J.A. López-Orozco

A kinetic model for beer production under industrial operational conditions

A kinetic model for beer production is proposed. The model takes into account five responses: biomass, sugar, ethanol, diacetyl and ethyl acetate. In contrast with previously published models, this model segregates biomass into three components: lag, active and dead cells and considers the active cells as the only fermentation agent. Experiments were first performed at laboratory scale and isothermal runs were carried out at five temperatures (8°C, 12°C, 16°C, 20°C and 24°C). Fitting of experimental data was made by non-linear regression. Parameter values calculated were similar to those given in the literature. The kinetic model was able to fit experimental data with a very good agreement. Afterwards, experiments were conducted at pilot plant scale and runs were now carried out changing temperature with time, in the industrial way. The kinetic model, with the parameter values calculated as a function of temperature, was able to predict with a very high accuracy the non-isothermal experimental data achieved. This model can be used for simulation of the industrial process under different operational conditions and for faults detection. It can also be utilized for the optimization and even for the supervised control of the process and its automatization.

Virtual and remote robotic laboratory using EJS, MATLAB and LabVIEW.

This paper describes the design and implementation of a virtual and remote laboratory based on Easy Java Simulations (EJS) and LabVIEW. The main application of this laboratory is to improve the study of sensors in Mobile Robotics, dealing with the problems that arise on the real world experiments. This laboratory allows the user to work from their homes, tele-operating a real robot that takes measurements from its sensors in order to obtain a map of its environment. In addition, the application allows interacting with a robot simulation (virtual laboratory) or with a real robot (remote laboratory), with the same simple and intuitive graphical user interface in EJS. Thus, students can develop signal processing and control algorithms for the robot in simulation and then deploy them on the real robot for testing purposes. Practical examples of application of the laboratory on the inter-University Master of Systems Engineering and Automatic Control are presented.

Remote Control Laboratory Using EJS Applets and TwinCAT Programmable Logic Controllers

This paper presents a new methodology to develop remote laboratories for systems engineering and automation control courses, based on the combined use of TwinCAT, a laboratory Java server application, and Easy Java Simulations (EJS). The TwinCAT system is used to close the control loop for the selected plants by means of programmable logic controllers (PLCs) deployed in PCs with the TwinCAT run-time tool. EJS is used to develop the laboratory front-end applets that let teachers and students parametrize and observe the behavior of the PLCs from any computer. The laboratory Java server application establishes the connection between the EJS applets and the PLCs, fulfilling the TwinCAT connection requirements while ensuring an individualized access to each PLC. This paper also shows how the practical work in some undergraduate control courses at the Complutense University of Madrid, Spain, already uses the TwinCAT PLC + Java server + EJS applet strategy to provide real-time support to the controllers, remote individualized access to the experiments, and a user-friendly graphic controller interface for the students.

Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms

This paper describes empirical research on the model, optimization and supervisory control of beer fermentation. Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results. The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs). Successful finding of optimal ways to drive these processes were reported. Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

Evolutionary path planner for UAVs in realistic environments

This paper presents a path planner for Unmanned Air Vehicles (UAVs) based on Evolutionary Algorithms (EA) that can be used in realistic risky scenarios. The path returned by the algorithm fulfills and optimizes multiple criteria which (1) are calculated based on properties of real UAVs, terrains, radars and missiles, and (2) are used to rank the solutions according to the priority levels and goals selected for each mission. Developed originally to work with only one UAV, the planner currently allows us to obtain the optimal path of several UAVs that are flying simultaneously. It works globally offline and locally online to recalculate a part of the path when an unexpected threat appears. Finally, the effectiveness of the solutions given by this planner has been successfully tested against a simulator that implements a complex model of the UAV and its environment.

An Asynchronous, Robust, and Distributed Multisensor Fusion System for Mobile Robots

In this paper, a multisensor fusion system that is used for calculating the position and orientation of an autonomous mobile robot is presented. The developed fusion system is distributed, robust, and asynchronous. It is distributed to permit the parallel function of all the sensors. It is robust because, being distributed, the system has been designed to keep working properly in spite of the failure, removal, or change of any sensor. It is asynchronous to take advantage of the different features and rates of each sensor. The implementation of the system is based on the distributed Kalman filter developed by Durrant-Whyte and Rao. In that distributed filter, all the sensors work in parallel to obtain their own estimate based on their own observations and on the observations coming from other sensors. Changes have been made to simplify and speed up the computation of the external validation equations and to allow the use of any sensor model. The equations have also been adapted to deal with asynchronously operating sensors and with the existence of communication delays. The fusion system is used to estimate the position and orientation of a mobile robot. The performance of the fusion system is shown both under simulation and in a real test with a mobile robot.

Multisensor Out of Sequence Data Fusion for Estimating the State of Discrete Control Systems

The fusion center of a complex control system estimates its state with the information provided by different sensors. Physically distributed sensors, communication networks, pre-processing algorithms, multitasking, etc, introduce non-systematic delays in the arrival of information to the fusion center, making the information available out-of-sequence (OOS). For real-time control systems, the state has to be efficiently estimated with all the information received so far. So, several solutions of the OOS problem for dynamic multiple-input multiple-output (MIMO) discrete control systems traditionally solved by the Kalman filter (KF) have been proposed recently. This paper presents two new streamlined algorithms for the linear and non-linear case. IFAsyn, the linear algorithm, is equivalent to other optimal solutions but more general, efficient and easy to implement. EIFAsyn, the nonlinear one, is a new solution of the OOS problem in the extended KF (EKF) framework.

Relaxation labeling in stereo image matching

Abstract This paper outlines a method for solving the global stereovision matching problem using edge segments as the primitives. A relaxation scheme is the technique commonly used by existing methods to solve this problem. These techniques generally impose the following competing constraints: similarity, smoothness, ordering and uniqueness, and assume a bound on the disparity range. The smoothness constraint is basic in the relaxation process. We have verified that the smoothness and ordering constraints can be violated by objects close to the cameras and that the setting of the disparity limit is a serious problem. This problem also arises when repetitive structures appear in the scene (i.e. complex images), where the existing methods produce a high number of failures. We develop our approach from a relaxation labeling method ( [1] W.J. Christmas, J. Kittler, M. Petrou, Structural matching in computer vision using probabilistic relaxation, IEEE Trans. Pattern Anal. Mach. Intell. 17(8) (1995) 749–764), which allows us to map the above constraints. The main contribution is made, (1) by applying a learning strategy in the similarity constraint and (2) by introducing specific conditions to overcome the violation of the smoothness constraint and to avoid the serious problem produced by the required fixation of a disparity limit. Consequently, we improve the stereovision matching process. A better performance of the proposed method is illustrated by comparative analysis against some recent global matching methods.

Multisensor fusion for linear control systems with asynchronous, Out-Of-Sequence and erroneous data

This paper presents a set of new centralized algorithms for estimating the state of linear dynamic Multiple-Input Multiple-Output (MIMO) control systems with asynchronous, non-systematically delayed and corrupted measurements provided by a set of sensors. The delays, which make the data available Out-Of-Sequence (OOS), appear when using physically distributed sensors, communication networks and pre-processing algorithms. The potentially corrupted measurements can be generated by malfunctioning sensors or communication errors. Our algorithms, designed to work with real-time control systems, handle these problems with a streamlined memory and computational efficient reorganization of the basic operations of the Kalman and Information Filters (KF & IF). The two versions designed to deal only with valid measurements are optimal solutions of the OOS problem, while the other two remaining are suboptimal algorithms able to handle corrupted data.

Application of genetic algorithms and simulations for the optimization of batch fermentation control

Batch fermentations are dynamic processes that must be guided along convenient paths to obtain the desired results. Our research deals with the application of computers for advanced control of such processes. We selected beer fermentation, and started to investigate whether it is possible to optimize the process, taking as reference to be improved a real industrial fermentation. A good mathematical model is needed for that, and, as we refer to realistic industrial conditions, we had to develop a new one. Then we started optimization studies, exploring the adaptation of genetic algorithms for our problem. Good results are obtained, furnishing a promising ground for additional improvements. In this paper we describe the process, the new model, the optimization problem, and the solution by genetic algorithms.