Food Processing Chain Control via ML Inference for 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 processes data from sensors to determine product characteristic values, generating commands for processing appliances to optimize operations, predict yield, quality, and CO2 footprint, thereby improving efficiency and safety.
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 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 relationships between parameters during training, enabling it to handle non-linear interactions that rule-based systems cannot capture, while providing adaptive optimization of processing chain operations.
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 and computational requirements increase
Solution Approach 1:
The machine learning model is trained offline using historical data to learn optimal control strategies before deployment. This preliminary training phase allows the model to capture complex parameter interactions in advance, so that during actual operation, the model can make real-time control decisions without requiring complex computational resources, thus reducing operational device complexity while maintaining high adaptability.
3Ease of manufacture
If traditional control algorithms are used, then the system is easier to implement and maintain, but productivity and operational efficiency are limited
Solution Approach 1:
The machine learning model autonomously optimizes processing chain operations by continuously receiving sensor data, processing it through the trained model, and generating control commands without human intervention. The system self-adjusts to changing conditions and optimizes productivity automatically, eliminating the need for manual tuning and maintenance while achieving superior operational efficiency compared to traditional algorithms.
4Reliability
If rule-based control is used, then the system is transparent and interpretable, but it fails to adapt to changing conditions and optimize operations dynamically
Solution Approach 1:
The system continuously monitors sensor data from the processing chain and feeds this information back to the machine learning model, which dynamically adjusts control commands based on current conditions. This closed-loop feedback mechanism enables the system to adapt to changing conditions in real-time, optimizing productivity while maintaining reliability through continuous monitoring and adjustment based on actual system state.
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.