Air Compressor Group Scheduling Using Simulation and Fuzzy Preferences
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Solution Overview
Problem
Current methods for scheduling air compressor groups face challenges in establishing a universal model, efficiently assessing energy consumption, and integrating decision-maker preferences, leading to suboptimal resource utilization and high energy consumption.
Innovation Solution
Constructing an air compressor energy consumption model using expert experience and a least squares algorithm, applying simulation technology and depth first tree search to optimize scheduling, and incorporating fuzzy logic for decision-maker preferences to enhance scheduling decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling methods are used for air compressor groups, then implementation is simple, but resource utilization rate is low and energy consumption is high
Solution Approach 1:
The patent transforms the scheduling problem by changing parameters from traditional single-objective optimization to multi-objective optimization, incorporating energy consumption, output, and cost parameters simultaneously. The depth-first tree search algorithm evaluates multiple scheduling schemes based on these transformed parameters to identify optimal configurations that maximize resource utilization while minimizing energy consumption.
Solution Approach 2:
The patent introduces an intermediary assessment system that converts complex scheduling decisions into equivalent electricity values. This intermediary mechanism enables comprehensive evaluation of different scheduling schemes by translating diverse parameters (energy consumption, output, cost) into a unified metric, facilitating optimal decision-making without requiring direct complex multi-parameter optimization.
2Use of energy by moving object
If multi-target optimal scheduling model is applied, then energy conversion efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing the air compressor energy consumption model and organizing historical operation data before executing the optimization. The depth-first tree search algorithm systematically evaluates all possible scheduling schemes in advance, identifying optimal solutions before actual scheduling decisions are made, thereby reducing real-time computational burden while maintaining high energy conversion efficiency.
3Measurement precision
If expert experience is used to construct energy consumption model, then model accuracy is improved, but subjectivity increases
Solution Approach 1:
The patent implements feedback mechanisms by using actual operation data to continuously refine and validate the energy consumption model. The system compares predicted energy consumption with actual measurements, adjusting model parameters accordingly. This feedback loop reduces subjectivity by grounding expert experience in empirical data, improving model accuracy while minimizing subjective bias.
Data Source
AI summary
The present invention provides a method for an optimal scheduling decision of an air compressor group based on a simulation technology, which belongs to the technical field of information. The present invention uses expert experience to construct an air compressor energy consumption model sample set, and applies a least squares algorithm to learn relevant parameters of an air compressor energy consumption model; uses maximum energy conversion efficiency and minimum economic cost based on an equivalent electricity as target functions, and applies the simulation technology and a depth first tree search algorithm to solve a multi-target optimal scheduling model of the air compressor group; and finally uses a fuzzy logic theory to describe the preferences of decision makers, and introduces the decision maker preference information into interactive decision making, thereby assisting production staff to formulate safe, economical, efficient and environmentally friendly operation schemes to achieve an operation mode of maximum resource utilization of the air compressor group. The method also has wide application value in different industrial fields.