scholarly journals ABC-ANFIS-CTF: A Method for Diagnosis and Prediction of Coking Degree of Ethylene Cracking Furnace Tube

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 909 ◽  
Author(s):  
Zhiping Peng ◽  
Junfeng Zhao ◽  
Zhaolin Yin ◽  
Yu Gu ◽  
Jinbo Qiu ◽  
...  
Keyword(s):  
Ethylene Production ◽  
Fuzzy Inference ◽  
Prediction Method ◽  
Development Trend ◽  
Inference System ◽  
Bee Colony ◽  
Treatment Measures ◽  
Furnace Tube ◽  
Diagnosis Accuracy ◽  
Ethylene Cracking Furnace

The carburizing and coking of ethylene cracking furnace tubes are the important factors that affect the energy efficiency of ethylene production. To realize the diagnosis and prediction of the different coking degrees of cracking furnace tubes, and then take corresponding treatment measures, is of great significance for improving ethylene production and prolonging the service life of the furnace tube. Therefore, a fusion diagnosis and prediction method based on artificial bee colony (ABC) and adaptive neural fuzzy inference system (ANFIS) is proposed, which also introduces a coking-time factor (CTF). The actual data verification shows that the method not only improves the training efficiency and diagnosis accuracy of the coking diagnosis and inference system of the cracking furnace tube, but also realizes the prediction of the development trend of the coking degree of the furnace tube.

scholarly journals Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
İsmail Kıyak ◽  
Gökhan Gökmen ◽  
Gökhan Koçyiğit
Keyword(s):  
Fuzzy Inference ◽  
Prediction Method ◽  
Operating Conditions ◽  
Lifetime Prediction ◽  
Statistical Characteristics ◽  
Ambient Conditions ◽  
Inference System ◽  
Initial Task ◽  
Neuro Fuzzy ◽  
A Cell

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

Modeling and predict environmental indicators for land leveling using adaptive neuro-fuzzy inference system (ANFIS), and regression

2018 ◽  
Vol 12 (4) ◽  
pp. 484-506 ◽  
Author(s):  
Farhad Mirzaei ◽  
Mahmoud Delavar ◽  
Isham Alzoubi ◽  
Babak Nadjar Arrabi
Keyword(s):  
Energy Consumption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Environmental Indicators ◽  
Inference System ◽  
Content Type ◽  
Bee Colony ◽  
Fill Volume ◽  
Land Leveling ◽  
Neural Fuzzy

PurposeThe purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.Design/methodology/approachThis paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).FindingsBy applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highestR2value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highestR2with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.Originality/valueAs land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

scholarly journals Neuro-Fuzzy Dynamic Position Prediction for Autonomous Work-Class ROV Docking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 693
Author(s):  
Petar Trslić ◽  
Edin Omerdic ◽  
Gerard Dooly ◽  
Daniel Toal
Keyword(s):  
Fuzzy Inference ◽  
North Atlantic Ocean ◽  
Prediction Method ◽  
Hydrodynamic Drag ◽  
Remotely Operated Vehicle ◽  
Inference System ◽  
Drag Forces ◽  
Neuro Fuzzy ◽  
Heave Motion ◽  
Work Class

This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system.

scholarly journals A Novel Exercise Thermophysiology Comfort Prediction Model with Fuzzy Logic

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Nan Jia ◽  
Liang Yu ◽  
KaiXing Yang ◽  
RuoMei Wang ◽  
XiaoNan Luo ◽  
...  
Keyword(s):  
Prediction Model ◽  
Human Body ◽  
Fuzzy Inference ◽  
Exercise Program ◽  
Prediction Method ◽  
Inference System ◽  
Physiological Properties ◽  
Improve Health Status ◽  
Regulatory Model ◽  
Improve Health

Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE) as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

Artificial Bee Colony-Fuzzy Q Learning for Reinforcement Fuzzy Control (Truck Backer-Upper Control Problem)

2016 ◽  
Vol 24 (01) ◽  
pp. 123-136 ◽  
Author(s):  
Sima Saeed ◽  
Aliakbar Niknafs
Keyword(s):  
Fuzzy Control ◽  
Fuzzy System ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Fuzzy Inference ◽  
Food Resource ◽  
Q Value ◽  
Inference System ◽  
Q Learning ◽  
Bee Colony

A new method for reinforcement fuzzy controllers is presented by this article. The method uses Artificial Bee Colony algorithm based on Q-Value to control reinforcement fuzzy system; the algorithm is called Artificial Bee Colony-Fuzzy Q learning (ABC-FQ). In fuzzy inference system, precondition part of rules is generated by prior knowledge, but ABC-FQ algorithm is responsible to achieve the best combination of actions for the consequence part of the rules. In ABC-FQ algorithm, each combination of actions is considered a food source for consequence part of the rules and the fitness level of this food source is determined by Q-Value. ABC-FQ Algorithm selects the best food resource, which is the best combination of actions for fuzzy system, using Q criterion. This algorithm tries to generate the best reinforcement fuzzy system to control the agent. ABC-FQ algorithm is used to solve the problem of Truck Backer-Upper Control, a reinforcement fuzzy control. The results have indicated that this method arrives to a result with higher speed and fewer trials in comparison to previous methods.

Surface Roughness Prediction in Precision Surface Grinding of Nano-Ceramic Coating Based on Improved ANFIS

2010 ◽  
Vol 44-47 ◽  
pp. 2293-2298 ◽  
Author(s):  
Xiong Hua Guo ◽  
Mao Fu Liu ◽  
Chang Rong Zhao
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Ceramic Coating ◽  
Fuzzy Inference ◽  
Prediction Method ◽  
Surface Grinding ◽  
Precision Grinding ◽  
Anfis Model ◽  
Inference System ◽  
Roughness Prediction

For improving surface integrity and machining quality after precision grinding of the parts of nano-ceramic coating, and investigating its prediction technique of surface roughness, the prediction model of surface roughness in precision surface grinding of nano-ceramic coating based on adaptive network-based fuzzy inference system (ANFIS) was proposed in this paper. Then, the proposed prediction model was improved by hybrid Taguchi genetic algorithm (HTGA). At last, by comparative analysis of prediction results from traditional BP neural network model, simple ANFIS model and improved ANFIS model, the effectiveness of the proposed model was verified using grinding parameters and measured surface roughness in grinding tests as training and testing samples. It showed that the prediction accuracy of the improved ANFIS model proposed in this paper was higher, and it was an effective prediction method of surface roughness in precision grinding of nano-ceramic coating.

scholarly journals A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1071 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Xiaohui Cui
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Oil Consumption ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Prediction Problems

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

scholarly journals Smart Prediction Method of Software Defect Using Neuro-Fuzzy Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 6-10
Author(s):  
Sunil Kumar Singh ◽  
Raj Shree
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Prediction Method ◽  
Inference System ◽  
Software Failure ◽  
Neuro Fuzzy ◽  
Software Program ◽  
Software Fault ◽  
Data Program ◽  
Program Reliability

Faults in software program structures continue to be a primary problem. A software fault is a disorder that reasons software failure in an executable product. A form of software fault predictions techniques were proposed, however none has proven to be continually correct. So, on this examine the overall performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting software program defects and software program reliability has been reviewed. The datasets are taken from NASA Metrics Data Program (MDP) statistics repository. In the existing work a synthetic intelligence technique viz. Adaptive Neuro Fuzzy Inference System (ANFIS) goes for use for software disorder prediction.

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