Deep reinforcement learning optimization framework for a power generation plant considering performance and environmental issues

Adams, Derrick; Oh, Dong Hoon; Kim, Dong Won; Lee, Chang Ha; Oh, Min

doi:10.1016/j.jclepro.2021.125915

Cited by 22 publications

(7 citation statements)

References 44 publications

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“…In addition, it is the largest in boiler heat loss and is closely related to temperature of exhaust gas (T e ). q 3 is the loss of the inadequacy burning, %, which is observed on-site by measuring the CO concentration in the exhaust gas while none of the other losses can be measured in real-time (Adams et al, 2021), Therefore, the boiler efficiency is mainly represented by the exhaust gas temperature and the CO concentration in the exhaust gas in this study.…”

Section: Design Of the Combustion Optimization System Based On Dw-dyn...mentioning

confidence: 96%

Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

Wei-Jie

Wang²,

Ping-Ji³

et al. 2023

Front. Energy Res.

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The combustion process of boilers under deep peak shaving is a multivariate process which has complex characteristics such as super multivariability, being nonlinear, and large delay. It is difficult to handle complex data and calculate appropriate distributed results. To this end, this study applies the A3C method based on the dynamic weight Dyna structure to the boiler combustion system. This method trains and optimizes the boiler combustion system by establishing a data center and designing appropriate states and reward values, and the simulation results show that this method can be used to optimize the boiler combustion system. It can effectively reduce NOX emissions and improve the boiler combustion efficiency.

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Section: Design Of the Combustion Optimization System Based On Dw-dyn...mentioning

confidence: 96%

Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

Wei-Jie

Wang²,

Ping-Ji³

et al. 2023

Front. Energy Res.

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“…, 2020b), prediction algorithms (Adams et al. , 2020), and source estimations (Adams et al. , 2021), by utilizing the attention mechanism.…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

“…It is an attempt within deep neural networks to mimic human cognition by selectively concentrating on a few important elements while disregarding others (Jiang et al, 2020;Zhang et al, 2020b;. DL has the potential to enhance air quality prediction in a variety of ways, including estimates using remote sensing data (Zhang et al, 2020b), prediction algorithms (Adams et al, 2020), and source estimations (Adams et al, 2021), by utilizing the attention mechanism. DL-based forecasting of the air quality index is becoming a valuable tool when attention mechanisms are used to provide early warning of environmental pollution events such as forest fires and industrial smog (Zhang et al, 2020b;Huberman et al, 2021).…”

Section: Deep Learning Applications Reviewmentioning

confidence: 99%

Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

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PurposeDeep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.Design/methodology/approachUsing bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.FindingsThis research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.Research limitations/implicationsThis research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.Originality/valueThis research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

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“…Driven by the development of intelligent algorithms, data-driven modeling technology for complex industrial processes is attracting the attention of researchers [11,12]. The datadriven models are applied and migrated without considering the design parameters of units.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

2022

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Coal slime blending can effectively improve the utilization rate of fossil fuels and reduce environmental pollution. However, the combustion in the furnace is unstable due to the empty pump phenomenon during the coal slurry transport. The combustion instability affects the material distribution in the furnace and harms the unit operation. The bed pressure in the circulating fluidized bed unit reflects the amount of material in the furnace. An accurate bed pressure prediction model can reflect the future material quantity in the furnace, which helps adjust the operation of the unit in a timely fashion. Thus, a deep learning-based prediction method for bed pressure is proposed in this paper. The Pearson correlation coefficient with time correction was used to screen the input variables. The Gaussian convolution kernels were used to implement the extraction of inertial delay characteristics of the data. Based on the computational theory of the temporal attention layer, the model was trained using the segmented approach. Ablation experiments verified the innovations of the proposed method. Compared with other models, the mean absolute error of the proposed model reached 0.0443 kPa, 0.0931 kPa, and 0.0345 kPa for the three data sets, respectively, which are better than those of the other models.

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Deep reinforcement learning optimization framework for a power generation plant considering performance and environmental issues

Cited by 22 publications

References 44 publications

Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

Deep learning applications in manufacturing operations: a review of trends and ways forward

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

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