Adaptive Chocolate Mass Control via Laser and NIR Sensors
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Solution Overview
Problem
Industrial chocolate production systems face challenges in maintaining process stability and quality due to varying operating conditions and interdependencies between production steps, leading to inefficiencies and inconsistencies in end product characteristics.
Innovation Solution
A self-optimizing, adaptive control system that integrates real-time monitoring and adjustment across dosing, mixing, refining, conching, and tempering processes, using advanced measurement technologies like laser triangulation and near-infrared sensors to dynamically control roller pressure, gap settings, and ingredient dosing, ensuring consistent plasticity, particle size, and fat content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fixed-parameter control systems are used, then the system is simple to design and operate, but the control performance deteriorates when operating conditions or process dynamics change
Solution Approach 1:
The control system transitions from fixed parameters to dynamic adaptive parameters that automatically adjust in real-time based on process conditions. The system continuously identifies process dynamics and retunes controller parameters without manual intervention, making the control system adaptive to changing operating conditions while maintaining reliability.
Solution Approach 2:
The control system performs self-diagnosis and self-adjustment by automatically identifying process dynamics and retuning its own parameters. This self-service capability eliminates the need for external intervention when process conditions change, maintaining optimal control performance autonomously.
2Productivity
If manual monitoring and adjustment of production parameters is performed, then the system complexity is low, but productivity is reduced and labor requirements increase
Solution Approach 1:
The system continuously measures actual production parameters (particle size, plasticity, fat content) and feeds this information back to automatically adjust process settings. This closed-loop feedback mechanism enables real-time optimization of production parameters, increasing productivity while reducing manual labor through automated decision-making.
Solution Approach 2:
Manual monitoring and adjustment operations are replaced by an automated control system that uses sensors, data processing, and automatic actuation. The mechanical/manual system is substituted with an electronic control system that continuously optimizes production parameters without human intervention.
3Manufacturing precision
If real-time adaptive control is implemented, then control performance and product consistency are improved, but the device complexity and initial investment increase
Solution Approach 1:
The system performs preliminary identification of process dynamics and pre-adjusts controller parameters before production issues arise. By continuously monitoring process conditions and proactively adapting parameters, the system prevents deviations in product quality rather than reacting to them, ensuring consistent manufacturing precision.
Solution Approach 2:
The control system dynamically changes operational parameters (roller pressure, gap settings, dosing rates) based on real-time process conditions and identified dynamics. This continuous parameter adaptation ensures optimal control performance and consistent product quality across varying production conditions.
4Productivity
If separate processing steps are performed in different devices, then each step can be optimized independently, but the interdependencies between steps make overall optimization difficult
Solution Approach 1:
The control system serves multiple processing steps (milling, conching, refining) simultaneously through a unified adaptive control architecture. This multi-functional control system manages interdependencies between steps by coordinating parameter adjustments across the entire production line, achieving overall optimization rather than isolated step optimization.
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 enhances productivity, reduces labor requirements, and achieves consistent high-quality chocolate production by automatically adjusting operational parameters based on real-time data, minimizing waste and optimizing resource usage.
Implementation Method 1
assessing a topography of the chocolate mass in an inlet zone of rolls of the pre-refiner by line triangulation
Implementation Method 2
an optical, inline, non-invasive, real-time measuring system detecting the particle size of the finer chocolate mass
Implementation Method 3
shearing elements extend from said shaft towards said inner surface pressing the conching chocolate mass against the vessel surface
Data Source
AI summary
Proposed is a self-optimizing, adaptive industrial chocolate production system (1), and method thereof. The system comprising a chocolate mass processing line (11) with at least dosing means (2), one or more mixers (3), one or more refiners (4), one or more conches (5), and liquefying and tempering means (6). Appropriate inter-dependent operational parameters of the various devices (2/3/4/5/6) are measured by real-time measuring devices and transmitted to a controller device 12. The measured inter-dependent operational parameters are mutually optimized and dynamically adjusted providing an optimal operation at least in terms of the characteristics of the end chocolate mass 7 and/or throughputs of the chocolate production line 11 and/or other operation conditions as energy consumption.