Material Flow Optimization Using Simulation-Aggregated Parameters
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Solution Overview
Problem
Existing optimization methods for complex industrial systems, such as material flow optimization, struggle with nonlinear and discontinuous effects, leading to suboptimal solutions due to the need for reduced models that sacrifice prediction power.
Innovation Solution
An integrated optimizing system combining a high-level optimizer module, a low-level simulation module, and an aggregator module, which iteratively modifies high-level process parameters to achieve an optimum value by leveraging detailed low-level material flow data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reduced models are derived through linearization and averaging to enable MILP optimization, then the optimization can be applied to complex systems, but the prediction power is lost
Solution Approach 1:
The patent introduces simulation codes as an intermediary component between the optimization program and the real-world system. The simulation codes receive inputs from the optimization program and generate outputs that reflect complex nonlinear and discontinuous effects, thereby mediating between the simplified optimization model and the complex reality without requiring direct simplification of the latter
Solution Approach 2:
The system is segmented into distinct functional modules: an optimization program for high-level decision making, simulation codes for detailed system behavior analysis, and an interface for integrating both. This segmentation allows each component to operate at its appropriate level of detail without compromising the other
2Ease of manufacture
If manual trial and error methods are used to improve the system, then the approach is simple to implement, but the optimization is inefficient and time-consuming
Solution Approach 1:
The system establishes a feedback loop where simulation results are fed back to the optimization program to refine and adjust inputs. This automated feedback mechanism replaces manual trial-and-error with systematic iterative optimization, improving efficiency while maintaining ease of use through automated processes
3Measurement precision
If high-fidelity simulation codes are used directly for optimization, then the prediction accuracy is maintained, but the optimization methods cannot be applied due to lack of information and nonlinear effects
Solution Approach 1:
The patent adds a temporal dimension to the optimization process by using simulation codes to predict future system dynamics. This allows the optimization program to work with aggregated, time-independent parameters while the simulation handles the complex time-dependent nonlinear behaviors, effectively adding a dimension of temporal simulation to bridge the gap
Data Source
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AI summary
A method of material flow optimization in an industrial process by using an integrated optimizing system is described. The integrated optimizing system includes: a high-level optimizer module describing the material flow by coarse high-level process parameters (x) and including an optimization program for the high-level process parameters (x), the optimization program being dependent on high-level model parameters (A, b, c) and including an objective function subject to constraints; a low-level simulation module for simulating the material flow, the low-level simulation module including a low-level simulation function adapted for obtaining detailed low-level material flow data (F) based on the high-level process parameters, (x); and an aggregator module including an. aggregator function adapted for calculating the high-level, model parameters (A, b, c) based on the low-level material, flow data (F). The method includes approaching an optimum, value of the objective function by iteratively modifying the high-level process parameters (x), wherein an iteration, includes: carrying out, by the low-level, simulation, module, a low-level simulation thereby obtaining the detailed low-level material flow data (F); aggregating, by the aggregator module, the low-level material flow data (F) thereby calculating, from the low-level material flow data (F), aggregated high-level model parameters (fA, fb, fc); inputting the aggregated high-level model parameters (fA, fb, fc) into the optimization program.