Food Process Control Using Correlated Spectral and Thermal Sensing
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Solution Overview
Problem
Current food manufacturing processes face challenges in achieving repeatable and controlled outcomes due to the complex nature of food products, which require significant fine-tuning and are affected by variations in starting conditions, leading to inefficiencies and quality control issues in automated systems.
Innovation Solution
A control system that integrates multiple product sensors, plant sensors, and control devices with a machine learning module to process and correlate spectrographic and thermographic data, allowing for real-time adjustments and proactive control of subprocesses, enabling timely and automatic regulation of food treatment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple sensors and machine learning modules are integrated for real-time monitoring and control, then manufacturing precision and process repeatability are improved, but device complexity increases
Solution Approach 1:
The system segments the food manufacturing process into multiple subprocesses, each monitored by dedicated sensors (product sensors for material characteristics, plant sensors for machine operating quantities). This segmentation allows independent optimization and control of each subprocess while maintaining overall process coherence, improving repeatability without overwhelming complexity.
Solution Approach 2:
The system implements comprehensive feedback loops where sensor data from each subprocess is continuously fed to control devices that adjust process parameters in real-time. The machine learning module analyzes this feedback data to optimize control decisions, ensuring high manufacturing precision through dynamic adaptation while managing complexity through automated closed-loop control.
2Manufacturing precision
If extensive fine-tuning of process parameters is performed to achieve desired outcomes, then manufacturing precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The machine learning module is pre-trained with historical process data and reference information to establish optimal control strategies before actual production begins. This preliminary action allows the system to immediately apply learned knowledge to new production runs, minimizing setup time while maintaining high precision through data-driven parameter optimization.
Solution Approach 2:
The system performs self-tuning through the machine learning module that automatically adjusts process parameters based on real-time sensor data and historical patterns. This self-service capability eliminates the need for extensive manual fine-tuning by operators, reducing setup time while maintaining manufacturing precision through automated adaptive control.
3Reliability
If classic feedback control is used to regulate subprocesses based on downstream sensor data, then control of individual subprocesses is achieved, but the entire process cannot be optimized in a coordinated manner
Solution Approach 1:
The system merges individual subprocess controls into a unified control framework where the machine learning module receives and integrates data from all subprocesses simultaneously. This holistic approach allows coordinated optimization across the entire manufacturing process, improving overall productivity while maintaining reliable control of individual subprocesses through integrated decision-making.
Solution Approach 2:
The machine learning module serves multiple functions: it monitors individual subprocess parameters, detects anomalies across the entire process, optimizes resource allocation, and coordinates control decisions across all subprocesses. This multi-functionality enables both reliable subprocess control and overall process optimization without requiring separate control systems for each function.
4Adaptability or versatility
If traditional knowledge and experimentation are used to develop food manufacturing processes, then adaptability to product variations is improved, but loss of time for process development increases
Solution Approach 1:
The system replaces traditional manual experimentation and knowledge-based process development with automated machine learning algorithms that analyze sensor data to identify optimal processing conditions. This substitution maintains adaptability to product variations through data-driven insights while dramatically reducing development time by eliminating iterative trial-and-error experimentation.
Solution Approach 2:
The machine learning module creates digital copies or models of optimal processing conditions based on historical data and reference processes. These digital twins can be rapidly replicated and adapted to new products or variations without physical experimentation, maintaining versatility while reducing development time through virtual modeling and simulation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This system provides precise control and optimization of food processing by identifying deviations and anomalies, ensuring consistent product quality and reducing the need for extensive fine-tuning, thereby improving process efficiency and repeatability without requiring significant changes to existing equipment or automation.
Implementation Method 1
at least a spectrograph apt to acquire spectrographic spectrum of a food material being processed in a subprocess
Implementation Method 2
a thermograph apt to acquire thermographic spectrum of food material in said same subprocess
Data Source
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AI summary
A control method and system of a food treating process made of a plurality of sub-processes is disclosed. The system comprises a plurality of product sensors, apt to detect characteristic sensor data of a food material being processed in each single subprocess, a plurality of plant sensors, apt to detect sensor data relating to operating quantities of single treatment machines which control each of said subprocesses in a plant, a plurality of control devices, apt to adjust process parameters of said treatment machines, a main hardware and software architecture apt to process said characteristic sensor data of food material depending on said sensor data of operating quantities of treatment machines for a plurality of subprocesses making up in sequence said treatment process, and a database structure, wherein all said sensor data of food material and operating quantities for said plurality of subprocesses are stored for a plurality of treatment processes, wherein said main hardware and software architecture comprises a machine learning module which defines a driving metric of said subprocess control devices based on said characteristic sensor data of food material in a plurality of subprocesses making up in sequence a treatment process, said product sensors comprising at least a spectrograph apt to acquire spectrographic spectrum of a food material being processed in a sub-process, and a thermograph apt to acquire thermographic spectrum of food material in said same sub-process, said spectrographic spectrum and thermographic spectrum being processed in said main hardware and software architecture in a way correlated and combined in time, and said driving metric of said sub-process control devices is defined based on a comparison of a current spectrographic spectrum and current thermographic spectrum with a reference spectrographic spectrum and reference thermographic spectrum.