جستجوی مقالات مرتبط با کلیدواژه « Pattern Recognition » در نشریات گروه « مهندسی شیمی، نفت و پلیمر »

Although groundwater withdrawal is considered to be the main and most common cause of land subsidence, extraction of oil and gas, or minerals from beneath the earth's surface also results in land subsidence. Land subsidence over oil and gas fields leads to several natural and manufactured disasters such as critical and strategic infrastructure destruction. In addition, it imposes significant costs on the oil industry, which indicates the need to control and monitor land subsidence in oil fields. Due to irrecoverable damage caused by this phenomenon, it is necessary to regularly monitor areas subject to subduction in oil and gas fields. There are different ways to measure crustal deformation in Geomatics engineering based on geodetic and remote sensing technologies. Despite high accuracy, the use of geodetic techniques such as Global Navigation Satellite System (GNSS) to regularly monitor crustal deformation in a wide coverage is limited by some disadvantages like discontinuous data collection, the need for installation of the equipment on the ground or direct contact with the ground which is time- and cost-consuming. InSAR technology has been widely used in recent decades to monitor crustal deformations with high spatiotemporal resolution and lower cost and time. This study aims to present a new methodology based on the Persistent Scatterer-InSAR (PS-InSAR) method to investigate the correlation between land subsidence and petroleum extraction in the southwestern region of Iran with active oil and gas fields during two periods from 2017/04 to 2017/10 and from 2019/04 to 2019/10, respectively with the highest and lowest petroleum extraction. The results showed that subductions of 10 to 30 cm during the period of 2017/04 to 2017/10 with an annual rate of 20 to 50 cm have occurred in southwestern Iran around the active oil fields which might be due to the significant petroleum extraction since 2016.

Tomato is the second most important crop in the world, which is often consumed freshly. One of the most importance tomato products is paste that considered being the stuffing ingredient in Iranian foods. Therefore, the safety of tomato paste is so important. The aim of this study was diagnosis adulteration in tomato paste. For this purpose, olfactory machine system based on five gas sensors (TGS2600, TGS2620, MQ3, TGS880, TGS2610) was constructed and its potential was evaluated in determining the different levels of pumpkin adulteration in tomato paste (0, 5, 10, 15 and 20%). Pure tomato paste detection from adulteration samples was based on the samples smell in the head space and the receipt of smell by sensors. The principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) and partial least squares (PLS) were used to classify and analyze the extracted features of the sensor response. Results of PCA and PLS indicated that 99 and 94 percent of data variance was covered by two main components. TGS2610 and MQ-3 sensors and TGS880 sensor had the highest and lowest application in detection of tomato paste adulteration. The accuracy of the classification by the LDA method was 79.7%. The polynomial function with accuracy of 77.78% of the training and 76.66% validation in the C-SVM method and the radial base function with a precision of 98.8% of the training and 88.14% of Validation in the Nu-SVM method had the highest classification accuracy. In total, the olfactory machine system had acceptable performance in separation of different levels of adulteration.