scholarly journals Numerical Simulation on Double-nozzle Spray Evaporation of Desulfurization Wastewater

2021 ◽  
Author(s):  
Hong Xu ◽  
Shuqin Feng ◽  
Liehui Xiao ◽  
Yazhen Hao ◽  
Xiaoze Du
Keyword(s):  
Flue Gas ◽  
Gas Flow ◽  
Evaporation Rate ◽  
Flow Direction ◽  
Droplet Diameter ◽  
Numerical Results ◽  
Least Square ◽  
Lagrangian Model ◽  
Support Vector ◽  
Exhaust Heat

To achieve the near zero emission of wastewater in the flue gas desulfurization (FGD) system in coal-fired power plant and better utilize the exhaust heat from flue gas, a feasible technology of spraying FGD wastewater in the flue duct for evaporation is discussed in the present study. A full-scale influencing factor investigation on the wastewater droplet evaporation performance is established under the Eulerian-Lagrangian model numerically. The dominant factors, including the characters of wastewater droplets, flue gas and the spray nozzles were analyzed under different conditions, respectively. Considering the multiple factors and conditions in the process, a Least-Square support vector machine (LSSVM) model is introduced to predict the evaporation rate based on the numerical results. Conclusions are made that the flue gas temperature and droplet diameter are of great importance in the evaporation process. The spray direction of droplet parallel with the flue gas flow direction is profitable for the dispersion of droplet, resulting the maximal evaporation rate. A double-nozzle arrangement optimized with relatively small flow rate is recommended. The LSSVM model can accurately predict the evaporation rate using the numerical results with different conditions.

scholarly journals FEATURES OF FLUE GAS CLEANING IN WET SCRUBBERS WITH VENTURI PIPE AT EXCESS SPRAYING

2018 ◽  
pp. 41-50
Author(s):  
I.A. Volchyn ◽  
V.A. Raschepkin
Keyword(s):  
Fly Ash ◽  
Flue Gas ◽  
Gas Flow ◽  
Droplet Diameter ◽  
Operating Mode ◽  
Hydrodynamic Resistance ◽  
Gas Cleaning ◽  
Wet Gas ◽  
Wet Scrubbers ◽  
Venturi Pipe

The radical increase in the density of spraying the flue gas stream in wet Venturi scrubbers allows to significantly increase the efficiency of these dust precipitators to the level of compliance with the European requirements for dust emissions. Such a shift in the operating mode significantly affects the nature of the processes of heat and mass transfer and has feature sthat are important to consider when reconstructing and designing wet gas cleaning plants. The mathematical modeling of the process of flue gas cleaning from fly ash particles in wet Venturi scrubbers in the conditions of excess spraying is performed, the dynamics of the main thermophysical parameters of the heterogeneous flow in a medium with variable moisture content and the influence of the droplet diameter on the efficiency of wet scrubbers are investigated. The problem of changing hydrodynamic resistance of a wet scrubber at different gas flow spraying densities has been studied; the effect of the input fly ash particles distribution on the result of dust cleaning is estimated. Bibl. 26, Fig. 5, Tab. 1.

An Echo State Network Based on Error Compensation for Prediction of Nonlinear Time Series in Industry

2012 ◽  
Vol 482-484 ◽  
pp. 99-102
Author(s):  
Quan Li Liu ◽  
Jun Zhao ◽  
Wei Wang ◽  
Yuan Bao Wang
Keyword(s):  
Time Series ◽  
Error Compensation ◽  
Gas Flow ◽  
Negative Impact ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Least Square ◽  
Support Vector ◽  
Echo State Network ◽  
Training Samples

The prediction accuracy of nonlinear time series is highly related to the dynamics of the data. In this study, a method based on error compensation is proposed to improve the accuracy of nonlinear time series prediction, especially for the problems in industry. First, an echo state network is modeled to describe the system complexity and then an error compensation is constructed based on least square support vector machine (LSSVM), in which a genetic algorithm is designed to select the training samples for LSSVM so as to reduce the negative impact by noise mixture. To verify the quality of the proposed model, a class of industrial problem for gas flow prediction is employed, and the experimental results indicate that the method gives a better performance than the others.

scholarly journals Analysis of Main Influencing Factors of the Wastewater Evaporation in Flue Duct

2020 ◽  
Vol 143 ◽  
pp. 02025
Author(s):  
Haowei Pan ◽  
Liang Zhang ◽  
Wei Li ◽  
Ning Gao ◽  
Yin Liu
Keyword(s):  
Flue Gas ◽  
Gas Flow ◽  
Droplet Diameter ◽  
Operating Cost ◽  
Simulation Method ◽  
Inlet Temperature ◽  
Factors Affecting ◽  
Two Factors ◽  
High Pollution ◽  
Desulfurization Wastewater

Desulfurization wastewater has the characteristics of small discharge and high pollution, and must be strictly treated. To obtain the main factors affecting the evaporation characteristics of desulfurization wastewater in boiler flue, a 600MW unit of a coal-fired power plant in China was taken as an example. According to the theory of fluid mechanics and heat transfer, the numerical simulation method was used. The results show that the way the nozzle is installed on the upper wall of the flue inlet can enhance the evaporation effect of the desulfurization wastewater. It is also revealed that the influence of the flue gas flow rate on the droplet evaporation effect is relatively small. The smaller droplet diameter and the higher flue gas inlet temperature will obviously enhance the evaporation effect of the droplets in the flue. However these two factors will increase the operating cost and reduce the boiler thermal efficiency. Therefore, the values of the droplet diameter and the flue gas inlet temperature need to be further determined by technical and economic comparison.

EFFECT OF GAS FLOW DIRECTION ON PASSIVE SUBSEA COOLER EFFECTIVENESS

2019 ◽  
Vol 11 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
Nikolay Ivanov ◽  
Vladimir V. Ris ◽  
Nikolay A. Tschur ◽  
Marina Zasimova
Keyword(s):  
Gas Flow ◽  
Flow Direction

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 618
Author(s):  
Tanvir Tazul Islam ◽  
Md Sajid Ahmed ◽  
Md Hassanuzzaman ◽  
Syed Athar Bin Amir ◽  
Tanzilur Rahman
Keyword(s):  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Quantitative Estimation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Frequency Noise ◽  
Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

scholarly journals Effect of Flue Gases’ Corrosive Components on the Degradation Process of Evaporator Tubes

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3860
Author(s):  
Mária Hagarová ◽  
Milan Vaško ◽  
Miroslav Pástor ◽  
Gabriela Baranová ◽  
Miloš Matvija
Keyword(s):  
Flue Gas ◽  
Power Plants ◽  
Gas Flow ◽  
Degradation Process ◽  
Flue Gases ◽  
Saturated Steam ◽  
Mineral Salts ◽  
Related Factors ◽  
Local Overheating ◽  
Inner Surface

Corrosion of boiler tubes remains an operational and economic limitation in municipal waste power plants. The understanding of the nature, mechanism, and related factors can help reduce the degradation process caused by corrosion. The chlorine content in the fuel has a significant effect on the production of gaseous components (e.g., HCl) and condensed phases on the chloride base. This study aimed to analyze the effects of flue gases on the outer surface and saturated steam on the inner surface of the evaporator tube. The influence of gaseous chlorides and sulfates or their deposits on the course and intensity of corrosion was observed. The salt melts reacted with the steel surface facing the flue gas flow and increased the thickness of the oxide layer up to a maximum of 30 mm. On the surface not facing the flue gas flow, they disrupted the corrosive layer, reduced its adhesion, and exposed the metal surface. Beneath the massive deposits, a local overheating of the inner surface of the evaporator tubes occurred, which resulted in the release of the protective magnetite layer from the surface. Ash deposits reduce the boiler’s thermal efficiency because they act as a thermal resistor for heat transfer between the flue gases and the working medium in the pipes. The effect of insufficient feedwater treatment was evinced in the presence of mineral salts in the corrosion layer on the inner surface of the tube.

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