Food Processing Chain Control Using ML for Complex Parameter Interactions
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Solution Overview
Problem
Existing control systems for food factory processing chains struggle to adequately model the complex interactions between parameters, making it difficult to optimize operations effectively.
Innovation Solution
A controller using machine learning, specifically a neural network, collects data from sensors to generate 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 implement, but it cannot adequately model the complex interactions between parameters influencing operations
Solution Approach 1:
The patent replaces the traditional rule-based control system (mechanical/systematic approach) with a machine learning-based control system that uses neural networks and predictive models. This substitution enables the system to automatically learn and model complex interactions between multiple parameters without requiring explicit programming of rules, thereby resolving the contradiction between system simplicity and modeling capability.
Solution Approach 2:
The patent transforms the control approach from static rule-based parameters to dynamic machine learning models that continuously adapt parameters based on observed data. The system collects operational data, trains predictive models, and uses these models to dynamically adjust control parameters, enabling the system to capture complex parameter interactions while maintaining operational simplicity through automated decision-making.
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 patent segments the control system into distinct functional modules: data collection from sensors, predictive model training, machine learning inference engine, and command generation for processing appliances. This segmentation allows each module to be optimized independently and facilitates easier maintenance and updates, thereby reducing overall system complexity while preserving the advanced modeling capabilities of machine learning.
Solution Approach 2:
The patent introduces an intermediary layer consisting of predictive models and machine learning inference engines that bridge the gap between raw sensor data and control commands. This intermediary layer processes and interprets complex parameter interactions, transforming them into actionable control decisions, thereby managing system complexity by abstracting the computational burden from the direct control logic.
3Measurement precision
If more sensors are deployed to collect data for machine learning optimization, then the precision of product characteristic determination improves, but the device complexity and cost increase
Solution Approach 1:
The patent designs sensors and data collection mechanisms that serve multiple functions: monitoring product characteristics, tracking processing conditions, and detecting environmental parameters. This multi-functionality reduces the need for separate specialized sensors for each measurement type, thereby improving measurement precision across multiple parameters while limiting the increase in device complexity and cost.
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.