Multi-objective optimizations were conducted for a compressor station comprising two dissimilar compressor units driven by two dissimilar gas turbines, two coolers of different size, and two parallel pipeline sections to the next station. Genetic algorithms were used in this optimization along with models describing the performance characteristics of gas turbines, compressors, aerial coolers, and downstream pipeline section. Essential in these models is the heat transfer between the gas and soil as it affects the pressure drop along the pipeline, and hence relates back to the coolers and compressor flow/pressure settings. Further investigative techniques were developed to also minimize NOx and CO2e emissions along with total energy consumption, i.e. fuel (used in the driver gas turbines) and electrical energy (used in the electrical fans of the aerial coolers). Two optimization scenarios were conducted: 1) Two-objective optimization of total energy consumption and NOx emission, and 2) Two-objective optimization of total energy consumption and CO2e emission. The results showed that savings in the energy consumption in the order of 5–6% is achievable with slight adjustment to unit load sharing and coolers by-pass/fan speed selections. It appears that most of the savings (around 70–75%) are derived from optimizing the load sharing between the two parallel compressors, while the balance of the savings is realized from optimizing the aerial coolers settings. In order to optimize operation for minimum NOx emission as well, a shift towards employing more of the aerial coolers is required. Preliminary cost analysis was conducted for valuation of balancing between energy consumption vs. emission loading in terms of both NOx and CO2e.

This content is only available via PDF.
You do not currently have access to this content.