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Results

  • 1. The optimized configuration resulted in a reduction of US$6m in capital costs.
  • 2. The model gave us confidence that the power plant would meet its performance targets.
  • 3. After the refinery was commissioned, the actual load proved to be just 55 MW, not 70 MW as projected originally. One way to manage the excess capacity was to sell power to other local consumers. The refinery did this very well, laying feeders directly to purchasers. They managed to sell about 20 MW, creating a new revenue stream.

Lessons

Mathematical modeling of new projects improves their Return on Investment.

It helps eliminate surplus equipment and associated maintenance costs over the plant life.

It allows what-if scenario development at relatively low cost.

We should have done the modeling work much earlier, at the conceptual stage of the project. This would have identified the advantage of having two gas turbine and three steam turbine generators at a stage where commitments for the GTs had not already been made. A further capital cost saving of up to US $10 million was thus not realized.

It turned out that the power requirement was grossly overestimated at the conceptual stage of the project. With a better estimate, we could have eliminated one HRSG-GT combination without having to order a third steam turbine generator, with further capital and operating cost reductions.

Modeling can be used in the operating phase as well, especially when operating contexts change during the life of the plant. Sensitivity analysis (what- if scenarios) offers a powerful way to estimate the outcome of changes in maintenance policy, resource levels, shutdown intervals or durations, and equipment replacement decisions.

 
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