A Flame Imaging-Based Online Deep Learning Model for Predicting NOₓ Emissions From an Oxy-Biomass Combustion Process

Qin, Li; Lü, Gang; Hossain, Md. Moinul; Morris, A.; Yan, Yong

doi:10.1109/tim.2021.3132998

Cited by 14 publications

(4 citation statements)

References 34 publications

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“…These strategies offer robust support for investigating the sample completion mechanism in the modeling of the MSWI process. To enhance multisource information representation and model interpretability, diverse methods have been introduced, including multifeature information fusion [192], multimodal deep learning [193], visual data depth modeling [194], Bayesian data-driven T-S fuzzy [195], and deep forest regression [66,196]. These serve as the theoretical foundation for exploring intelligent reduction in multisource features and constructing interpretable models in the MSWI process.…”

Section: Operational Indices Modelingmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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Section: Operational Indices Modelingmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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show abstract

“…In what concerns DL models, Zhang et al developed an approach to estimate biomass by integrating LiDAR and Landsat-8 data through a DL framework, including autoencoders [402]. Li et al presented a methodology for predicting NO x emissions, which is based on the combustion process of biomass [403,404]. Kartal et al introduced a circulating fluidized bed gasifier model as a tool to create a huge amount of data sets for the training of a DL model to predict the lower heating value of the biogas [405].…”

Section: Modeling Biomass Powermentioning

confidence: 99%

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

et al. 2023

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To face the impact of climate change in all dimensions of our society in the near future, the European Union (EU) has established an ambitious target. Until 2050, the share of renewable power shall increase up to 75% of all power injected into nowadays’ power grids. While being clean and having become significantly cheaper, renewable energy sources (RES) still present an important disadvantage compared to conventional sources. They show strong fluctuations, which introduce significant uncertainties when predicting the global power outcome and confound the causes and mechanisms underlying the phenomena in the grid, such as blackouts, extreme events, and amplitude death. To properly understand the nature of these fluctuations and model them is one of the key challenges in future energy research worldwide. This review collects some of the most important and recent approaches to model and assess the behavior of power grids driven by renewable energy sources. The goal of this survey is to draw a map to facilitate the different stakeholders and power grid researchers to navigate through some of the most recent advances in this field. We present some of the main research questions underlying power grid functioning and monitoring, as well as the main modeling approaches. These models can be classified as AI- or mathematically inspired models and include dynamical systems, Bayesian inference, stochastic differential equations, machine learning methods, deep learning, reinforcement learning, and reservoir computing. The content is aimed at the broad audience potentially interested in this topic, including academic researchers, engineers, public policy, and decision-makers. Additionally, we also provide an overview of the main repositories and open sources of power grid data and related data sets, including wind speed measurements and other geophysical data.

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“…Limited by the sampling structure and measurement mechanism of CEMS, NOx concentration measurement has the following two problems: firstly, the long time required for gas sampling, pipeline transportation, and gas analysis results in lag in NOx concentration measurement, which reduces the real-time performance of the measurement; Secondly, the CEMS needs to regularly purge the sampling probe, and the measured value of NOx is in an abnormal fluctuation state during the purging, which reduces the accuracy of the measurement. In summary, accurate estimation of NOx emissions from boilers can improve the real-time and accuracy of NOx measurements, which is of great significance for precise ammonia injection and reducing pollutant emissions from power plants [6].…”

Section: Introductionmentioning

confidence: 99%

NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

Jiang

Dong

Sun

et al. 2023

Meas. Sci. Technol.

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The measurement of NOx emissions in the selective catalytic reduction (SCR) system of boiler has problems with poor real-time performance and abnormal measurement during purging. It is necessary to accurately estimate NOx emissions. For this reason, the NOx emissions prediction method of boiler based on mutual information feature reconstruction and optimization of extreme learning machine (ELM) is proposed: Firstly, delay estimation and data space reconstruction of input features are performed based on mutual information; Then the conditional mutual information based on greedy selection strategy is adopted to rank and choose the input features; Finally, the hybrid quantum sparrow search algorithm (QSSA) was proposed by combining Lévy flight strategy and quantum strategy in the sparrow search algorithm (SSA), and QSSA is used to optimize the weights and biases of the ELM. Taking the operation data of the SCR system of a 1000MW thermal power unit as an example for verification. The results show that the proposed method can effectively improve the accuracy and generalization ability of the ELM, and provide a new method for NOx emissions estimation of boiler.

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A Flame Imaging-Based Online Deep Learning Model for Predicting NOₓ Emissions From an Oxy-Biomass Combustion Process

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 14 publications

References 34 publications

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

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