitoring operations of the processing chain. The computing device determines at least one product characteristic value based on the collected data. The computing device executes the machine learning inference engine, which uses a predictive model for inferring command(s) for controlling processing appliance(s) of the processing chain based on inputs. The inputs comprise the at least one product characteristic value. The computing device transmits the command(s) to the processing appliance(s) of the processing chain. Examples of product characteristic values comprise: a product temperature, a product humidity level, a product geometric characteristic, a product weight, and a product defect measurement.