A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes

Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and the associated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data

The effect of land use configurations on concentration, spatial distribution, and ecological risk of heavy metals in coastal sediments of northern part along the Persian Gulf

In the present study, a total of 41 sediment samples were collected from the areas with different land uses: industrial (IS), urban (US), agricultural (AGS), and natural field (NS) in the northern coasts along the Persian Gulf from November 2016 to January 2017. Samples were analyzed to determine the concentration of heavy metals (Zn, Cu, Pb, Cd, Cr, and Ni). The mean concentration of Ʃ6 heavy metals in the samples taken from IS, US, AGS, NS were 2300.24, 251.02, 553.21, and 40.93 mg/kg, respectively. The predominant metals were Zn, Cu, and Pb and the mean concentrations of Ʃ3 metals (Zn, Cu, and Pb) in IS, US, AGS, NS areas were 2245.6, 241.44, 529.61, and 36.98 mg/kg, respectively. The results indicated that the mean concentrations of Ʃ6Metals/Ʃ3 metals in the IS and AGS samples were significantly higher than US and NS samples (p Cu (465.00) > Zn (427.16) > Cr (34.20) > Cd (19.45) > Ni (7.09); urban region: Zn (97.45) > Cu (79.90) > Pb (64.09) > Cr (5.30) > Ni (2.55) > Cd (1.73); agricultural region: Zn (247.88) > Pb (164.89) > Cu (116.84) > Cr (11.09) > Ni (7.45) > Cd (5.06); and natural fields: Zn (27.43) > Cu (6.34) > Pb (3.18) > Cr (1.94) > Ni (1.18) > Cd (0.83). According to geo-accumulation index (I-geo), the IS, US, and AGS were classified into “highly-extremely polluted”, “unpolluted-moderately polluted” and “highly polluted”, respectively. Similarly, in accordance with the ecological risk index (ERI), the IS and AGS fell into the very high and considerable categories, respectively, while US land uses area was categorized as low risk. Based on the results obtained from the present study, it can be concluded that the sediments of Asalouyeh coasts in the northern part of the Persian Gulf are heavily contaminated with heavy metals, causing serious negative effects on both the human being and environment