Food Process Control Using Spectrographic and Thermographic Feedback
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Solution Overview
Problem
Current food manufacturing processes face challenges in achieving repeatable and timely control due to the complexity of multiple subprocesses and the variability of food products, which leads to inefficiencies and quality issues, despite efforts in automation and sensor-based feedback control systems.
Innovation Solution
A control system that integrates multiple product sensors, plant sensors, and control devices with a machine learning module, utilizing spectrographic and thermographic data to adjust process parameters across subprocesses, enabling real-time, automatic regulation and optimization of food treatment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple subprocesses are used to treat food material, then manufacturing precision and quality control are improved, but device complexity and process time increase
Solution Approach 1:
The food treatment process is divided into multiple subprocesses (e.g., heating, cooling, drying, ripening), each handled by dedicated treatment machines. This segmentation allows each subprocess to be optimized independently for quality control while maintaining overall process manageability through the central control system that coordinates transitions between subprocesses.
Solution Approach 2:
Sensors are deployed in each subprocess to detect quality parameters (temperature, humidity, acidity, fermentation completion). This feedback is sent to the central control system, which automatically adjusts process parameters of upstream or downstream subprocesses to maintain quality standards, creating a closed-loop control system that improves manufacturing precision.
2Productivity
If automation is implemented in food processes, then productivity and repeatability are improved, but device complexity and initial investment increase
Solution Approach 1:
The control system serves multiple functions: it monitors sensors across all subprocesses, processes data from various sensor types, makes decisions about process adjustments, and controls multiple treatment machines. This multi-functional central control system improves productivity by automating coordination across the entire food treatment process while consolidating control logic in a single system.
Solution Approach 2:
The system uses sensor data from the food material itself (acidity, fermentation state, temperature) to automatically adjust process parameters without human intervention. The process self-regulates by using its own measured parameters to control its own progression through subprocesses, improving productivity and repeatability.
3Manufacturing precision
If sensors are used for feedback control, then manufacturing precision is improved, but the control action becomes limited to single subprocess and does not optimize the entire process
Solution Approach 1:
The control system merges data from sensors across multiple subprocesses into a unified control decision-making process. Instead of isolated single-subprocess control, the system combines information about food material state, process parameters, and quality metrics from all subprocesses to make coordinated adjustments that optimize the entire food treatment process rather than individual steps.
Solution Approach 2:
The control system uses sensor data to make preliminary adjustments in upstream subprocesses before issues manifest in downstream processes. For example, if fermentation is progressing slower than expected, the system can pre-adjust temperature or time parameters in subsequent drying or ripening subprocesses to compensate, optimizing the entire process flow rather than reacting to problems after they occur.
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 timely and automatic regulation of food processes, improving repeatability and quality by dynamically adjusting parameters based on continuous learning from sensor data, reducing the need for extensive fine-tuning and human intervention, and enabling the production of consistent high-quality food products.
Implementation Method 1
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
AI summary
A method and a control system of a food treating process made of a plurality of sub-processes is disclosed. The system comprises a plurality of product sensors including at least a spectrograph sensor and a thermograph sensor, plant sensors, control devices, a main hardware and software architecture apt, and a database structure to provide timely and automatic regulation controls, preferably exploiting synergies between various optimised parallel processes.