A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach

Krzywański, Jarosław; Czakiert, Tomasz; Błaszczuk, Artur; Rajczyk, R.; Muskała, W.; Nowak, Wojciech

doi:10.1016/j.fuproc.2015.04.012

Cited by 60 publications

(30 citation statements)

References 63 publications

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“…It has a well-established ability to dig and learn the complex nonlinearities and interactions out of high dimensional and complex input space data [82][83][84]. Gradient descent with momentum was employed as a training function, and tangent hyperbolic was used as a transfer function between the layers of MLP for the neural network model development [64,85].…”

Section: Development Of Ann Process Modelmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

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Section: Development Of Ann Process Modelmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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“…It is proved that one hidden layer is enough to approximate the nonlinearity present in the data provided enough number of neurons are present in the hidden layer [46]. The optimum number of neurons in the hidden layer is determined by hit and trial methods [47,48]. The feedforward backpropagation network algorithm is used in this work.…”

Section: Development Of Process Modelsmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

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Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

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“…Please do not adjust margins Please do not adjust margins Krzywanski et al 145 developed a generalised ANN model to predict the SO 2 emissions from large-and small-scale circulating fluidised bed (CFB) boilers under air-firing, oxygen-enriched and oxy-fired combustion conditions with the dimension and operating parameters of the CFB boilers as the inputs. The authors 145 also conducted a sensitivity analysis to investigate the effects of changing operating parameters on the SO 2 emissions using the trained ANN models. The results indicated that the ANN model can serve as a fast tool to provide the accurate prediction of SO 2 emissions for the coal combustion in the CFB boilers under the different combustion environments with less complexity and costs 145 .…”

Section: Machine Learning In Oxyfuel and Chemical-looping Combustionmentioning

confidence: 99%

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

Yan

Borhani

Subraveti

et al. 2021

Energy Environ. Sci.

151

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A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach

Cited by 60 publications

References 63 publications

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

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