Performance Indicator Calculation for Food Processing Lines
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Solution Overview
Problem
Food processing plants face challenges in accurately and flexibly indicating the performance of their systems, leading to inefficiencies and increased costs due to product loss and water usage, necessitating a monitoring system for continuous performance tracking.
Innovation Solution
A method involving a computer program that uses a control device to calculate performance indicators, detect inefficiencies, and adjust processing systems by analyzing data from measurement devices and time models, with a settings change database for sharing experiences across systems to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a monitoring system is implemented to continuously track performance, then performance monitoring accuracy is improved, but device complexity increases
Solution Approach 1:
A control device acts as an intermediary between measurement devices and the processing system. The control device receives data from multiple measurement devices, processes it centrally, and generates performance indicators. This intermediary approach consolidates the complexity into a single device rather than distributing it across the entire system, thereby improving monitoring accuracy while managing device complexity.
Solution Approach 2:
The system implements continuous feedback loops where performance indicators are calculated from measurement data and used to detect inefficiencies. The feedback mechanism enables automatic detection of underperforming units and triggers adjustments to processing parameters, improving monitoring precision through systematic data collection and analysis without proportionally increasing overall system complexity.
2Productivity
If performance monitoring is increased to reduce product loss, then productivity is improved, but use of energy increases
Solution Approach 1:
The control device continuously receives data from measurement devices and calculates performance indicators in real-time. When inefficiencies are detected through feedback comparison against expected performance levels, the system automatically adjusts processing parameters to optimize productivity and reduce product loss. This feedback-driven approach ensures energy is used efficiently by only adjusting parameters when performance deviations are detected, rather than continuous high-energy monitoring and adjustment.
Solution Approach 2:
The processing system performs self-diagnosis and self-adjustment through automated inefficiency detection. When the control device identifies underperforming units based on performance indicators, it automatically adjusts processing parameters without requiring external intervention. This self-service capability reduces the need for continuous high-energy monitoring and manual adjustments, optimizing the balance between productivity improvement and energy consumption.
3Reliability
If continuous monitoring is implemented to ensure food safety, then reliability is improved, but loss of time increases
Solution Approach 1:
The system implements continuous monitoring of performance indicators without interrupting the food processing flow. Measurement devices continuously collect data from the processing system, and the control device continuously calculates performance indicators in the background. This continuous action ensures food safety is monitored at all times while maintaining uninterrupted processing, preventing time loss that would occur with periodic sampling or stop-and-check approaches.
Solution Approach 2:
The control device serves as an intermediary that processes monitoring data without interfering with the primary food processing operations. By centralizing the analysis of performance indicators and inefficiency detection in the control device, the system maintains continuous food safety monitoring while allowing the processing system to operate without interruption. The intermediary processes information in parallel, ensuring reliability without adding sequential time delays to the processing workflow.
Data Source
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AI summary
It is presented a method for determining a performance indicator for a processing system. The processing system is divided in a number of processing lines, referred to as a first sub-level, in turn divided in a plurality of machine units, referred to as a second sub-level. The method comprises receiving a request for a performance indicator, determining a performance indicator level by determining if said performance indicator is associated with said processing system, one of said processing lines in said first sub-level or one of said machine units in said second sub-level, determining a time period related to said performance indicator level, receiving a time period value related to said time period, determining a measurement device output related to said performance indicator, receiving at least one measurement device output value related to said measurement device output, and determining said performance indicator based on said measurement device output value and said time period value.