A Monte Carlo modeling methodology for the simulation of hydrotreating processes

Oliveira, Luís Pereira de; Verstraete, J.; Kolb, M.

doi:10.1016/j.cej.2012.05.039

Cited by 47 publications

(35 citation statements)

References 32 publications

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“…As many authors recommend, the Monte Carlo algorithm is used to find the best roots values [61][62][63]. e steps to follow are similar to those described in 2.3.…”

Section: Controller Parameters Selectionmentioning

confidence: 99%

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

Fernández

Pantano

Serrano

et al. 2020

Mathematical Problems in Engineering

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Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.

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“…As many authors recommend, the Monte Carlo algorithm is used to find the best roots values [61][62][63]. e steps to follow are similar to those described in 2.3.…”

Section: Controller Parameters Selectionmentioning

confidence: 99%

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

Fernández

Pantano

Serrano

et al. 2020

Mathematical Problems in Engineering

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“…In this test, the Monte Carlo algorithm is applied to tune the controller. This method is chosen because it has several advantages: i) there is low probability of producing an incorrect result (Motwani and Raghavan, 1995); ii) less computational complexity in comparison with other algorithms (Tempo and Ishii, 2007); iii) often, a slight modification of the algorithm for solving systems of linear algebraic equations allows one to solve other linear algebra problems such as matrix inversion and computing the extreme eigenvalues (Dimov et al, 2015); iv) high reliability and easy application (Cheein and Scaglia, 2014, de Oliveira et al, 2012, Mohammadi et al, 2014.…”

Section: Controller Structurementioning

confidence: 99%

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Fernández¹,

Pantano²,

Rossomando³

et al. 2019

Braz. J. Chem. Eng.

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In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

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“…The aim is not to focus on the model (which is simple regarding a more complex model [10]) but rather to explain sensitivity analysis and which information could be provided by such analysis.…”

Section: Introductionmentioning

confidence: 99%