A Linux cluster of personal computers for the numerical simulation of natural airflows in greenhouses using a lattice model

Some numerical models need a considerable computational effort to run their simulations. This fact is translated into long execution times delaying the decisions that have to be taken by the modeller in order to obtain the best phenomena description. This situation is also frequent in agronomical modelling, particularly when the subject of study is the fluid flow pattern as happens when simulating the natural airflows in a greenhouse. The knowledge of the effects of different ventilator configurations allows the crop manager to improve the greenhouse's natural ventilation conditions. In this work, a Linux cluster of ten personal computers (PC) is proposed with the aim of reducing the execution time of the lattice model simulations by means of parallel computing. This model has been used for describing fluid flow patterns in the presence of solid obstacles since the 1990s. The lattice model structure makes them suitable for a straightforward parallel computing implementation. As Jiménez-Hornero et al. (2005) show, this model is suitable for simulating the two-dimensional natural airflow in the vertical cross-section of a tropical crop protection structure described by Montero et al. (2001). The performance metrics clearly show the benefit of using the proposed Linux PC cluster in terms of execution time reduction and speedup with respect to the sequential running in a single PC

Control of a PEM fuel cell based on maximum power tracking using radial basis function neural networks

This article presents the proposal of a two-level control approach for a type of commercial PEM fuel cell. Thus, in the external control level a model based on neural networks of the FC is used together with a tracking algorithm to follow the maximum efficiency points as a function of the oxygen excess and in the internal level, a PI control strategy is used to guarantee the compressor motor voltage that satisfies the oxygen excess ratio demanded. The neural model of the FC response is developed through the steady-state FC response provided by the physical modelling using a multimodel approach. This approach allows a good relation between the computational cost of the training and the performance that the network offers. The performance of the global controller and the tracking algorithm are evaluated for variable load conditions by simulations and conclusions are drawn

An educational computer tool for simulating long-term soil erosion on agricultural landscapes

Due to its economic and environmental impacts, soil erosion has been a major concern to farmers, engineers and policy makers in recent years. Water and tilling are two of the main agents responsible for this phenomenon and considerable efforts have been made to model them in previous work but not with educational purposes. A computer tool for facilitating any user’s simulation of long-term landscape evolution in a plot due to the combined action of water and tillage erosion is presented here. It integrates a graphic user interface with two well-verified erosion models, each one independently devoted to reproduce the effects of water and tilling. This computer tool permits to the student the consideration of the erosivity index and the presence of a crop in the plot, when simulating water erosion, as well as the planning of a different type of tilling each year. Each kind of tilling corresponds to a different combination of tillage tools with their own date, tillage depth and tillage direction. A handy ASCII (XYZ) file is generated containing the long-term soil erosion spatial pattern as result. From this information, the student can derive other results that will help to understand soil erosion. An example is presented here with the aim of showing how to use this computer tool to simulate this phenomenon on an agricultural landscape with a complex topography

Design, Implementation and Validation of a Hardware-in-the-Loop Test Bench for Heating Systems in Conventional Coaches

Experimental work with heating systems installed in public transport vehicles, particularly for optimisation and control design, is a challenging task due to cost and space limitations, primarily imposed by the heating hardware and the need to have a real vehicle available. In this work, a hybrid hardware-in-the-loop (HIL) test bench for heating systems in conventional coaches is introduced. This approach consists of a hardware system made up of the main heating components, assembled as a lab experimental plant, along with a simulation component including a cabin thermal model, both exchanging real-time data using a standard communication protocol. This scheme presents great flexibility regarding data logging for further analysis and easily changing the experimental operational conditions and disturbances under different scenarios (i.e., solar irradiance, outside temperature, water temperature from the engine cooling circuit, number of passengers, etc.). Comparisons between the hybrid system’s transient and steady-state responses and those from selected experiments conducted on an actual coach allowed us to conclude that the proposed system is a suitable test bed to aid in optimisation and design tasks. In this context, several closed-loop test experiments using the test bench were additionally carried out to assess the performance of the proposed control system

Design of event-based PI-P controllers using interactive tools

In the field of event-based control, the tuning and synthesis of controllers represents a challenging task where the lack of specific computer-aided design tools makes very difficult the consolidation of emerging approaches. Under this scenario, it was recently proposed the PI-P control strategy, a promising alternative which provides the designer with a practical design and an intuitive tuning methodology. This work addresses the design task of PI-P event-based controllers through the use of interactive tools. To this aim, firstly, a new interactive tool is provided. The tool is the result of implementing the theoretical developments of previous researches with the aim of consolidating the strategy. Secondly, the tool is used to analyze in detail the design paradigm through their main trade-offs between performance and robustness. The usefulness of analyses developed is experimentally demonstrated through several meaningful designs for typical industrial examples

A practical tuning methodology for event-based PI control

This paper is focused on the tuning of an event-based PI controller for first-order plus time delay systems(FOPTD). In this work, a novel design and combination of a controller and event generator with an easy-to-use tuning methodology is presented. The event generator combines the Smith predictor structurewith the symmetric send-on-delta (SSOD) sampling scheme to compensate the delay and trigger theevents. The controller has an adaptive structure with the purpose of improving the set-point trackingand guaranteeing stability under conditions of uncertainty. The approach is focused on FOPTD systemsbut can be easily extended to higher order systems. Stability and robustness analyses are conducted, andthe experimental results verify the effectiveness of the approach

Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling

Optimization of biotechnological processes. The acetic acid fermentation. Part I: The proposed model

Vinegar is a food product of increasing significance by virtue of its widely variable origin and uses (particularly as a condiment or food preservative). The gastronomic value of vinegar has been appreciated for thousands of years. The growing social and economic significance of these products has fostered research into the most salient aspects of their production processes. The widespread use of submerged cultures in such processes has aroused an obvious interest in their modelling with a view to facilitating their design, control and optimization. Also, the availability of increasingly powerful utility and dedicated software tools has enabled a much rigorous approach to devising and application of more complex and accurate models for these purposes. This paper (Part I) reviews previous attempts at modelling acetic acid fermentation and proposes a new mathematical model for the process based on extensive experimental testing. The model introduces newequations and considers cell lysis during the process. Part II is devoted to study the key subject of parameter estimation and finally Part III deals with the optimization task. Though the wine vinegar process is being considered, many of the studied issues could be applied to other fermentations

Optimization of biotechnological processes. The acetic acid fermentation. Part II: Practical identifiability analysis and parameter estimation

In part I of this series a mathematical model for acetic acid fermentation was reported. However, no kinetic model can be complete until its equation parameters are estimated. This inevitably entails a practical identifiability analysis intended to ascertain whether the parameters can be estimated in an unambiguous manner based not only on the sensitivity of the model to them, but also on the amount and quality of available experimental data for this purpose. Also, estimating the model parameters entails optimizing a specific objective function subject to the model equations as major constraints and to additional, minor constraints on variables and parameters. This approach usually leads to the formulation of a non-linear programming problem involving differential and algebraic constraints where the decision variables constitute the parameter set to be estimated. In the scope of modelling biotechnological processes, this problem is not usually dealt with in a proper way. This second paper reviews available models for practical identifiability assessment and parameter estimation with a viewto their prospective application to the proposed model and its validation