An SVM-Based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels

Evaluation and measurement of bioethanol extraction from melon waste (Qassari cultivar)

As the world progresses, more energy is required to get along with everyday changes. Using bio-ethanol as one of the most important bio-fuels is an appropriate response to those changes. Much agricultural waste is being produced in Iran and the combination of such waste can turn it into a good source for the production of bio-ethanol. Thus, in this study, the amount of bioethanol extracted from melon waste was measured and evaluated. To do so, considering the 15 kg capacity of the device's fermenter, 12 kg of refined melon sugar syrup and three kilograms of water (with the standard ratio of 4 to 1), i.e. a total of 15 kg of substrate with the brix degree of 20, as well as 75 gr of saccharomyces cervisiae yeast, cultured in standard conditions (5 grams of yeast for each kilogram of substrate) were transferred into the fermenter. The tests were conducted in 35 hours with three replications and at three different rotation speeds of the fermenter's mixer. Sampling took place every five hours. Fermentation temperature was set as 30 degrees centigrade and distillation temperature was set to be 75 degrees centigrade which is the standard temperature of bioethanol's boiling point. The results showed that about 60.5 grams of bioethanol was obtained from each kilogram of melon. In addition, considering the alcohol produced and in order to optimize energy consumption, it was observed that the best speed for the mixer in the device's fermenter was 120 rpm.

ANN Application for Prediction of Diesel Engine Heat with Nano-Additives on Diesel-Biodiesel Blends

     Biodiesel is renewable clean bioenergy as it can be produced from vegetable oils, animal fats and micro-algal oil and also it can be applied instead of diesel fuel without any special modifications to the engines. In recent years, Nano-catalysts or Nano-additives in fuels improve the thermo-physical properties of fuels. In this study, the Carbon Nanotubes (CNT) as additive were mixed with the B5 and B10 fuel blends to evaluate the cylinder head and cylinder block temperature of a CI single-cylinder engine with an artificial neural network. carbon Nanotubes with concentrations of 30, 60, and 90 ppm were used for each fuel blends. Assessed characteristics were cylinder head and cylinder block temperature for full load engine at three speeds of 1800, 2300, and 2800 rpm. The results for optimum ANN model showed that the training algorithm of Back-Propagation with 24 neurons in a hidden layer was sufficient enough in predicting engine cylinder head and cylinder block temperature for different engine speeds and different fuel blends ratios. The MSE error and R-value for training, validation and testing of optimum ANN model were 0.00095, 10.40, 9.71 and 0.9999, 0.9487 and 0.9726 respectively. It can be concluded neural network is a powerful tool to predict diesel engine cylinder head and cylinder block temperature parameter with reasonable accuracy