NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis

Wang, Guoyang; Awad, Omar I.; Liu, Shiyu; Shuai, Shijin; Wang, Zhiming

doi:10.1016/j.energy.2020.117286

Cited by 77 publications

(25 citation statements)

References 23 publications

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“…The BP neural network algorithm is likely to trap in local extremum during data training, resulting in data training failure since it adopts the standard gradient descent algorithm. Meanwhile, the BP algorithm has the drawback of data overfitting phenomenon (Wang G et al, 2020). Generally speaking, the prediction ability of the neural network is directly proportional to the sample training ability.…”

Section: Power Prediction Model Of the Bp Networkmentioning

confidence: 99%

Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

Li¹,

Zhou²,

Pei-qi

et al. 2022

Front. Energy Res.

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With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (BP) and improved BP neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neural network algorithms (i.e., BP, GA-BP, and PSO-BP) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-BP and PSO-BP network forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original BP model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.

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Section: Power Prediction Model Of the Bp Networkmentioning

confidence: 99%

Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

Li¹,

Zhou²,

Pei-qi

et al. 2022

Front. Energy Res.

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“…The regression loss, Lr, the distillation loss, Ld, and the output of the i th ODL model, Y o i , in (8) are defined as follows, ( )…”

Section: ) Odl's Objective Functionmentioning

confidence: 99%

“…The model-driven prediction approaches such as Computational Fluid Dynamics predict the NOx formation through simulating the physical and chemical reactions associated with the combustion process of given fuels, which is a very useful tool for a theoretical understanding of the combustion phenomenon, but not suitable for real-time processes due to the complexity and computational resources required [6]. The data-driven models are mainly based on historical boiler operation data to predict the NOx emission through various machine learning techniques, such as ANN (Artificial Neural Network) [7], [8], SVM (Support Vector Machine) [9], [10], ELM (Extreme Learning Machine) [11], and DBN (Deep Brief Network) [12]. Least square support vector regression and deep learning models were also proposed for NOx prediction based on the flame radical images [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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

Qin

Lü

Hossain

et al. 2022

IEEE Trans. Instrum. Meas.

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“…In DNN, data quantity for training and input/output definitions are well known for their impacts for problem-solving, and there are some studies to predict NOx emissions using DNN. However, despite the importance of the hyperparameter definitions, these values for DNN designs have often been overlooked or found manually (intuition or trial & error) in previous researches [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Prediction of engine NOx for virtual sensor using deep neural network and genetic algorithm

Kim

Park

Shin

et al. 2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The Nitrogen Oxides (NOx) from engines aggravate natural environment and human health. Institutional regulations have attempted to protect the human body from them, while car manufacturers have tried to make NOx free vehicles. The formation of NOx emissions is highly dependent on the engine operating conditions and being able to predict NOx emissions would significantly help in enabling their reduction. This study investigates advanced method of predicting vehicle NOx emissions in pursuit of the sensorless engine. Sensors inside the engine are required to measure the operating condition. However, they can be removed or reduced if the sensing object such as the engine NOx emissions can be accurately predicted with a virtual model. This would result in cost reductions and overcome the sensor durability problem. To achieve such a goal, researchers have studied numerical analysis for the relationship between emissions and engine operating conditions. Also, a Deep Neural Network (DNN) is applied recently as a solution. However, the prediction accuracies were often not satisfactory where hyperparameter optimization was either overlooked or conducted manually. Therefore, this study proposes a virtual NOx sensor model based on the hyperparameter optimization. A Genetic Algorithm (GA) was adopted to establish a global optimum with DNN. Epoch size and learning rate are employed as the design variables, and R-squared based user defined function is adopted as the object function of GA. As a result, a more accurate and reliable virtual NOx sensor with the possibility of a sensorless engine could be developed and verified.

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NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis

Cited by 77 publications

References 23 publications

Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

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

Prediction of engine NOx for virtual sensor using deep neural network and genetic algorithm

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