The current gas turbine performance monitoring infrastructure in Shell Malaysia yields inaccuracies of ±15% with no links towards emissions and fuel economics. This has resulted in severe limitations towards the ability to improve greenhouse gas (GHG) performance and generate value. This paper describes a novel, data centric approach to derive meaningful insights and economics/carbon savings from existing data on Plant Information (PI) and SMART CONNECT, a Shell in house performance management IT tool.
This project applies advanced analytics techniques based on historical data, supplemented by engineering performance models to derive robust outcomes. First, gas turbine and compressor modelling principles are programmed in Python and validated with engineering software such as UNISIM based on available operating data via PI. This yields a multivariate dataset tabulating the historical efficiency, power and fuel gas consumption of the fleet. The model is then utilized in a mathematical optimization algorithm and the optimized data used for training and validation of a Random Forest Regressor model.
The performance model in Python is able to achieve accuracies of <1% absolute error when validated with UMSFM on the key performance parameters. Through parametric optimization, the Mean Squared Error (MSE) of the gas turbine and compressor powers is reduced to 0.55MW2 from its original 4.94MW2. The Heat Rate, Shaft Power, and gas generator exit pressures are also identified as the variables most correlated with efficiency. Lastly, the trained machine learning model demonstrated agreement with the dataset during testing, with a R2 value of 0.86 reflecting a strong correlation.
With a predictive digital model in place, production programmers can accurately identify the key levers to optimize the machine operating point for optimum fuel gas consumption. Optimizing Gumusut Kakap's high pressure compressors can yield 62,400 USD in savings per annum from increased sales gas and and 880 tCO2e per annum of reduction in GHG emissions, for every 1% increase in efficiency. This approach is a novel concept, leveraging on expertise from both engineering and data science to enhance equipment performance, and can be replicated towards other types of equipment to achieve efficiency, economic and emissions improvements at scale.