Papers by RAMIRO RICO MARTINEZ
IEEE Access
Ohmic heating, also known as electrical resistance heating, is a novel process of pasteurization ... more Ohmic heating, also known as electrical resistance heating, is a novel process of pasteurization that has several advantages including homogeneous heating, low heating time, high inactivation of microorganisms, and organoleptic and nutritional properties. Furthermore, mathematical tools allow for quick and low-cost analysis of behaviors with various operating parameters. This study aimed to improve the ohmic pasteurization process of the mango pulp using computational fluid dynamics (CFD) and response surface methodology (RSM), using the experimental properties of mango pulp. The generated mathematical model is an accurate tool for predicting ohmic pasteurization behavior in an industrial environment; it presents a 4.4% standard deviation error in the experimental data. Furthermore, the arrangement and intensity of the added voltage analysis suggested operating variables with better pasteurization performance. Another observation was that the production speed was related to the voltage intensities of the inlet speed in a non-proportional manner. Additionally, operation at a low inlet velocity may be more susceptible to thermal damage, whereas operation at a high inlet velocity requires a particular proportion of voltage insertion to ensure adequate heat distribution.
Computer-aided chemical engineering, 2017
A generalized mathematical model based on compartments is used to represent economic, ecological ... more A generalized mathematical model based on compartments is used to represent economic, ecological and social elements of a real-world ecosystem. The model is then posed as an optimal control problem by maximizing/minimizing alternative sustainability indicators. Further, we include stochastic processes to represent the time-dependent uncertainties of some of the model parameters. The result is a stochastic optimal control problem; mortality and birth rates are assumed as stochastic parameters. The aim of this paper is solving the optimal control problem to derive time-dependent socio-economic policies which result in optimal values for the performance indicators (used as measures of sustainability). A numerical technique based on the BONUS algorithm is used for solving the large-scale dynamic optimization model. The case-study shows the benefits of applying the proposed methodology and illustrates the impact of human activities on the ecosystem.
Computer Aided Chemical Engineering, 2010
... 10, 805-809 [9]A. Sitz and U. Schwarz and J. Kurths and HU Voss, Estimation of parameters and... more ... 10, 805-809 [9]A. Sitz and U. Schwarz and J. Kurths and HU Voss, Estimation of parameters and unobserved components for nonlinear systems from noisy time series, Physical Review E, 2002, 66, 01621.1-0162210.9 [10] R. Rico-Mart nez, JS Anderson and IG Kevrekidis, 1994 ...
Mathematics
A conventional approach to solving stochastic optimal control problems with time-dependent uncert... more A conventional approach to solving stochastic optimal control problems with time-dependent uncertainties involves the use of the stochastic maximum principle (SMP) technique. For large-scale problems, however, such an algorithm frequently leads to convergence complexities when solving the two-point boundary value problem resulting from the optimality conditions. An alternative approach consists of using continuous random variables to capture uncertainty through sampling-based methods embedded within an optimization strategy for the decision variables; such a technique may also fail due to the computational intensity involved in excessive model calculations for evaluating the objective function and its derivatives for each sample. This paper presents a new approach to solving stochastic optimal control problems with time-dependent uncertainties based on BONUS (Better Optimization algorithm for Nonlinear Uncertain Systems). The BONUS has been used successfully for non-linear programmi...
Journal of the Serbian Chemical Society
ABSTRACT
ABSTRACT Se desarrolló un modelo cinético que describe la generación de metanol durante el cocimi... more ABSTRACT Se desarrolló un modelo cinético que describe la generación de metanol durante el cocimiento de piñas de Agave angustifolia en el proceso de producción de mezcal. Los cocimientos se realizaron en una autoclave piloto a dos temperaturas: 102 y 112°C por 18 y 12 horas respectivamente. Para este modelo se determinó las constantes de la velocidad, así como las energías de activación y los factores preexponenciales de la ecuación de Arrhenius. Se encontró que el modelo ajusta adecuadamente los datos experimentales. Las energías de activación estimadas para la formación de metanol en hoja y tallo son 43 y 49 kJ/mol, sustancialmente menores a la energía de activación de las reacciones de generación de azúcares simples por cocimiento de agave, por lo que resulta improbable encontrar una combinación de tiempo y temperatura donde se inhiba la producción de metanol sin perder eficiencia en la liberación de carbohidratos fermentables.
Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328), 1999
In this study system parameters are adaptively varied to identify bifurcations from equilibrium i... more In this study system parameters are adaptively varied to identify bifurcations from equilibrium in simulations of a reduced-order model for turbomachinery aeromechanics. An element of the adaptive process is identification of a low-order locally nonlinear discrete-time map from the observers. The nonlinear behavior of the system in the neighborhood of the bifurcation is then estimated from the identified system
Proceedings of IEEE Workshop on Neural Networks for Signal Processing, 1994
R. Rico-Martinez, J. S. Anderson and I. G. Kevrekidis Department of Chemical Engineering, Princet... more R. Rico-Martinez, J. S. Anderson and I. G. Kevrekidis Department of Chemical Engineering, Princeton University, Princeton NJ 08544 ... Abstract Artificial neural networks (ANNs) are often used for short term dis-crete time series predictions. Continuous-time models are, however, re- ...
Chemical Engineering Science, 2014
ABSTRACT This second paper of our series is concerned with the formulation and solution strategie... more ABSTRACT This second paper of our series is concerned with the formulation and solution strategies of fractional optimal control problems (FOCP). Given the sets of fractional differential equations representing the behavior of fermentation and thermal hydrolysis reactive systems, here we formulate the corresponding FOCP’s and describe suitable techniques for solving them. An analytical/numerical strategy that combines the optimality conditions and the gradient method for FOCP as well as the predictor–corrector fractional integrator is used to obtain optimal dilution rate profiles for the fermentation case-study. For the case of the thermal hydrolysis, the strategy involves discretization of the FOCP to formulate it as a Non-Linear Programming problem; then, the solution algorithm involves the use of an NLP solver and the shooting technique coupled to an inverse Laplace transformation subroutine. The optimal profiles show the performance of the numerical solution approaches proposed and the effect of the fractional orders in the optimal results.
We present a method of constructing low-dimensional nonlinear models de- scribing the main dynami... more We present a method of constructing low-dimensional nonlinear models de- scribing the main dynamical features of a discrete 2D cellular fault zone, with many degrees of freedom, embedded in a 3D elastic solid. The fault system contains a vertical planar fault with a uniform grid of cells where slip is governed by a static/kinetic friction law surrounded by regions where a uniform slip rate is prescribed to represent the tectonic loading. Quasi- static stress transfer and tectonic loading along the fault are calculated using 3D elastic dislocation theory. A given fault system is characterized by a set of parameters that describe the dynamics, rheology, property disorder and fault geometry. Depending on the location in the system parameter space, we show that the coarse dynamics of the fault is confined to an attractor whose dimension is significantly smaller than the space in which the dynamics takes place. Our strategy of system reduction is to search for a few coherent structures t...
Pure and Applied Geophysics, 2004
We present a method of constructing low-dimensional nonlinear models describing the main dynamica... more We present a method of constructing low-dimensional nonlinear models describing the main dynamical features of a discrete 2D cellular fault zone, with many degrees of freedom, embedded in a 3D elastic solid. A given fault system is characterized by a set of parameters that describe the dynamics, rheology, property disorder, and fault geometry. Depending on the location in the system parameter space we show that the coarse dynamics of the fault can be confined to an attractor whose dimension is significantly smaller than the space in which the dynamics takes place. Our strategy of system reduction is to search for a few coherent structures that dominate the dynamics and to capture the interaction between these coherent structures. The identification of the basic interacting structures is obtained by applying the Proper Orthogonal Decomposition (POD) to the surface deformations fields that accompany strike-slip faulting accumulated over equal time intervals. We use a feed-forward artificial neural network (ANN) architecture for the identification of the system dynamics projected onto the subspace (model space) spanned by the most energetic coherent structures. The ANN is trained using a standard back-propagation algorithm to predict (map) the values of the observed model state at a future time given the observed model state at the present time. This ANN provides an approximate, large scale, dynamical model for the fault. The map can be evaluated once to provide short term predictions or iterated to obtain prediction for the long term fault dynamics.
Journal of Food Lipids, 2002
Experimental measurements of the variation in the solid fraction during crystallization of lipid ... more Experimental measurements of the variation in the solid fraction during crystallization of lipid mixtures are ofren correlated in terms of the so-called Avrami model. In this paper, the above model was employed to describe measurements taken during the crystallization of blends of tripalmitin in olive oil at high concentrations. Although the blends appeared to behave ideally, the Avrami model failed to describe the experimental results over the entire range of tripalmitin concentration investigated. This discrepancy appears to be correlated with the interfacial free energy. As an alternative to the description of lipid crystallization experiments, the use of continuous-time artificial neural network (ANN) approximators isproposed. For the system studied here, the ANN successfully reproduced the experimentally observed behavior for all temperatures and tripalmitin concentrations used.
Electrochimica Acta, 2013
ABSTRACT We report the spontaneous formation of spatial reactivity patterns during the oxidation ... more ABSTRACT We report the spontaneous formation of spatial reactivity patterns during the oxidation of H-2/CO mixtures in diluted perchloric acid on a rotating Pt ring-electrode. Under potentiostatic conditions, the system is oscillatory. At the onset of oscillations at low applied voltage, regular spatio-temporal structures form. At intermediate and high applied voltages spatio-temporal chaos is observed, which exhibits an increasing number of high reactivity spikes with increasing voltage. The transition between the regular and chaotic regime is rather complex and involves period-doubled states. Under galvanostatic conditions, a similar spatio-temporal scenario is observed as a function of applied current.
Computers & Chemical Engineering, 2000
We present and discuss an inherent shortcoming of neural networks used as discrete-time models in... more We present and discuss an inherent shortcoming of neural networks used as discrete-time models in system identification, time series processing, and prediction. Trajectories of nonlinear ordinary differential equations (ODEs) can, under reasonable assumptions, be integrated uniquely backward in time. Discrete-time neural network mappings derived from time series, on the other hand, can give rise to multiple trajectories when followed backward in time: they are in principle nonin6ertible. This fundamental difference can lead to model predictions that are not only slightly quantitatively different, but qualitatively inconsistent with continuous time series. We discuss how noninvertibility arises, present key analytical concepts and some of its phenomenology. Using two illustrative examples (one experimental and one computational), we demonstrate when noninvertibility becomes an important factor in the validity of artificial neural network (ANN) predictions, and show some of the overall complexity of the predicted pathological dynamical behavior. These concepts can be used to probe the validity of ANN time series models, as well as provide guidelines for the acquisition of additional training data.
Computers & Chemical Engineering, 1998
ABSTRACT
Chemical Engineering Communications
... DISCRETE- vs. CONTINUOUS-TIME NONLINEAR SIGNAL PROCESSING OF ce ELECTRODISSOLUTION DATA R. RI... more ... DISCRETE- vs. CONTINUOUS-TIME NONLINEAR SIGNAL PROCESSING OF ce ELECTRODISSOLUTION DATA R. RICO-MARTiNEZ, K. KRISCHER and IG KEVREKIDIS ...
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Papers by RAMIRO RICO MARTINEZ