scholarly journals Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 965 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Masoud Hadipoor ◽  
Alireza Baghban ◽  
Amir Mosavi ◽  
Jozsef Bukor ◽  
...  
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Operational Parameters ◽  
Inference System ◽  
Mercury Emissions ◽  
Power Stations ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

scholarly journals Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahab Shamshirband ◽  
Masoud Hadipoor ◽  
Alireza Baghban ◽  
Amir Mosavi ◽  
Jozsef Bukor ◽  
...  
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Operational Parameters ◽  
Inference System ◽  
Mercury Emissions ◽  
Power Stations ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

scholarly journals Developing an ANFIS-PSO Model to Estimate Mercury Emission in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Alireza Baghban ◽  
Masoud Hadipoor ◽  
Amir Mosavi
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Operational Parameters ◽  
Inference System ◽  
Power Stations ◽  
Neuro Fuzzy ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed ANFIS-PSO model the statistical meter of MARE% was implemented, which resulted 0.003266 and 0.013272 for training and testing respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

scholarly journals Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Alireza Baghban ◽  
Masoud Hadipoor ◽  
Amir Mosavi
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Inference System ◽  
Power Stations ◽  
Proposed Model ◽  
Operational Information ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost important to environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with particle swarm optimization (PSO). Input parameters were selected from coal characteristics and the operational configuration of boilers. The proposed ANFIS-PSO model is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed model. To evaluate the performance of the proposed model the statistical meter of MARE% was implemented, which resulted 0.003266 and 0.013272 for training and testing respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of PSO-ANFIS model.

scholarly journals Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahab Shahab ◽  
Alireza Baghban ◽  
Masoud Hadipoor
Keyword(s):  
Flue Gas ◽  
Power Plants ◽  
Fuzzy Inference ◽  
Mercury Content ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Operational Information ◽  
Predicted Values ◽  
Relative Errors

Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Input parameters were selected from coal characteristics and the operational configuration of boilers. The ANFIS approach is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed ANFIS model. Resulted values from the model were compared to the collected data and it indicates that the model possesses an extraordinary level of precision with a correlation coefficient of unity. The MARE% for training and testing parts were 0.003266 and 0.013272, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1% which confirm the accuracy of PSO-ANFIS model.

Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 792 ◽  
pp. 243-247 ◽  
Author(s):  
Alexandra Khalyasmaa ◽  
Artem Aminev ◽  
Dmitry Bliznyuk
Keyword(s):  
Expert Systems ◽  
Fuzzy Inference ◽  
Technical Condition ◽  
Gas Analysis ◽  
Assessment System ◽  
Technical Diagnostics ◽  
Inference System ◽  
Power Stations ◽  
Neuro Fuzzy ◽  
Equipment State

The paper is dedicated to analyze the modern expert systems to assess the technical condition of power stations and substations high-voltage equipment. The main problems of modern expert systems and their possible solutions are determined. As the structure and their basic construction principles are considered. Also this paper proposes an algorithm for the expert system model using fuzzy inference on the basis of technical diagnostics and tests. As a case study of assessment of power transformers state based on dissolved gas analysis in the oil is presented.

Fuzzy Inference System to Automatic Fault Classification in Power Plants

2016 ◽  
Vol 14 (2) ◽  
pp. 746-751
Author(s):  
Miguel Moreto ◽  
Dionatan Augusto Guimaraes Cieslak
Keyword(s):  
Fuzzy Inference System ◽  
Power Plants ◽  
Fuzzy Inference ◽  
Fault Classification ◽  
Inference System

scholarly journals The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

2021 ◽  
Vol 13 (19) ◽  
pp. 10541
Author(s):  
Yan Li ◽  
Fathin Nur Syakirah Hishamuddin ◽  
Ahmed Salih Mohammed ◽  
Danial Jahed Armaghani ◽  
Dmitrii Vladimirovich Ulrikh ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Fuzzy Model ◽  
Inference System ◽  
Ranking Systems ◽  
Neuro Fuzzy ◽  
Index Tests ◽  
Predicted Values ◽  
Non Destructive

Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of MLR and ANFIS approaches, where the focus was to consider the most important parameters of these techniques. As a result, and according to background and behaviour of the ANFIS (or neuro-fuzzy) model, the predicted values obtained by this intelligent methodology are closer to the actual BTS compared to MLR which works based on linear statistical rules. For instance, in terms of system error and a-20 index, values of (0.84 and 1.20) and (0.96 and 0.80) were obtained for evaluation parts of ANFIS and MLR techniques, which revealed that the ANFIS model outperforms the MLR in forecasting BTS values. In addition, the same results were obtained through ranking systems by the authors. The neuro-fuzzy developed in this study is a strong technique in terms of prediction capacity and it can be used in the other rock-based projects for solving relevant problems.

The Output Power of the PV Power Plant Modeling Based on ANFIS

2014 ◽  
Vol 1006-1007 ◽  
pp. 945-954 ◽  
Author(s):  
Ling Liu ◽  
Bao Guo Tang ◽  
Kai Sun
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Power Plants ◽  
Fuzzy Inference ◽  
Engineering Application ◽  
Subtractive Clustering ◽  
Weather Factors ◽  
Inference System ◽  
Practical Engineering ◽  
Application Requirements

To find an effective and reasonable method for calculating precisely the output power of the PV power plant, adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno (TS) is proposed. Analysis of the various weather factors that affect the output power of the PV power plant, and select the appropriate input ,MATLAB as a tool ,depend on the different input variable to establish different output power of photovoltaic power plants based on the subtractive clustering the ANFIS model .Results show that all the model has a high accuracy and meet the practical engineering application requirements,by comparing models choose the optimal model.

scholarly journals Model of medium-term forecasting of energy mix in Poland

2019 ◽  
Vol 108 ◽  
pp. 01002
Author(s):  
Janusz Sowiński
Keyword(s):  
Electricity Generation ◽  
Power Plants ◽  
Fuzzy Inference ◽  
Medium Term ◽  
Short Term ◽  
Inference System ◽  
Energy Mix ◽  
Neuro Fuzzy ◽  
Forecasting Method ◽  
Main Activity

The paper presents analyses based on data published by ARE S.A. (Energy Market Agency) concerning the balance and structure of electricity generation. The data include monthly amount of energy generated by main activity power plants (thermal, hydropower and wind), independent power producers and industrial combined heat and power plants. The forecasting method based on Adaptive Neuro-Fuzzy Inference System (ANFIS) provides a tool for making a short-term forecast of electricity generation, together with its structure, by means of which it is possible to analyze the energy mix. The results of estimation and verification of the above-mentioned model are presented as well as selected forecast results.

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