Bayesian inference for parameters estimation using experimental data

2020 ◽  
Vol 60 ◽  
pp. 103025 ◽  
Author(s):  
Chiara Pepi ◽  
Massimiliano Gioffrè ◽  
Mircea Grigoriu
2005 ◽  
Vol 52 (1-2) ◽  
pp. 419-426 ◽  
Author(s):  
C.A. Aceves-Lara ◽  
E. Aguilar-Garnica ◽  
V. Alcaraz-González ◽  
O. González-Reynoso ◽  
J.P. Steyer ◽  
...  

In this work, an optimization method is implemented in an anaerobic digestion model to estimate its kinetic parameters and yield coefficients. This method combines the use of advanced state estimation schemes and powerful nonlinear programming techniques to yield fast and accurate estimates of the aforementioned parameters. In this method, we first implement an asymptotic observer to provide estimates of the non-measured variables (such as biomass concentration) and good guesses for the initial conditions of the parameter estimation algorithm. These results are then used by the successive quadratic programming (SQP) technique to calculate the kinetic parameters and yield coefficients of the anaerobic digestion process. The model, provided with the estimated parameters, is tested with experimental data from a pilot-scale fixed bed reactor treating raw industrial wine distillery wastewater. It is shown that SQP reaches a fast and accurate estimation of the kinetic parameters despite highly noise corrupted experimental data and time varying inputs variables. A statistical analysis is also performed to validate the combined estimation method. Finally, a comparison between the proposed method and the traditional Marquardt technique shows that both yield similar results; however, the calculation time of the traditional technique is considerable higher than that of the proposed method.


Biomolecules ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 308
Author(s):  
Hamid Zentou ◽  
Zurina Zainal Abidin ◽  
Robiah Yunus ◽  
Dayang Awang Biak ◽  
Mustapha Zouanti ◽  
...  

Modelling has recently become a key tool to promote the bioethanol industry and to optimise the fermentation process to be easily integrated into the industrial sector. In this context, this study aims at investigating the applicability of two mathematical models (Andrews and Monod) for molasses fermentation. The kinetics parameters for Monod and Andrews were estimated from experimental data using Matlab and OriginLab software. The models were simulated and compared with another set of experimental data that was not used for parameters’ estimation. The results of modelling showed that μmax = 0.179 1/h and Ks = 11.37 g.L−1 for the Monod model, whereas μmax = 0.508 1/h, Ks = 47.53 g.L−1 and Ki = 181.01 g.L−1 for the Andrews model, which are too close to the values reported in previous studies. The validation of both models showed that the Monod model is more suitable for batch fermentation modelling at a low concentration, where the highest R squared was observed at S0 = 75 g.L−1 with an R squared equal to 0.99956, 0.99954, and 0.99859 for the biomass, substrate, and product concentrations, respectively. In contrast, the Andrews model was more accurate at a high initial substrate concentration and the model data showed a good agreement compared to the experimental data of batch fermentation at S0 = 225 g.L−1, which was reflected in a high R squared with values 0.99795, 0.99903, and 0.99962 for the biomass, substrate, and product concentrations respectively.


Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3383
Author(s):  
Uzair Sajjad ◽  
Imtiyaz Hussain ◽  
Muhammad Imran ◽  
Muhammad Sultan ◽  
Chi-Chuan Wang ◽  
...  

The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling of nanoporous surfaces with different working fluids with completely different thermophysical properties. The proposed deep learning method is applicable to a wide variety of substrates and coating materials manufactured by various manufacturing processes. The analysis of the correlation matrix confirms that the pore diameter, the thermal conductivity of the substrate, the heat flow, and the thermophysical properties of the working fluids are the most important independent variable parameters estimation under consideration. Several deep neural networks are designed and evaluated to find the optimized model with respect to its prediction accuracy using experimental data (1042 points). The best model could assess the HTC with an R2 = 0.998 and (mean absolute error) MAE% = 1.94.


2017 ◽  
Vol 18 (1) ◽  
pp. 127 ◽  
Author(s):  
Marcia De Fatima Brondani ◽  
Airam Teresa Zago Romcy Sausen ◽  
Paulo Sérgio Sausen ◽  
Manuel Osório Binelo

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.


2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
B. F. Silva ◽  
P. S. Sausen ◽  
A. Sausen

This paper presents a proposition of a new optimization method called Search in Improved Network, which is an extension of the method Search in Modified Network, to calculate the empirical parameters of the Rakhmatov and Vrudhula model for predicting batteries lifetime used in mobile devices. The new method is evaluated according to the following methodology. At first empirical parameters are computed considering the optimization methods Search in Improved Network, Search in Modified Network, and Least Squares, as well as the experimental data obtained from a testbed, considering a Lhition-Ion battery, model BL5F, used in the Nokia N95 cell phone. In a second moment, the Rakhmatov and Vrudhula model is validated for each set of parameters obtained, and the simulated data from the model are compared with a set of experimental data. From simulations results a comparative analysis is performed and it is found that by the application of the method Search in Improved Network in the parameters estimation of the Rakhmatov and Vrudhula model it is possible to obtain an easy and intuitive implementation, improving the results obtained in the model accuracy, as well as preserving the runtime.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 618
Author(s):  
Sergey Kucheryavskiy ◽  
Alexander Egorov ◽  
Victor Polyakov

Eddy current (EC) measurements, widely used for diagnostics of conductive materials, are highly dependent on physical properties and geometry of a sample as well as on a design of an EC-sensor. For a sensor of a given design, the conductivity and thickness of a sample as well as the gap between the sample and the sensor (lift-off) are the most influencing parameters. Estimation of these parameters, based on signals acquired from the sensor, is quite complicated in case when all three parameters are unknown and may vary. In this paper, we propose a machine learning based approach for solving this problem. The approach makes it possible to avoid time and resource-consuming computations and does not require experimental data for training of the prediction models. The approach was tested using independent sets of measurements from both simulated and real experimental data.


Sign in / Sign up

Export Citation Format

Share Document