Prediction of Steam Turbine Performance as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network

Herawan, Safarudin Gazali; Talib, Kamarulhelmy; Shamsudin, Shamsul Anuar; Putra, Azma; Musthafah, M. T.; Ismail, Ahmad Faris

doi:10.1109/iscmi.2016.22

Cited by 3 publications

(1 citation statement)

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“…The AI-based studies published in the literature generally report the modeling performance of the algorithms and, in some cases, the optimization results for the lab-scale, pilot-scale, and model-simulated studies. ,− The data sets for such studies follow the typical experimental designs, and the performance enhancement of the investigated system is guaranteed with the considered design space. However, the industrial data-driven AI model development, finding the improvement in the already-designed control space with respect to the operational constraints and subsequent contribution to the net-zero goal, is a challenging task that has not been reported and is of particular importance as well as a research gap to demonstrate the potential of AI for the performance enhancement of industrial systems to the industrial community.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

et al. 2023

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Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector.

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