Measurement of the moisture content of the granulated sugar by infrared transphotometry

The moisture content of granulated sugar is a critical parameter for its transformation into cubes. To the best of our knowledge there is no easy-to-use method for the determination of this parameter. Toresolve this, a new method using infrared transphotometry technique based on the attenuation of an infrared radiation through a sample of sugar, was developed and tested in our laboratory. Using linear regression analysis it was observed that the transphotometer response varies linearly (r2 > 0.996) with the moisture content of sugar. The results obtained by this new method compares very well (ANOVA, p=5%) with other known classical, but laborious and expensive methods of moisture determination

Simulations Based on Experimental Data of the Behaviour of a Monocrystalline Silicon Photovoltaic Module

International audienceThe performance of monocrystalline silicon cells depends widely on the parameters like the series and shunt resistances, the diode reverse saturation current, and the ideality factor. Many authors consider these parameters as constant while others determine their values based on the I-V characteristic when the module is under illumination or in the dark. This paper presents a new method for extracting the series resistance, the diode reverse saturation current, and the ideality factor. The proposed extraction method using the least square method is based on the fitting of experimental data recorded in 2014 in Ngaoundere, Cameroon. The results show that the ideality factor can be considered as constant and equal to 1.2 for the monocrystalline silicon module. The diode reverse saturation current depends only on the temperature. And the series resistance decreases when the irradiance increases. The extracted values of these parameters contribute to the best modeling of a photovoltaic module which can help in the accurate extraction of the maximum power

Comparative study of the reliability of MPPT algorithms for the crystalline silicon photovoltaic modules in variable weather conditions

AbstractThe crystalline silicon photovoltaic modules are widely used as power supply sources in the tropical areas where the weather conditions change abruptly. Fortunately, many MPPT algorithms are implemented to improve their performance. In the other hand, it is well known that these power supply sources are nonlinear dipoles and so, their intrinsic parameters may vary with the irradiance and the temperature. In this paper, the MPPT algorithms widely used, i.e. Perturb and Observe (P&O), Incremental Conductance (INC), Hill-Climbing (HC), are implemented using Matlab®/Simulink® model of a crystalline silicon photovoltaic module whose intrinsic parameters were extracted by fitting the I(V) characteristic to experimental points. Comparing the simulation results, it is obvious that the variable step size INC algorithm has the best reliability than both HC and P&O algorithms for the near to real Simulink® model of photovoltaic modules. With a 60Wp photovoltaic module, the daily maximum power reaches 50.76W against 34.40W when the photovoltaic parameters are fixed. Meanwhile, the daily average energy is 263Wh/day against 195Wh/day

Séparation des états du graphe de marquages d'un réseau de Petri pour la commande par supervision des systèmes à événements discrets

The markings graph is usually large so that we can identify in the simple and efficient way the suitable set of states for control synthesis of discrete event systems. Moreover, the combinatorial explosion problem inherent in control theory is present in the methods of synthesis based on the marking graph. Among these methods, the method of invariants markings which is the most used cannot guarantee the optimality of the results if and only if the suitable set of linear constraints links to forbidden states or markings supplied to it. To find such a suitable set of constraints, the state space of the markings graph of Petri net modeling the discrete event system must be separated. This paper presents an approach of separation of accessible markings graph into sets of forbidden states and allowed states. The markings graph is represented by his codified transition function matrix. This separation is defined by a decision function that characterizes the set of border or criticism states. This set constitutes the hyperplane separation that can be used to determine bijectively admissible constraints necessary for the synthesis of supervision by the method of invariants markings.Le graphe de marquages est généralement de taille importante pour que l'on puisse identifier de manière simple et efficace l'ensemble des états adéquats pour la synthèse de supervision des systèmes à événements discrets. En outre, le problème d'explosion combinatoire inhérent à la théorie de supervision affecte les méthodes de synthèse s'appuyant sur le graphe de marquages. Parmi ces méthodes, la méthode des invariants de marquages qui est la plus utilisée ne peut garantir l'optimalité des résultats que si l'ensemble adéquat des contraintes linéaires liées aux états ou marquages interdits lui est fourni. Pour trouver un tel ensemble adéquat de contraintes, l'espace d'états du graphe de marquages du réseau de Petri modélisant le système à événements discrets doit être séparé. Cet article présente une approche de séparation des ensembles d'états interdits et d'états autorisés du graphe de marquages accessibles, représenté par sa matrice de fonction de transition codifiée. Cette séparation est définie par une fonction de décision qui caractérise l'ensemble des états-frontières ou critiques. Cet ensemble constitue l'hyperplan de séparation qui peut être utilisé pour déterminer de manière bijective les contraintes admissibles nécessaires à la synthèse de supervision par la méthode des invariants de marquages

Classification of Pepper Seeds by Machine Learning Using Color Filter Array Images

The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874

Management of Hybrid Renewable Energy Systems Using Differential Hybrid Petri Nets

Investigations in this paper concern management of Hybrid Renewable Energy systems. To achieve it, a supervisory system based on hybrid systems concept is designed, in order to ensure power flow between energy generators (solar panel and pico-hydroelectric), batteries and load. Differential Hybrid Petri Net is used to model the proposed supervisory and simulations are made in Matlab environment.Results obtained present a good performance criteria Loss of Power Supply Probability, and this show the effectiveness of our approach in the coordination of HREs components during the energy sharing process by reducing load shedding in microgrid system