Semi-Automatic Production Line MPC for Device Sleep Scheduling
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Solution Overview
Problem
Existing energy-saving strategies for industrial production lines, particularly semi-automatic production lines, face limitations in achieving optimal energy savings due to their event-driven approaches, which result in unsatisfactory stability and less effective outcomes, especially for mature production systems with fewer random events.
Innovation Solution
A method and system utilizing model predictive control (MPC) based on max-plus algebra models to formulate an energy-saving strategy by optimizing the sleep start and end moments of automated devices, incorporating work-in-process products as discrete elements and controlling the devices' sleep states to maximize energy savings without impacting production capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If event-driven energy-saving strategies are used to control automated devices, then energy consumption is reduced, but production stability and optimization effectiveness deteriorate
Solution Approach 1:
The patent implements a closed-loop feedback mechanism where the production line state is continuously monitored and fed back to the model predictive control algorithm. The algorithm adjusts the sleep wake-up timing of automated devices based on real-time production state feedback, ensuring that energy-saving actions do not compromise production stability. This resolves the contradiction by making energy-saving control adaptive rather than event-triggered.
Solution Approach 2:
The model predictive control algorithm performs preliminary calculation and optimization of device sleep wake-up timing before actual production operations. By pre-planning the energy-saving schedule based on predicted production demands, the system avoids reactive event-driven decisions that may disrupt production stability, thus achieving both energy reduction and stability maintenance.
2Device complexity
If event-driven methods are applied to trigger energy-saving decisions, then data collection burden is reduced, but energy-saving optimization effectiveness deteriorates
Solution Approach 1:
The patent transforms the control approach from event-driven parameter changes to model-based continuous optimization. The model predictive control algorithm optimizes multiple parameters simultaneously (device sleep wake-up timing, production scheduling) based on a comprehensive production model, achieving superior energy-saving effectiveness without significantly increasing data collection complexity since it uses the same production line data.
3Productivity
If automated devices operate continuously to maintain production capacity, then productivity is maintained, but energy consumption increases
Solution Approach 1:
The patent makes the automated device operation dynamic rather than static continuous operation. The model predictive control algorithm dynamically adjusts device sleep and wake-up timing based on predicted production demands and current production state. This dynamic control allows devices to enter low-power sleep modes when production capacity can be maintained by other devices, reducing energy consumption while preserving overall productivity.
Solution Approach 2:
The patent segments the production line into multiple automated devices with differentiated sleep wake-up schedules. Instead of all devices operating continuously or shutting down together, the control algorithm optimizes individual device schedules based on production requirements. This segmentation allows selective idle placement where certain devices can sleep while others maintain production capacity, resolving the contradiction between productivity and energy consumption.
Data Source
AI summary
An energy-saving strategy formulation method for a semi-automatic production line based on model predictive control is performed as follows. max-plus algebra model is constructed, based on which an optimization objective function with the WIP products as discrete elements and sleep start moment and sleep end moment of the automated device as control variables is established. A first unoptimized WIP product is substituted into the optimization objective function, and the optimization objective function is solved to obtain an optimal sequence, in which a first set of values is output as optimization result. Whether all WIP products have been optimized is determined, and if yes, an energy-saving strategy is generated and used to control the automated device, otherwise, the optimization result is treated as disturbance information to be incorporated into the modeling. An energy-saving strategy formulation system is also provided.