Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Zhou, Hao; Zhao, Jia Pei; Zheng, Li; Wang, Chun Lin; Cen, Ke Fa

doi:10.1016/j.engappai.2011.08.005

Cited by 125 publications

(34 citation statements)

References 42 publications

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“…These models improved the performance of traditional models and increased the prediction accuracy. Zhou et al [26] developed a hybrid model by combining SVR and ant colony optimisation (ACO) to estimate NOx emissions in urban areas. In this study, the optimal model's parameters were selected based on the ACO model.…”

Section: Previous Workmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

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Section: Previous Workmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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“…Algorithms of that kind find a number of applications, for example, in solving the image segmentation problem [16], in multiobjective design optimization of composite structures [17], in solving the asymmetric traveling salesman problem [18], in modeling NO x emissions [19], in citywide planning of open WiFi access networks [20], in training neural networks [21], in solving the flow shop scheduling problem with limited buffers [22], and many others.…”

Section: Introductionmentioning

confidence: 99%

Experimental Verification of Selected Artificial Intelligence Algorithms Used for Solving the Inverse Stefan Problem

Hetmaniok

Słota

Zielonka

2014

Numerical Heat Transfer, Part B: Fundamentals

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The article presents a comparative study of three procedures applied for solving the inverse Stefan problem. The investigated problem consists of reconstruction of the unknown boundary condition on the basis of measurement data, and the procedures of solution differ in the way of minimizing the proper functional-in each approach considered, one of three artificial intelligence algorithms (Ant Colony Optimization, Artificial Bee Colony, and Harmony Search) is used. Methods applying the respective algorithms are compared with regard to their velocity and the precision of results.

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“…Si et al reported modeling NO X emission with the popular SVR [3,4], in which better agreement between the predicted and measured NO X emission concentrations was achieved due to the outstanding nonlinear mapping capacities of SVR. For the procedures of NO X emissions optimization, some heuristic algorithms such as genetic algorithms [2] (GA), particle swarm optimization [5] (PSO), ant colony optimization [6] (ACO) and their variants [4,7] were used to reduce NO X emissions and have achieved success.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods

Tan

Cheng

et al. 2014

Energy Procedia

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A new methodology combining the advanced extreme learning machine (ELM) and harmony search (HS) was proposed to model and optimize the operational parameters of the boiler for the control of NO X emissions in a 700 MW pulverized coal-fired power plant. About five days' worth of real data were obtained from supervisory information system (SIS) of the power plant to build the ELM NO X model. HS was employed to optimize the operational parameters of the boiler to minimize NO X emissions based on the prediction of NO X by ELM. Compared with the widely used learning method such as ANN and SVR, ELM performed better both in accuracy and computing time for the modeling of NO X emission. The proposed comprehensive methodology can provide desired and feasible optimal solutions within one second, which is acceptable for the online optimization.

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Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Cited by 125 publications

References 42 publications

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

Experimental Verification of Selected Artificial Intelligence Algorithms Used for Solving the Inverse Stefan Problem

Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods

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