ANFIS-based model for predicting actual shear rate associated with wall slip phenomenon

Chin, Ren Jie; Lai, Sai Hin; Ibrahim, Shaliza; Jaafar, Wan Zurina Wan; El‐Shafie, Ahmed

doi:10.1007/s00500-019-04475-5

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…Adaptive neuro-fuzzy inference system (ANFIS) is a technique that integrates both neural networks and fuzzy logic principles within a single framework. This may strengthen the ability of the model to reach a higher level of accuracy [26][27][28]. A basic ANFIS architecture is presented in Fig.…”

Section: Architecture Of Adaptive Neuro-fuzzy Inference System (Anfis...mentioning

confidence: 99%

“…where 𝐴 1 , 𝐴 2 , 𝐵 1 and 𝐵 2 are the membership functions for each input x and y (part of the premises), f 1 and f 2 are the outputs within the fuzzy region specified by the fuzzy rule, while 𝑝 1 , 𝑞 1 , 𝑟 1 , 𝑝 2 , 𝑞 2 and 𝑟 2 are linear parameters in part-Then (consequent part) of Takagi-Sugeno fuzzy inference model [26].…”

Section: Architecture Of Adaptive Neuro-fuzzy Inference System (Anfis...mentioning

confidence: 99%

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New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

Hin

Chin

Goh

et al. 2022

IIUMEJ

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Fecal coliform removal using stormwater biofilters is an important aspect of stormwater management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R2-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work. ABSTRAK: Penyingkiran kolifom tinja menggunakan turas biologi (bioturas) air hujan merupakan aspek penting dalam pengurusan air hujan. Model yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting. Oleh itu, model rangkaian suapan neural perambatan belakang (FBNN) dan sistem adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri input, iaitu jenis rumput, ketebalan bioturas dan kepekatan awal E. coli, manakala anggaran kepekatan akhir bagi E. coli merupakan hasil pembolehubah. Model ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding model FBNN, kerana ia mempunyai nilai R2 yang lebih tinggi iaitu 0.9874, nilai MAE dan RMSE yang lebih rendah iaitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih kecil sebanyak 14.2%. Oleh itu, model ANFIS yang dicadangkan boleh dijadikan alternatif awal bagi menggantikan keperluan kerja makmal.

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Section: Architecture Of Adaptive Neuro-fuzzy Inference System (Anfis...mentioning

confidence: 99%

Section: Architecture Of Adaptive Neuro-fuzzy Inference System (Anfis...mentioning

confidence: 99%

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

Hin

Chin

Goh

et al. 2022

IIUMEJ

Self Cite

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show abstract

“…ANFIS (adaptive network-based fuzzy inference system) is among these techniques attracting the researchers as a result of its capability for learning multifaceted associations with usages indicated in several studies. ,− The accuracy of the ANFIS approach can be changed by altering the prediction model structure. Moreover, it can be adapted based on the association complexity.…”

Section: Introductionmentioning

confidence: 99%

gbell Learning function along with Fuzzy Mechanism in Prediction of Two-Phase Flow

et al. 2020

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The integration of the computational fluid dynamics (CFD) and the adaptive network-based fuzzy inference system, known as ANFIS, is investigated for simulating the hydrodynamic in a bubble column reactor. The Eulerian–Eulerian two-phase model is employed as the CFD approach. For the ANFIS technique, a sensitivity analysis is done by varying the number of inputs and the number of membership functions (MFs). The x and z coordinates of the fluid location, the air velocity, and the pressure are considered as the inputs of the ANFIS, while the air vorticity is the output. The results revealed that the ANFIS with all four inputs and the MFs of five achieved the highest intelligence with the regression number close to 1. More specifically, gbell function in the learning framework is used to train all local computing nodes from solving Navier–Stokes equations. In the decision or prediction part, the fuzzy mechanism is used for the prediction of extra nodes that solve, which Navier–Stokes equations did not solve. The results show that the gbell function enables us to fully train all numerical points and also store data set in the frame of mathematical equations. Besides, this function responds well with the number of inputs and MFs for accurate prediction of reactor hydrodynamics. Additionally, a high number of MFs and input parameters influence the accuracy of the method during prediction. In the current study, gbell MF was studied to investigate its accuracy in the prediction of the two-phase flow. Also, different numbers of MFs were considered to investigate the level of accuracy and capability of prediction. ANFIS clustering methods, grid partition and fuzzy C-mean (FCM) clustering, are compared to see the ability of the method in prediction. To compare the accuracy of the ANFIS method with FCM clustering, the data were compared to the gaussmf function. The results showed that the method has high accuracy and that it could predict the flow pattern.

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“…), find the general pattern of data, and predict the targeted variables for the other conditions without any further computational expenses. The machine learning of the CFD results was used for the first time by references [15][16][17][18][19][20][21][22] . The researchers used the fuzzy inference system (FIS) with the adaptive network (AN) algorithm for the training process.…”

mentioning

confidence: 99%

Pressure and temperature predictions of Al2O3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS

Babanezhad

Behroyan

Marjani

et al. 2021

Sci Rep

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Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al2O3/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses.

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ANFIS-based model for predicting actual shear rate associated with wall slip phenomenon

Cited by 15 publications

References 35 publications

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

gbell Learning function along with Fuzzy Mechanism in Prediction of Two-Phase Flow

Pressure and temperature predictions of Al2O3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS

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