Speed up gas turbine selection, and make design iterations faster when optimising a system with a gas turbine and a production process, to allow faster decision making and a deeper level of optimisation without the conservatism and assumptions normally used.
When sizing a gas turbine and associated heat recovery systems there are typically many parameters which need to be considered, including numerous process operating cases over a life of field, seasonal and diurnal ambient temperature changes, and operation at part load performance. These factors can affect gas turbine behaviour significantly, and require a large number of iterations between equipment vendors and designers to specify the correct machine and size related equipment.
Where gas turbines are more deeply integrated into the process design (for example direct coupled to rotating equipment, or part of a complex cogen system) the interface between the process/utility model and the gas turbine is a potential source of error, can slow down design work and is often over simplified resulting in oversized equipment.
GT-SIM solves these problems by providing a native gas turbine model within the Petro-SIM environment, where it can be built into a process simulation or ProSteam model directly. GT-SIM is a simple to use predictive model that can be set up using data commonly available in the Gas Turbine World Handbook. GT-SIM predicts the full range of turbine operation from this data (covering ambient air temperature and humidity, part load operation, and the impact of fuel conditions, HRSG etc). It has a variety of control options and includes prediction of performance with NOx or augmentation steam addition.
This results in a unified modelling tool where the performance of the entire system can be evaluated in a single environment.
Real time monitoring and optimisation of utility systems offers scope to reduce energy costs by up to 5% without incurring capital investment.
Reduce energy costs by 10-30% and minimise the capital spend when designing or revamping heat exchanger systems, by using SuperTarget to perform Pinch-based analytics.