scholarly journals Selection and Configuration of Sorption Isotherm Models in Soils Using Artificial Bees Guided by the Particle Swarm

2017 ◽  
Vol 2017 ◽  
pp. 1-22
Author(s):  
Tadikonda Venkata Bharat
Keyword(s):  
Parameter Estimation ◽  
Sorption Isotherms ◽  
Initial Guess ◽  
Performance Comparison ◽  
Measured Data ◽  
Least Square ◽  
Isotherm Model ◽  
Isotherm Models ◽  
Model Parameters ◽  
Local Optima

A precise estimation of isotherm model parameters and selection of isotherms from the measured data are essential for the fate and transport of toxic contaminants in the environment. Nonlinear least-square techniques are widely used for fitting the isotherm model on the experimental data. However, such conventional techniques pose several limitations in the parameter estimation and the choice of appropriate isotherm model as shown in this paper. It is demonstrated in the present work that the classical deterministic techniques are sensitive to the initial guess and thus the performance is impeded by the presence of local optima. A novel solver based on modified artificial bee-colony (MABC) algorithm is proposed in this work for the selection and configuration of appropriate sorption isotherms. The performance of the proposed solver is compared with the other three solvers based on swarm intelligence for model parameter estimation using measured data from 21 soils. Performance comparison of developed solvers on the measured data reveals that the proposed solver demonstrates excellent convergence capabilities due to the superior exploration-exploitation abilities. The estimated solutions by the proposed solver are almost identical to the mean fitness values obtained over 20 independent runs. The advantages of the proposed solver are presented.

Thermodynamic Studies for Adsorption of Cd2+ and Pb2+ from Waster Water on Modified Walnut Shell

2015 ◽  
Vol 737 ◽  
pp. 622-626
Author(s):  
Shao Hua He ◽  
Dan Wang ◽  
Qing Qiu Kong ◽  
Xi Wu
Keyword(s):  
Freundlich Isotherm ◽  
Isotherm Model ◽  
Walnut Shell ◽  
Isotherm Models ◽  
Model Parameters ◽  
Maximum Adsorption Capacity ◽  
Adsorption Data ◽  
Batch Technique ◽  
Adsorption Processes ◽  
Langmuir Isotherm Model

The adsorption isothermal curve and thermodynamic adsorption of Cd2+ and Pb2+ on modified walnut shell from waster water were investigated using batch technique. The equilibrium adsorption data are fitted to Langmuir and Freundlich isotherm models and the model parameters are evaluated. The Langmuir isotherm model shows a better fit to adsorption data than the Freundlich isotherm model for the sorption of Cd2+ and Pb2+ on modified walnut shell. The maximum adsorption capacity of Cd2+ and Pb2+ by modified walnut shell is found to be 32.68 mg·g-1 and 84.75 mg·g-1 at 298K temperature, respectively. The adsorption processes of Cd2+ and Pb2+ has feasibility and spontaneous nature. Thermodynamic parameters depict the endothermic nature of sorption and the process is spontaneous and favorable.

Thermal Error Compensation and Modeling for CNC Boring and Milling Center

2014 ◽  
Vol 945-949 ◽  
pp. 1665-1668
Author(s):  
Jun Sun ◽  
Yan Ping Xu ◽  
Xing Liu
Keyword(s):  
Error Compensation ◽  
Structural Characteristics ◽  
Thermal Error ◽  
Least Square Method ◽  
Measured Data ◽  
Least Square ◽  
Model Parameters ◽  
Heat Sources ◽  
Thermal Error Model ◽  
Thermal Error Compensation

The thermal error compensation and modeling is an effective way to improve the machining tool precision. This article took the CNC boring and milling center for example. Firstly, this article made analysis and research on the heat sources by analyzing the structural characteristics of CNC boring and milling center, and then on the basis of the previously measured data of selected critical temperature points, established the thermal error model using a method of multiple linear regression based on least square method. The model parameters were solved by MATLAB software. Finally, thermal error compensation model was tested.

A Simple Parameter Estimation Approach to Modeling of Photovoltaic Modules Based on Datasheet Values

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Xiangsai Feng ◽  
Xiangyun Qing ◽  
C. Y. Chung ◽  
Hongqiao Qiao ◽  
Xunchun Wang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Standard Test ◽  
Least Square ◽  
Model Parameters ◽  
Electrical Model ◽  
Characteristic Model ◽  
Test Conditions ◽  
Two Parameters ◽  
Simple Parameter

This work presents a simple parameter estimation approach for a photovoltaic (PV) module using a single-diode five-parameter electrical model. The proposed approach only uses the information from manufacturer datasheet without requiring a specific experimental procedure or a curve extractor. The number of parameters to be determined is first reduced from five to two by gaining insight into electrical equations of the model at the standard test conditions (STCs). A nonlinear least square (NLS) objective function is then constructed and minimized by a complete scan for all possible values of the two parameters within some specific ranges based on their physical meaning. Consequently, the single-diode five-parameter electrical model at the STC is determined based on two optimal parameter values. Besides, a PV full characteristic model with consideration of both the irradiance and temperature dependencies is also constructed by using the data at the nominal operating cell temperature (NOCT) test conditions. The proposed approach is easy to implement and free of the convergence problem. The evaluations on several PV modules show that the proposed approach is capable of extracting accurate estimates of the model parameters.

Sorption Isotherms of Granny Smith Apples Hot-Air Dried and Texturized by "Controlled Sudden Decompression to the Vacuum"

2007 ◽  
Vol 3 (5) ◽  
Author(s):  
Tarik Iguedjtal ◽  
Nicolas Louka ◽  
Karim Allaf
Keyword(s):  
Surface Area ◽  
Goodness Of Fit ◽  
Gravimetric Method ◽  
Sorption Isotherms ◽  
Moisture Sorption ◽  
Least Square ◽  
Isotherm Models ◽  
Sorption Data ◽  
Sorption Behaviour ◽  
Hot Air

Moisture sorption isotherms of Granny Smith apples hot-air dried and texturized by Controlled Sudden Decompression (Détente Instantannée Contrôlée DIC®) were determined and compared using a gravimetric method. The DIC has been developed to confer a porous structure to partially dehydrated foods by expanding them and facilitating the drying process at lower water content. The samples were stored in a chamber; the relative humidity is controlled by an atomizing humidifier at 20, 30 and 40°C, and relative humidities ranging from 10% to 90%. The sorption capacity decreased with increasing temperature at a given water activity. The hysteresis effect was not significant for both of the dried and texturized apples. The experimental sorption data were fitted to 8 various isotherm models including two parameter relationships (BET, Halsey, Smith, Henderson, Oswin), three parameter equations (Ferro-Fontan, GAB) and four parameter equations (Peleg). A non-linear least square regression software was used to evaluate the model's constants. The goodness of fit of each isotherm was quantified through the mean relative percentage deviation modulus E. The Ferro-Fontan, Peleg, GAB and Oswin equations were best for characterizing the sorption behaviour of Granny Smith apples for a whole range of temperatures and water activities studied. The surface area corresponding to the monolayer was determined for the texturized apples and compared to the dried samples. The results showed that the treatment by DIC increases the surface area of apples. For understanding the water properties and calculating the energy requirements phenomena, net isosteric heat was evaluated by the applying Clausius-Clapeyron equation.

Competitive adsorption of organic solvents using modified and unmodified calcium bentonite clay mineral

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Adsorption Isotherm ◽  
Adsorption Capacity ◽  
Organic Solvents ◽  
Competitive Adsorption ◽  
Correlation Factor ◽  
Isotherm Model ◽  
Isotherm Models ◽  
High Concentration ◽  
Dual Purpose ◽  
Calcium Bentonite

Dodecyltrimethylammonium bromide (DTAB)–modified and unmodified calcium bentonite were both used for the competitive adsorption of aromatics (xylene, ethylbenzene and toluene) and petroleum products (gasoline, dual purpose kerosene and diesel) from their aqueous solution. Infrared spectroscopy (IR) and expansion tests (adsorption capacity and Foster swelling) measurement were performed in order to evaluate the performance of the adsorbents. The Foster swelling index and adsorption capacity of the DTAB modified calcium bentonite in the organic solvents follow the trend: xylene > ethylbenzene > toluene > gasoline > dual purpose kerosene (DPK) > diesel > water. However, the adsorption capacity of the adsorbent in diesel outweighed the adsorption capacity in DPK at high concentration of DTAB indicating that diesel has higher affinity for high DTAB concentration than DPK. The percentage removal of the solvent is directly proportional to the concentration of DTAB used in modifying the bentonite as well as the contact time between the adsorbent and the solvent, hence modified calcium bentonite adsorbed a higher percentage of organic solvents than the unmodified calcium bentonite. The adsorption characteristics of both adsorbents improved remarkably after proper agitation of the organic solvents, the unmodified calcium bentonite however adsorbed more water than the modified bentonite. Data obtained from adsorption isotherm models confirms that Freundlich adsorption isotherm model was favored more than Langmuir adsorption isotherm model with the correlation factor (R2) of the former tending more towards unity. The adsorption of ethylbenzene using DTAB modified and unmodified calcium bentonites follow a pseudo second order kinetics mechanism, suggesting that the rate determining step of adsorption involves both the adsorbent and the organic solvent.

scholarly journals Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 387
Author(s):  
Yiting Liang ◽  
Yuanhua Zhang ◽  
Yonggang Li
Keyword(s):  
Parameter Estimation ◽  
Kinetic Model ◽  
Model Parameters ◽  
Hydrogen Ions ◽  
Removal Process ◽  
Cobalt Removal ◽  
Estimation Scheme ◽  
Zinc Hydrometallurgy ◽  
The Impact ◽  
Parameter Estimation Scheme

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Causation Entropy Identifies Sparsity Structure for Parameter Estimation of Dynamic Systems

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Pileun Kim ◽  
Jonathan Rogers ◽  
Jie Sun ◽  
Erik Bollt
Keyword(s):  
Parameter Estimation ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Measurement Data ◽  
Relative Magnitude ◽  
Initial Guess ◽  
Series Data ◽  
System Parameters ◽  
Estimation Problems ◽  
System States

Parameter estimation is an important topic in the field of system identification. This paper explores the role of a new information theory measure of data dependency in parameter estimation problems. Causation entropy is a recently proposed information-theoretic measure of influence between components of multivariate time series data. Because causation entropy measures the influence of one dataset upon another, it is naturally related to the parameters of a dynamical system. In this paper, it is shown that by numerically estimating causation entropy from the outputs of a dynamic system, it is possible to uncover the internal parametric structure of the system and thus establish the relative magnitude of system parameters. In the simple case of linear systems subject to Gaussian uncertainty, it is first shown that causation entropy can be represented in closed form as the logarithm of a rational function of system parameters. For more general systems, a causation entropy estimator is proposed, which allows causation entropy to be numerically estimated from measurement data. Results are provided for discrete linear and nonlinear systems, thus showing that numerical estimates of causation entropy can be used to identify the dependencies between system states directly from output data. Causation entropy estimates can therefore be used to inform parameter estimation by reducing the size of the parameter set or to generate a more accurate initial guess for subsequent parameter optimization.

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