Equipment Operation Selection for Low Variable Expense
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Solution Overview
Problem
Existing systems face challenges in selecting operational conditions for equipment that minimize variable expenses, particularly in equipment like rectifying towers used for purifying raw materials.
Innovation Solution
A selection system that includes an input part, a first arithmetic part for simulation, a second arithmetic part for calculating variable expenses, and a selecting part to compare and select operational conditions with low variable expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual selection of operational conditions is performed by a designer, then flexibility and adaptability are maintained, but variable expenses cannot be minimized due to lack of systematic optimization
Solution Approach 1:
The patent replaces manual designer selection with an automated computer-based system that uses simulation and calculation to determine optimal operational conditions. The selecting part automatically compares variable expenses across multiple candidates and selects the condition with the lowest expense, substituting human judgment with systematic computational optimization.
Solution Approach 2:
The system changes operational parameters by simulating multiple candidate conditions and selecting the optimal one based on calculated variable expenses. The first arithmetic part generates different operational condition candidates, and the second arithmetic part calculates expenses for each, enabling parameter optimization through systematic variation and comparison.
2Loss of energy
If automated optimization system is implemented to minimize variable expenses, then cost efficiency is improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional parts: an input part for receiving operational data, a first arithmetic part for simulation and candidate generation, a second arithmetic part for expense calculation, and a selecting part for optimization. This segmentation allows each component to perform a specific function, making the complex optimization task manageable through modular architecture.
Solution Approach 2:
The patent introduces intermediate computational components that mediate between input data and final selection. The first arithmetic part acts as an intermediary that transforms input operational conditions into candidate solutions through simulation, and the second arithmetic part serves as another intermediary that calculates expenses for each candidate, enabling systematic optimization through intermediate processing steps.
3Measurement precision
If multiple candidate operational conditions are simulated and compared, then optimal condition selection is improved, but calculation time and processing requirements increase
Solution Approach 1:
The system performs preliminary simulation and candidate generation before final selection. The first arithmetic part pre-calculates multiple candidate operational conditions and their associated variable expenses using the second arithmetic part, so that when the selecting part needs to make a decision, the comparative data is already prepared, reducing real-time calculation requirements.
Solution Approach 2:
The system maintains continuous optimization by repeatedly executing the simulation and calculation cycle. The selecting part causes the first and second arithmetic parts to repeat calculations, continuously generating and evaluating candidate conditions to ensure the optimal solution is found, while the automated nature of this repetition improves efficiency over manual iteration.
Data Source
AI summary
A selection system selects an operational condition of equipment in which a cost including a variable expense of the equipment is low. The selection system includes an input part, a first arithmetic part, a second arithmetic part, and a selecting part. The input part is usable to input a restriction relating to an operation of the equipment, a spec of a product to be produced with the equipment, and a production quantity of the product. The first arithmetic part automatically executes a simulation of the operation of the equipment, and calculates a candidate of the operational condition satisfying the restriction, the spec, and the production quantity. The second arithmetic part calculates the variable expense when operating in accordance with the candidate. The selecting part causes the first and second arithmetic parts to repeat the calculations, and automatically compares variable expenses to select a first operational condition.