Strategic-level performance enhancement of a 660 MWe supercritical power plant and emissions reduction by AI approach

Ashraf, Waqar Muhammad; Uddin, Ghulam Moeen; Arafat, Syed Muhammad; Krzywański, Jarosław; Wang, Xiaonan

doi:10.1016/j.enconman.2021.114913

Cited by 36 publications

(24 citation statements)

References 57 publications

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“…Furthermore, the fuel savings also contribute toward the improved overall efficiency of the power complex. 19 The fuel savings are converted into the annual reduction in CO 2 , SO 2 , CH 4 , N 2 O, and Hg emission discharges from the power plant, 19 as shown in Figure 11. The reduction in emission concentration is calculated for the three power generation modes of the power plant, i.e., half-load, mid-load, and full load.…”

Section: Emission Reduction Equivalent Of Increment In Hp Turbinementioning

confidence: 99%

“…In the last two decades, AI-based data modeling tools have presented a remarkable performance in developing engineering solutions and optimization strategies for large-scale industrial systems overcoming the limitations of mathematical modeling tools. − Our research group has also reported performance enhancement solutions developed on the component level, system level, and strategic level of a 660 MW coal power plant using advanced AI modeling tools and statistical techniques. ,, AI-based modeling and simulation algorithms can provide accurate results mined out of the high-dimensional and nonlinear interacting features of engineering systems, which can be reliably implemented in the running operation of energy systems . However, asymmetric and high-dimensional space of the data, development of efficient AI models and their validation, domain knowledge-backed experimental designs, and operating strategies are the challenges to be addressed carefully to exploit the true potential of data and AI algorithms.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Emission Reduction Equivalent Of Increment In Hp Turbinementioning

confidence: 99%

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

Self Cite

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“…Implementations of procedures such as LSTM allow network training to take place without having long-term parameters "explode" or "vanish" as a result of multiple learning updates [77,78]. ML models based on SVM, deep learning, LSTM, and more have been used in various facets of energy engineering predictions, such as power plant heat transfer rate, power plant emission reduction, fluidized adsorption bed processes, and generator power curves [79][80][81]. However, despite the importance of quantifying emissions from open-pits, the authors of this article have not been informed of any studies attempting to estimate methane emission fluxes from open-pit mining facilities during different diurnal times and seasons using ML models.…”

Section: Modeling Techniques To Quantify Emission Fluxesmentioning

confidence: 99%

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

et al. 2022

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Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R2 (-) values higher than 0.8 (-). Considering the bias (Tonnes h−1), the ML predicted the emission fluxes within 6.3%, 3.3%, and 0.3% of WRF predictions for the old mine, new mine, and the pond, respectively.

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“…The energy imbalance instigated by the excessive use of nonrenewable fuels in the automotive industry in specific and the industrial sector in general is an alarming issue. − Moreover, the shambolic state of global warming and pollution associated with exhaust emissions and engine lubricating oil disposal is equally unignorable. , Among all fuels, hydrocarbon fuel is majorly responsible for environmental pollution . 18% of global primary energy is utilized by the transport sector and is primarily accountable for 23% of global CO 2 emissions, eventually leading to consequences of global warming .…”

Section: Introductionmentioning

confidence: 99%

Acetone–Gasoline Blend as an Alternative Fuel in SI Engines: A Novel Comparison of Performance, Emission, and Lube Oil Degradation

et al. 2023

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The disproportionate use of petroleum products and stringent exhaust emissions has emphasized the need for alternative green fuels. Although several studies have been conducted to ascertain the performance of acetone–gasoline blends in spark-ignition (SI) engines, limited work has been done to determine the influence of fuel on lubricant oil deterioration. The current study fills the gap through lubricant oil testing by running the engine for 120 h on pure gasoline (G) and gasoline with 10% by volume acetone (A10). Compared to gasoline, A10 produced better results in 11.74 and 12.05% higher brake power (BP) and brake thermal efficiency (BTE), respectively, at a 6.72% lower brake-specific fuel consumption (BSFC). The blended fuel A10 produced 56.54, 33.67, and 50% lower CO, CO2, and HC emissions. However, gasoline remained competitive due to lower oil deterioration than A10. The flash-point and kinematic viscosity, compared to fresh oil, decreased by 19.63 and 27.43% for G and 15.73 and 20.57% for A10, respectively. Similarly, G and A10 showed a decrease in total base number (TBN) by 17.98 and 31.46%, respectively. However, A10 is more detrimental to lubricating oil due to a 12, 5, 15, and 30% increase in metallic particles like aluminum, chromium, copper, and iron, respectively, compared to fresh oil. Performance additives like calcium and phosphorous in lubricant oil for A10 decreased by 10.04 and 4.04% in comparison to gasoline, respectively. The concentration of zinc was found to be 18.78% higher in A10 when compared with gasoline. A higher proportion of water molecules and metal particles were found in lubricant oil for A10.

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Strategic-level performance enhancement of a 660 MWe supercritical power plant and emissions reduction by AI approach

Cited by 36 publications

References 57 publications

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

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

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

Acetone–Gasoline Blend as an Alternative Fuel in SI Engines: A Novel Comparison of Performance, Emission, and Lube Oil Degradation

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