Evolutionary optimisation for power generation unit loading application

Power generation unit loading optimisation is a practically viable tool for efficiency improvement. The objectives for the coal-fired power generation loading optimisation are to minimize fuel consumption and to minimize emissions for a given load demand. This paper presents two models for this significant industrial application. Depending on the environmental regulation, either a single objective constrained model or a multi-objective constrained model can be chosen in practice. A multi-objective constraint-handling method incorporating the constraint dominance concept via Particle Swarm Optimisation (PSO) algorithm has been adopted for problem solving. The simulation results based on a coal-fired power plant demonstrates the capability, effectiveness and efficiency of using the proposed approach in a large scale industrial application

Performance based unit loading optimization using particle swarm optimization approach

This paper presents a Particle Swarm Optimization (PSO) based approach for economically dispatching generation load among different generators based on the unit performance. A modified PSO algorithm with preserving feasibility and repairing infeasibility strategies is adopted for handling constraints. A four-unit loading optimization for an Australian power plant is successfully implemented by using the modified PSO algorithm. The result reveals the capability, effectiveness and efficiency of using evolutionary algorithms such as PSO in solving significant industrial problems in the power industry

Community-Acquired Pneumonia Recognition by Wavelet Entropy and Cat Swarm Optimization

Community-acquired pneumonia (CAP) is a type of pneumonia acquired outside the hospital. To recognize CAP more efficiently and more precisely, we propose a novel method—wavelet entropy (WE) is used as the feature extractor, and cat swarm optimization (shortened as CSO) is used to train an artificial neural network (ANN). Our method is abbreviated as WE-ANN-CSO. This proposed WE-ANN-CSO algorithm yields a sensitivity of 91.64 ± 0.99%, a specificity of 90.64 ± 2.11%, a precision of 90.96 ± 1.81%, an accuracy of 91.14 ± 1.12%, an F1 score of 91.29 ± 1.04%, an MCC of 82.31 ± 2.22%, an FMI of 91.29 ± 1.03%, and an AUC of 0.9527. This proposed WE-ANN-CSO algorithm provides better performances than four state-of-the-art approaches.</p

Constrainted power plants unit loading optimization using particle swarm optimization algorithm

Power plants unit loading optimization problem is of practical importance in the power industry. It generally involves minimizing the total operating cost subject to satisfy a series of constraints. Minimizing fuel consumption while achieve output demand and maintain emissions within the environmental license limits is a major objective for the loading optimization. This paper presents a Particle Swarm Optimization (PSO) based approach for economically dispatching generation load among different generators based on the units’ performance. Constraints have been handled by a proposed modified PSO algorithm which adopting preserving feasibility and repairing infeasibility strategies. A simulation of an Australia power plant implementing the modified algorithm is reported. The result reveals the capability, effectiveness and efficiency of using evolutionary algorithms such as PSO in solving significant industrial problems in the power industry

Quality Assessment of SAR-to-Optical Image Translation

Synthetic aperture radar (SAR) images contain severe speckle noise and weak texture, which are unsuitable for visual interpretation. Many studies have been undertaken so far toward exploring the use of SAR-to-optical image translation to obtain near optical representations. However, how to evaluate the translation quality is a challenge. In this paper, we combine image quality assessment (IQA) with SAR-to-optical image translation to pursue a suitable evaluation approach. Firstly, several machine-learning baselines for SAR-to-optical image translation are established and evaluated. Then, extensive comparisons of perceptual IQA models are performed in terms of their use as objective functions for the optimization of image restoration. In order to study feature extraction of the images translated from SAR to optical modes, an application in scene classification is presented. Finally, the attributes of the translated image representations are evaluated using visual inspection and the proposed IQA methods

Numerical simulation of turbulent flow inside the Electrostatic Precipitator of a power plant

The performance of Electrostatic Precipitator (ESP) is significantly affected by complex flow distribution. In this study the flue gas flow through the ESP at a local power station is modelled numerically using computational fluid dynamics (CFD) code Fluent to give insight to the flow behavior inside the ESP. The flow simulation was performed using the Reynolds Stress Model (RSM). The prediction of the flow behaviour is compared and discussed with on-site data supplied by the power plant

Data Caching Optimization With Fairness in Mobile Edge Computing

Data Caching Optimization With Fairness in Mobile Edge Computin

Automatic estimation of soil biochar quantity via hyperspectral imaging

Biochar soil amendment is globally recognized as an emerging approach to mitigate CO2 emissions and increase crop yield. Because the durability and changes of biochar may affect its long term functions, it is important to quantify biochar in soil after application. In this chapter, an automatic soil biochar estimation method is proposed by analysis of hyperspectral images captured by cameras that cover both visible and infrared light wavelengths. The soil image is considered as a mixture of soil and biochar signals, and then hyperspectral unmixing methods are applied to estimate the biochar proportion at each pixel. The final percentage of biochar can be calculated by taking the mean of the proportion of hyperspectral pixels. Three different models of unmixing are described in this chapter. Their experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil. © 2019 by IGI Global

The potential of hyperspectral images and partial least square regression for predicting total carbon, total nitrogen and their isotope composition in forest litterfall samples

Purpose: The main objective of this study was to examine the potential of using hyperspectral image analysis for prediction of total carbon (TC), total nitrogen (TN) and their isotope composition (δ13C and δ15N) in forest leaf litterfall samples. Materials and methods: Hyperspectral images were captured from ground litterfall samples of a natural forest in the spectral range of 400–1700 nm. A partial least-square regression model (PLSR) was used to correlate the relative reflectance spectra with TC, TN, δ13C and δ15N in the litterfall samples. The most important wavelengths were selected using β coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set. Results and discussion: The results showed that the data of TC and δ13C could not be fitted to the PLSR model, possibly due to small variations observed in the TC and δ13C data. The model, however, was fitted well to TN and δ15N. The cross-validation R2cv of the models for TN and δ15N were 0.74 and 0.67 with the RMSEcv of 0.53% and 1.07‰, respectively. The external validation R2ex of the prediction was 0.64 and 0.67, and the RMSEex was 0.53% and 1.19 ‰, for TN and δ15N, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and δ15N, showing that the models were reliable for the prediction of TN and δ15N in new forest leaf litterfall samples. Conclusions: The PLSR model was not successful in predicting TC and δ13C in forest leaf litterfall samples using hyperspectral data. The predictions of TN and δ15N values in the external litterfall samples were reliable, and PLSR can be used for future prediction. © 2017, Springer-Verlag GmbH Germany

Using laboratory-based hyperspectral imaging method to determine carbon functional group distributions in decomposing forest litterfall

Studying C functional group distributions in decomposing litterfall samples is one of the common methods of studying litterfall decomposition processes. However, the methods of studying the C functional group distributions, such as 13C NMR spectroscopy, are expensive and time consuming and new rapid and inexpensive technologies should be sought. Therefore, this study examined whether laboratory-based hyperspectral image analysis can be used to predict C functional group distributions in decomposing litterfall samples. Hyperspectral images were captured from ground decomposing litterfall samples in the visible to near infrared (VNIR) spectral range of 400–1000 nm. Partial least-square regression (PLSR) and artificial neural network (ANN) models were used to correlate the VNIR reflectance data measured from the litterfall samples with their C functional group distributions determined using 13C NMR spectroscopy. The results showed that alkyl-C, O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1, aryl-C2 and carboxyl derivatives could be acceptably predicted using the PLSR model, with R2 values of 0.72, 0.73, 0.71, 0.74, 0.76, 0.75 and 0.63 and ratio of prediction to deviation (RPD) values of 1.86, 1.82, 1.78, 1.71, 1.90, 1.76 and 1.43, respectively. Predicted O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1 and aryl-C2 using the ANN model provided R2 values of 0.62, 0.68, 0.69, 0.82 and 0.67 and the RPDs of 1.54, 1.76, 1.52, 2.10 and 1.72, respectively. With the exception of aryl-C1, the PLSR model was more reliable than the ANN model for predicting C functional group distributions given limited amount of training data. Neither the PLSR nor the ANN model could predict the carbohydrate-C and O-aryl-C acceptably. Overall, laboratory-based hyperspectral imaging in combination with the PLSR modelling can be recommended for the analysis of C functional group distribution in the decomposing forest litterfall samples. © 2018 Elsevier B.V