Discussion Overview
The discussion revolves around the cost estimation formula used to calculate the cost of equipment or plants based on their capacity. Participants explore how to adapt this formula to derive cost per unit of production, particularly in the context of power generation and other industrial applications.
Discussion Character
- Exploratory
- Technical explanation
- Mathematical reasoning
Main Points Raised
- One participant presents the formula Cost B = Cost A * (CapacityB/Capacity A)^n and seeks to modify it for cost per unit of production, suggesting a potential connection to logarithms.
- Another participant questions the units involved in the formula, asking whether Cost B refers to a daily cost for the plant or a cost per item produced, and seeks clarification on the units of Capacity B.
- A participant clarifies that Cost B represents total capital cost and provides an example of calculating costs for different boiler capacities using the exponent.
- One participant expresses a desire to understand how to express the cost in terms of $/tph instead of total currency, indicating a need for a different formula for cost comparison.
- Another participant elaborates on the context of their inquiry, explaining the challenges of finding known values for cost estimation and the use of indices from industry guides for power plants.
- One participant suggests that while changing the units of capacity to include a time component is possible, it may not yield accurate correlations.
- A participant mentions the complexity of modeling multiple cases in an Excel spreadsheet and hints at the possibility of a simpler method for cost estimation.
Areas of Agreement / Disagreement
The discussion features multiple competing views and remains unresolved regarding the best approach to modify the cost estimation formula for cost per unit of production. Participants express uncertainty about the appropriate units and the implications of their calculations.
Contextual Notes
Participants highlight limitations in accessing "known" values for cost estimation, which are often proprietary, and the potential for errors when converting between different cost formats in large datasets.