Ship Operational Efficiency from AIS Data Using Big Data Technology

The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

Electricity Substitution Potential Prediction Based on Tent-CSO-CG-SSA-Improved SVM—A Case Study of China

Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step toward achieving sustainable development, the transformation of the end-use energy consumption structure, and double carbon goals. To better guide the broad promotion of electrical power substitution, and to offer theoretical support for its development, this paper quantifies the amount of electrical power substitution and the influencing factors that affect the potential of electrical energy substitution. This paper proposes a hybrid model, combining Tent chaos mapping (Tent), chicken swarm optimization (CSO), Cauchy–Gaussian mutation (CG), the sparrow search algorithm (SSA), and a support vector machine (SVM), as a Tent-CSO-CG-SSA-SVM model, which first uses the method of Tent chaos mapping to initialize the sparrow population in order to increase population diversity and improve the search ability of the algorithm. Then, the CSO is introduced to update the positions of sparrows, and the CG method is introduced to make the algorithm jump out of the local optimum, in order to improve the global search ability of the SSA. Finally, the final electrical power substitution potential prediction model is obtained by optimizing the SVM through a multi-algorithm combination approach. To verify the validity of the model, two regions in China were used as case studies for the prediction analysis of electrical energy substitution potential, and the prediction results were compared with multiple models. The results of the study show that Tent-CSO-CG-SSA-SVM offers a good improvement in prediction accuracy, and that Tent-CSO-CG-SSA-SVM is a promising method for the prediction of electrical power substitution potential.

Physicochemical Upgrading of a Biodetergent for Application in the Industrial Energy Sector

In the industries across the petroleum chain and those involved in energy generation, the use of petroderivatives as fuel oils is common. To clean parts, equipment and environments contaminated by hydrocarbons, they use expensive, toxic products, bringing risks to the environment as well as to workers’ health. Thus, the aim of this study was to check the stability of a biodetergent prepared using atoxic substances for large-scale production and industrial energy sector application. The relationship between volume (4 to 10 L) and stirring time (5 to 10 min) of the formulation at 3200 rpm and 80 °C was evaluated. The hydrophilic lipophilic balance (HLB), long-term stability (365 days), toxicity and efficiency of low-sulfur, viscous fuel oil removal from metal pieces and floors were investigated. The interaction among operating conditions was shown to influence the features of the product, which achieved approximately 100% stability after a stirring time of 7 min. The emulsion HBL index varied between 4.3 and 11.0. The biodetergent maintained its physicochemical properties during its 365 days of storage and showed high efficiency, removing 100% of the OCB1 impregnated on the metallic surfaces and floors tested. The formulation showed reliability in scale up when submitted to the study of physicochemical factors in the productive process, and safe application, by reducing risks for workers’ health and environment.

The Economic and Environmental Impact of Greenhouse Heating Pipe Insulation

This study aimed to determine the effect of optimum pipe insulation thickness on energy savings and air pollution under greenhouse conditions. In this regard, an optimization model based on a Life Cycle Cost (LCC) analysis was carried out using the P1–P2 method. Three fuel types, coal, natural gas, and fuel oil, were tested with nominal pipe sizes ranging from 25 to 65 mm, and hot water was used in the system. Our findings showed that the highest insulation thickness (0.807 m), the greatest energy savings ($62.351/m), and the lowest payback period (0.502 years) were achieved with a 65 mm pipe size for fuel oil. Overall, the insulation minimizes heat loss through the heating pipelines, resulting in economic and environmental benefits. Fuel oil was determined as the best option for savings in this study. Hence, for fuel oil utilization, the emissions of CO2 varied from 2.762 to 3.798 kg/m and SO2 from 0.014 to 0.020 kg/m for pipe thicknesses ranging from 25 and 65 mm, respectively.

Modeling NOx, CO, SO2 and PM emissions from Sabzevar cement plant using SCREEN3 software

Introduction: Nowadays, the cement industry is regarded as one of the most important air pollution industries globally. This study aimed to simulate the emission of NOx, CO, SO2, and PM pollutants caused by the Sabzevar Cement Factory chimney by SCREEN3 software. Materials and Methods: In this study, the SCREEN3 software was employed for the distribution of NOx, CO, SO2, and PM pollutants. The inputs of the model include the concentration and emission of pollutant gases, physical factors associated with the cement factory chimney, wind speed and direction, ambient temperature, and stability classes. Results: The results of this study indicated that the maximum concentrations of NOx, CO, SO2, and PM by the SCREEN3 software occurred in unstable weather conditions (B) and wind speed of 5 m.s. The highest concentrations of NOx, CO, and PM (use of gas) were at a distance of 1400 meters from the factory chimney with the rates of 0.9, 0.32, 6.2 μg.m³, respectively. Moreover, the highest concentrations of NOx, CO, SO2, and PM (using fuel oil) were predicted at a distance of 1100 m from the factory chimney with 19.5, 360, 9, and 7.9 μg.m³, respectively. A comparison of the obtained results with the standard of the Environmental Protection Agency of Iran (EPA) revealed that the concentrations of NOx, CO, SO2, and PM were not higher than the standards. Conclusion: The comparison of results with EPA standard and Iranian clean air standard showed that NOX, CO, SO2, and PM concentrations were not higher than standards during the sampling period.

The Transfer Phenomena of Air Pollutants Emitted from Power Station and Generators in Nasiriyah City and the Effect of Meteorological Parameters on its Dispersion by Using Fixed Box Model

The study aims to use the fixed box model to calculate the spread of pollutants (CO2, SO2, NOX, particulate) resulting from the burning of fuel used to produce electrical energy in the Nasiriyah city and to know the way they spread in the city through being affected by the wind speed and compare the results calculated from the model with the results measured by the lancom4 device. The results showed that the main pollutants for the air in Nasiriyah was emitted from burning the fuel used for the production of electric power, and the results showed that the concentration of pollutants (CO2, SO2, NOX) was much higher inside the city when compared with the upstream direction of the winds due to its increase with the movement of winds and its entry into the city. Through the application of the fixed box model and when comparing the calculated results through the model with the results measured by the lancom4 device, the error rate was (4 %, 2%, 2%, 5%) for pollutants (CO2, SO2, NOX, particulate) respectively, it was also observed that the highest emission rate of pollutants was result from using heavy fuel (fuel oil) and the lowest emission was from light oil (Dry gas). We noted the spread of pollutants and dilution in the atmosphere increases with the increase in wind speed, excluding for particles mater.

Oxidative Desulfurization of Fuel Oil Catalyzed by Carbon Nitride Supported Phosphotungstic Acid Based Dicationic Ionic Liquid

In this study, a novel phosphotungstic acid based dicationic ionic liquid [C2(MIM)2]PW12O40 was successfully prepared and immobilized on graphitic carbon nitride (g-C3N4). The supported catalysts wt% [C2(MIM)2]PW12O40/g-C3N4 (wt=3%, 10%, 30%,...