Fuzzy Modeling of a Nonlinear Continuous Fermentation Bioreactor for Ethanol Production

2012 ◽  
Author(s):  
Alfredo Díaz Jácome ◽  
Marco Sanjuan
Keyword(s):  
Ethanol Production ◽  
Fuzzy Inference ◽  
Continuous Fermentation ◽  
Nonlinear Behavior ◽  
Correlation Factor ◽  
Inlet Temperature ◽  
Kinetics Parameters ◽  
Inference System ◽  
Fuzzy Techniques ◽  
Basic Equations

Ethanol production from glucose was analyzed implementing fuzzy techniques to investigate and model the nonlinear behavior of the process. The model simulated involves the basic equations of a chemostat including the dependence of kinetics parameters on temperature and mass transfer of oxygen. For the identification of nonlinearities. Steady-state values for inlet temperature, the flow of refrigerant and the initial concentration of substrate are 25 ° C, 18 L / h and 60 g / L glucose, respectively. The implementation of the Fuzzy Inference System (FIS) was based on a Mamdani inference engine, and also that the defuzzification method implemented was centroid and the weight of the trapeziums describing the output from 15 to 30 g/L affects considerably. After evaluating and comparing the results of the simulation with FIS results, and calculating the correlation between Predicted and Expected values, it is concluded that correlation factor for the entire simulation and FIS was around 0.9. The lack of fitness is evaluated and analyzed after the comparison of results.

A General Methodology for Optimized Takagi-Sugeno Fuzzy Modeling of Nonlinear Continuous Fermenter for Biofuels (Ethanol) Production Using Golden Section Search Approach

2014 ◽  
Author(s):  
Alfredo Diaz Jacome ◽  
Marco Sanjuan Mejia
Keyword(s):  
Experimental Data ◽  
Ethanol Production ◽  
Search Algorithm ◽  
Golden Section ◽  
Nonlinear Behavior ◽  
Fuzzy Modeling ◽  
Biodiesel Production ◽  
Inference System ◽  
General Methodology ◽  
Highly Nonlinear

The overcoming inclusion of biotechnology in biofuels industry involves several challenges among which are found the variety of operational cycles, the highly nonlinear behavior of the processes and the need for measurement of intermediate variables. In order to reproduce biological conversion of biodiesel production discharge products into other biofuels, experimental data from ethanol production from glycerol/glucose mixture was analyzed implementing fuzzy techniques to investigate and model the nonlinear behavior of the process. This paper presents a general methodology for TS fuzzy modeling based on a novel approach on data structured regression which consists on combination of fuzzy c-regression model and clustering using a golden search algorithm approach to adjust the proper number of membership functions to fit the model and minimize the statistic difference among the experimental data, simulated data and the Fuzzy Inference System results.

scholarly journals Specific Wear Rate Modeling of Polytetraflouroethylene Composites Via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools

2021 ◽  
Author(s):  
Musa Alhaji Ibrahim ◽  
Yusuf Şahin ◽  
Auwal Ibrahim ◽  
Auwalu Yusuf Gidado ◽  
Mukhtar Nuhu Yahya
Keyword(s):  
Neural Network ◽  
Wear Rate ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Specific Wear Rate ◽  
Inference System ◽  
Modeling And Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Ptfe Composites

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.

scholarly journals Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Fuzzy Inference System ◽  
Metal Foam ◽  
Fuzzy Inference ◽  
Cfd Modeling ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Inlet Temperature ◽  
Inference System ◽  
Turbulence Parameters

Abstract The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.

Demand forecasting for apparel manufacturers by using neuro-fuzzy techniques

2014 ◽  
Vol 9 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Asli Aksoy ◽  
Nursel Öztürk ◽  
Eric Sucky
Keyword(s):  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Real Life ◽  
Learning Capability ◽  
Inference System ◽  
Content Type ◽  
Literature Research ◽  
Forecasting System ◽  
Fuzzy Techniques ◽  
Apparel Manufacturers

Purpose – According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers. Design/methodology/approach – The ANFIS is used forecasting the demand for apparel manufacturers. Findings – The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply. Originality/value – ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.

scholarly journals Prediction of Compressive Strength of Rice Husk Ash Concrete through Different Machine Learning Processes

Crystals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 352
Author(s):  
Ammar Iqtidar ◽  
Niaz Bahadur Khan ◽  
Sardar Kashif-ur-Rehman ◽  
Muhmmad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Rice Husk ◽  
Carbon Dioxide Emissions ◽  
Rice Husk Ash ◽  
Fuzzy Inference ◽  
Correlation Factor ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Data Points

Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet

2021 ◽  
Vol 20 (Supp01) ◽  
pp. 2140008
Author(s):  
R. Kannamma ◽  
K. S. Umadevi
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Data Traffic ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Fuzzy Techniques ◽  
Critical Traffic ◽  
Time Critical

IEEE802.1 Time-Sensitive Networking (TSN) makes it conceivable to convey the data traffic of time as well as critical applications using Ethernet shared by different applications having diversified Quality of Service (QoS) requirements for both TSN and non-TSN. TSN assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data for time-critical traffic. By holding networking resources for basic traffic, and applying different queuing and traffic shaping strategies, TSN accomplishes zero congestion loss for basic time-critical traffic. In proposed system, backpropagation algorithm is used to train the training set and fuzzy inference system methodologies such as Mamdani fuzzy inference system which has fuzzy inputs and fuzzy outputs, Sugeno FIS which has fuzzy inputs and a crisp output and adaptive-network-based fuzzy inference system has obtained from the neural network and fuzzy logic. The proposed system uses neuro-fuzzy techniques to handle frame pre-emption and reduces the time taken for decision making. It presents a decision making process using the traffic class.

scholarly journals Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques

2010 ◽  
Vol 20 (3) ◽  
pp. 363-376 ◽  
Author(s):  
Rudra Dash ◽  
Bidyadhar Subudhi
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Fault Location ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Induction Machine ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Fuzzy Techniques

Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniquesMotivated by the superior performances of neural networks and neuro-fuzzy approaches to fault detection of a single phase induction motor, this paper studies the applicability these two approaches for detection of stator inter-turn faults in a three phase induction motor. Firstly, the paper develops an adaptive neural fuzzy inference system (ANFIS) detection strategy and then compares its performance with that of using a multi layer perceptron neural network (MLP NN) applied to stator inter-turn fault detection of a three phase induction motor. The fault location process is based on the monitoring the three phase shifts between the line current and the phase voltage of the induction machine.

Nonlinear System Identification of Smart Buildings

2017 ◽  
pp. 328-347
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

Computational Intelligence Techniques for Assessing Anthropometric Indices Changes in Female Athletes

2020 ◽  
Vol 16 (4) ◽  
pp. 288-295 ◽  
Author(s):  
Mahnaz Kazemipoor ◽  
Mehdi Rezaeian ◽  
Maryam Kazemipoor ◽  
Sareena Hamzah ◽  
Shishir Kumar Shandilya
Keyword(s):  
Female Athletes ◽  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
Hybrid Approach ◽  
Anthropometric Indices ◽  
Thigh Circumference ◽  
Inference System ◽  
Natural Medicine ◽  
Novel Approach ◽  
Fuzzy Techniques

Background: Physical characteristics including body size and configuration, are considered as one of the key influences on the optimum performance in athletes. Despite several analyzing methods for modeling the slimming estimation in terms of reduction in anthropometric indices, there are still weaknesses of these models such as being very demanding including time taken for analysis and accuracy. Objective: This research proposes a novel approach for determining the slimming effect of a herbal composition as a natural medicine for weight loss. Methods: To build an effective prediction model, a modern hybrid approach, merging adaptivenetwork- based fuzzy inference system and particle swarm optimization (ANFIS-PSO) was constructed for prediction of changes in anthropometric indices including waist circumference, waist to hip ratio, thigh circumference and mid-upper arm circumference, on female athletes after consumption of caraway extract during ninety days clinical trial. Results: The outcomes showed that caraway extract intake was effective on lowering all anthropometric indices in female athletes after ninety days trial. The results of analysis by ANFIS-PSO was more accurate compared to SPSS. Also, the efficiency of the proposed approach was confirmed using the existing data. Conclusion: It is concluded that a development in predictive accuracy and simplification capability could be attained by hybrid adaptive neuro-fuzzy techniques as modern approaches in detecting changes in body characteristics. These developed techniques could be more useful and valid than other conventional analytical methods for clinical applications.

Nonlinear System Identification of Smart Buildings

Fuzzy Systems ◽  
2017 ◽  
pp. 1183-1202
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

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