Large amounts of degradable wastes including sewage water and agricultural waste or nutrients that stimulate growth of organic matter are discharged into the Caspian Sea. As a result, average Dissolved oxygen concentration in deeper layers is decreasing. This paper presents distribution and seasonal variations of the concentration of Dissolved oxygen over the southern shelf of the Caspian Sea adjacent to Iran. The Dissolved oxygen data were collected down to 200 m depth in two areas in east (off Babolsar in Mazandaran) and west (off Kiyashahr in Gilan) of the southern coast of the Caspian Sea. Surface Dissolved oxygen concentration varied between 7.1 and 10.9 mg/l. Distribution of Dissolved oxygen across the depth was in accordance with the temperature structure. The presence of the seasonal thermocline during spring to mid winter significantly affected the concentration of Dissolved oxygen across the depth. In autumn, in late October, the Dissolved oxygen concentration ranged between 7.6 mg/l below thermocline at 40 m level to less than 5 mg/l at 160 m level and 4.2 mg/l at 200 m level. In winter in late February, in the upper 100 m mixed layer the Dissolved oxygen concentration was more than 11 mg/l. The data indicates the possibility of significant decline in Dissolved oxygen concentration and serious damage to marine life if algal bloom occurs during the strong seasonal thermocline. The results highlight the necessity of certain measures for an effective decrease in the inputs of degradable wastes and plant nutrients into the Caspian Sea.

Water quality aspect with regard to Dissolved oxygen was studied for a 24 kilometer stretch of Malaprabha River in Karnataka State, India. Main objective of the research was to simulate the predicted Dissolved oxygen depletion due to the waste load allocation in the river with the ambient observed values of Dissolved oxygen, and to ascertain the application of mathematical modeling for predicting the Dissolved oxygen in a mixing zone. The entire river stretch selected for the study was divided in to four stations and water samples were collected from each station, and analyzed for different parameters. Station S1 was purely the upstream of the sewage discharge point and hence reflected the water quality of the river without pollution. Station S2 and downstream stations were the part of mixing zone where the discharged sewage at S2 kept spreading. Dissolved oxygen levels at these four stations were calculated through mathematical modeling, considering all variables that affect Dissolved oxygen variation. Further, this predicted Dissolved oxygen found with the application of mathematical modeling was then simulated with the actual observed values of Dissolved oxygen in these four stations. The results were highly encouraging, with the predicted values almost agreeing with the observed values. Thus the application of such mathematical modeling may be useful for a river stretch where water pollution due to the discharge of effluents is expected, and will help the engineers in arriving at a proper waste-load allocation for the river.

The reliability of fault diagnosis and the stability of electrical grids demand accurate analysis of key chemical gases in power transformer oil. DGA, which quantitatively measures concentrations of critical chemical gases, is still a cornerstone technique in the field, but is infamous for its interpretative complexity: the correlation between gas levels and specific fault types is too complex. The proposed methodology in this study attempts to integrate the strengths of traditional methods of DGA interpretation along with the power of a machine learning model, specifically a Random Forest algorithm. The process comprises the preprocessing of DGA data to extract meaningful chemical features from them further developing the model using machine learning to classify the different kinds of faults based upon those chemical features. This approach has been validated on multiple scenarios for the data coming from DGA transformer faults after a lot of testing. Results show that this method delivered an average accuracy of 95.86% for three types of faults and 93.67% for the same types of faults with varying conditions. For six types of faults, the delivery was placed at an average accuracy and consistency of 88.85% and 87.47%, respectively. This approach significantly shows improved performance in the traditional methods of diagnostics while promising much more accurate fault detection. In addition to enhanced diagnostic accuracy, it supports proactive, hence preventive, maintenance strategies, resulting in improved system efficiency and reduced downtime. The paper details a technique that combines chemical data analysis with machine learning, from which distinct solutions can be conceived to address the complex challenges facing industries.

Every year, billions of cubic meters of flare gas burn in various upstream and downstream sectors of the oil industry. This is a valuable source of energy and is wasted due to the lack of necessary infrastructure. Various solutions have been proposed to recover flare gases and reuse them and electricity production is of particular importance due to its high flexibility and the existence of many technologies. The purpose of this research is to investigate the distribution of associated and flare gases in provinces of Iran based on satellite data provided by the World Bank. Arc GIS software was used to prepare the dispersion map. Then, based on this information, the amount of lost capital, the amount of CO2 equivalent emissions caused by the burning of these gases, and the potential of electricity generation in each of the provinces of Iran have been calculated separately. According to the obtained results, the total gas flared in Iran raised incredibly from 2020 to 2023 (from 13258.05 to 20421.13 million of cubic meters). It means not only growing in the lost capital from 1521.66 to 2343.48 milliondollars and the total equivalent emission of CO2 from 35.35 to 57.18 million tones but also raising the total potential of power generation from 8181.82 to 12589.19 MW. Furthermore, the dispersion of associated and flare gases in Iran creates a suitable situation for creating dispersed generation power plants, especially in provinces such as Khuzestan, Bushehr, Kohgiloyeh and Boyer Ahmed, and Ilam.

A mathematical model was developed to predict the effects of wind speed, light, pH, Temperature, Dissolved carbon dioxide and chemical oxygen demand (COD) on Dissolved Oxygen (DO) in fish ponds. The effects of organic feeds, aeration and fish activity were added to the model developed by Kayombo et al. for Waste Stabilization Ponds (Ecological Modeling 127(2000): 21-31) to reflect the situation in fish ponds. Model calibration and validation was done by use of average DO, pH, temperature, COD, CO2 and algae biomass data measured from RETCO fish ponds in Dar es Salaam; and light intensity data were adopted from Kayombo et al. (2000). Model results showed a linear relationship between simulated DO and measured DO in fish pond (r2 = 0. 87) for model calibration and (r2= 0.88) for model validation. Simulation results also showed a general decrease of DO with time in 13 days by 28% and 38% for first and second batch, respectively. Thus, the model developed in this study could be used to predict the DO dynamics in fish ponds. Based on the model results, successful cultivation of healthy fish may require that retention time for water in the fish pond be 10 days.