An experimental modelling and performance validation study: Top gas pressure tracking system in a blast furnace using soft computing methods

2021 ◽  
pp. 095440892110331
Author(s):  
Yasin Tunckaya
Keyword(s):  
Blast Furnace ◽  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Tracking System ◽  
Computing Methods ◽  
Gas Pressure ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy

The blast furnace is a master iron-producing plant of iron and steel factories and affected by several process parameters as well as top gas pressure , which is a key process control phenomenon to maintain stability and operational productivity in such plants. Blast furnace operation is not tolerant to any interruption, unbalanced operations, momentary disturbances or loss of control due to its nature of intensive chemical reactions and heat balance requirements. Consequently, it is crucial to monitor and control top gas system components of the furnace with instrumentation measurements to maintain stable, efficient operation and system safety ongoing. In this study, a novel top gas pressure tracking system is developed using the chronologically obtained live process data of Erdemir BF#2 in Turkey. Eight process parameters are considered as input parameters as per the plant maintenance team's recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. Performance and success ratio analysis is carried out using coefficient of determination ( R2), mean absolute percentage error and root mean squared error terms. The best performing model output for the adaptive neuro fuzzy inference system is found to be 0.95, 1.21 and 0.023, and slightly lower performance is obtained for the artificial neural network model with the output values of 0.94, 0.029 and 1.32 against R2, mean absolute percentage error and root mean squared error terms, respectively. The maximum prediction error is found to be 9.85% and 10.2%, and the average prediction error is found to be 1.19% and 1.29% for adaptive neuro fuzzy inference system and ANN models, respectively, for optimum simulations. The proposed neuro-fuzzy-driven top gas pressure prediction system is unique in the literature and should be integrated into existing control systems to improve operational awareness and sustainability or can be used as input guidance for a possible future top gas recovery system.

scholarly journals Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

2015 ◽  
Vol 8 (1) ◽  
pp. 369-384 ◽  
Author(s):  
K. Ramesh ◽  
A. P. Kesarkar ◽  
J. Bhate ◽  
M. Venkat Ratnam ◽  
A. Jayaraman
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Microwave Radiometer ◽  
Percentage Error ◽  
Computational Techniques ◽  
Temperature Profiles ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Temperature And Humidity ◽  
Rainy Days

Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

APLIKASI SISTEM CERDAS UNTUK PREDIKSI ENERGI LISTRIK PEMAKAIAN SENDIRI DI PT INDONESIA POWER SUB UNIT PLTA KABUPATEN WONOGIRI

2017 ◽  
Vol 8 (2) ◽  
pp. 489
Author(s):  
Herliyani Hasanah ◽  
Nurmalitasari Nurmalitasari
Keyword(s):  
Fuzzy Inference System ◽  
Input Data ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy Input

Kebutuhan akan energi listrik menjadi kebutuhan primer nasional. Dalam keberlangsungan proses produksi energi listrik pada pembangkitan – pembangkitan diperlukan energi listrik untuk pemakaian sendiri. Dalam penelitian ini dibangun sebuah aplikasi sistem cerdas untuk memprediksi energi listrik pemakaian sendiri di PT Indonesia Power sub unit PLTA Wonogiri. Pada penelitian ini menggunakan 2 kelompok input, yaitu input FIS (Fuzzy Inference System) dan input pada NN (Neuro Fuzzy). Input data  merupakan data produksi harian energi listrik di PLTA Wonogiri selama kurun waktu 2010 – 2016. Variabel data yang digunakan dalam penelitian ini adalah data produksi listrik untuk pemakaian PLTA Wonogiri adalah energi listrik yang dihasilkan PLTA Wonogiri dengan satuan KwH (f), elevasi muka air waduk dengan satuan meter (a1) dan debit air yang masuk ke turbin dengan satuan /detik (a2).  Output yang diperoleh adalah pusat centroid (m), derajat keanggotaan (mf), bobot (w) dan konsekuen parameter ( c ). Dari hasil pengujian diperoleh keluaran dengan performansi yang optimal pada saat Fuzzy C Means 2 kelas dengan parameter laju pembelajaran 0.4, momentum 0.6 dengan bessar Mean Percentage Error 0.377970875.  Kata kunci:  prediksi, pemakaian sendiri, energi listrik, fuzzy inference system, neuro fuzzy

scholarly journals The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Abdullahi Garba Usman ◽  
Mubarak Hussaini Ahmad ◽  
Rabi’u Nuhu Danraka ◽  
Sani Isah Abba
Keyword(s):  
Linear Model ◽  
Fuzzy Inference System ◽  
Molecular Mechanisms ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Inflammatory Biomarkers ◽  
Herbal Products ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Comparison Of The Results

Abstract Background Medicinal plants are used to manage pain and inflammatory disorders in traditional medicine. A scientific investigation could serve as a basis for the determination of molecular mechanisms of antinociceptive and antiinflammatory actions of herbal products. In this work, we used both artificial intelligence (AI) based models inform of adaptive neuro-fuzzy inference system and artificial neural network (ANN) as well as a linear model, namely; stepwise linear regression in modelling the performance of four different inflammatory biomarkers namely; interleukin (1L)-1β, 1L-6, tumour necrosis factor (TNF)-α and prostaglandin E2 (PGE2). This modelling was done using number of abdominal writes, the reaction time of paw licking in mice and paw oedema diameter as the input variables. Results Four different performance indices were employed, which are determination coefficient (DC), root mean squared error (RMSE), mean square error (MSE) and correlation co-efficient (CC). The results have shown the superiority of the AI-based models over the linear model. Conclusions The overall quantitative and visualized comparison of the results showed that adaptive neuro-fuzzy inference system outperformed the ANN and SWLR models in modelling the performance of the four inflammation biomarkers in both the calibration and verification phases.

scholarly journals A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

2020 ◽  
Author(s):  
Sohail Saif ◽  
Priya Das ◽  
Suparna Biswas
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Bees Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Hybrid Forecasting

Abstract In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

scholarly journals Prediction of strength and durability properties of HPC composites using Adaptive Neuro-fuzzy Inference System

2020 ◽  
Vol 184 ◽  
pp. 01102
Author(s):  
P Magudeaswaran. ◽  
C. Vivek Kumar ◽  
Rathod Ravinder
Keyword(s):  
Fuzzy Inference System ◽  
High Performance ◽  
Fuzzy Inference ◽  
High Performance Concrete ◽  
Intelligent System ◽  
Computing Methods ◽  
Inference System ◽  
Performance Requirements ◽  
Neuro Fuzzy ◽  
And Performance

High-Performance Concrete (HPC) is a high-quality concrete that requires special conformity and performance requirements. The objective of this study was to investigate the possibilities of adapting neural expert systems like Adaptive Neuro-Fuzzy Inference System (ANFIS) in the development of a simulator and intelligent system and to predict durability and strength of HPC composites. These soft computing methods emulate the decision-making ability of human expert benefits both the construction industry and the research community. These new methods, if properly utilized, have the potential to increase speed, service life, efficiency, consistency, minimizes errors, saves time and cost which would otherwise be squandered using the conventional approaches.

scholarly journals Early Warning System for Flood Disaster Prediction in Wetland Area in Greater Yola Using Adaptive Neuro Fuzzy Inference System

2019 ◽  
pp. 1-19
Author(s):  
Ishaya Bitrus ◽  
P. B. Zirra ◽  
Sarjiyus Omega
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Human Life ◽  
Warning System ◽  
Flood Disaster ◽  
Percentage Error ◽  
Flood Prediction ◽  
Inference System ◽  
Flood Level ◽  
Neuro Fuzzy

Natural calamity disrupts our daily life activities; thereby bring many sufferings in our life. One of the natural disasters is the flood. Flood is one of the most catastrophic disasters. However, too much rainfall courses environmental hazard. These prompted to flood prediction in order to help communities and Government with the necessary tool to take precaution to safe human life and properties. This work was developed using an (ANFIS) Adaptive Neuro-Fuzzy Inference System to compare some weather parameter (temperature and relative humidity) with rainfall to forecast the amount of rainfall capable of coursing flood in the study area. From the above graph (Fig. 22) it can be seen that the actual and the forecasted rainfall followed the same pattern from 2008 to 2010 with slight decrease in 2011. A high amount of rainfall in 2012 was forecasted to be flooded during that year and tally with the forecasted rainfall on the above graph in 2012. Based on the results on the graph, it shows that from 2014 to 2017 gives a constant flow between the actual and forecasted rainfall. It is predicted that the maximum amount of rainfall forecasted was 124.0 mm which is far below the recommended flood level of 160.0 mm which reveals that, River Benue would not experience flood disaster in the year ahead. The model developed was validated using (MAPE) Mean Absolute Percentage Error as 4.0% with model efficiency of 96.0% which shows very high excellent prediction accuracy.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

2012 ◽  
Vol 57 (4) ◽  
pp. 933-943
Author(s):  
Abbas Aghajani Bazzazi ◽  
Mohammad Esmaeili
Keyword(s):  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Open Pit ◽  
Good Prediction ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Good Prediction Performance ◽  
Predicted Values

Abstract Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

scholarly journals AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND

Aviation ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 150-163 ◽  
Author(s):  
Panarat Srisaeng ◽  
Glenn S. Baxter ◽  
Graham Wild
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Low Cost ◽  
Percentage Error ◽  
Subtractive Clustering ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Partition Method

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

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