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

Olive oil intake and cancer risk: A systematic review and meta-analysis

Background Research evidence has established the beneficial effects of diet in cancer prevention; various epidemiological studies have suggested that olive oil component could play a role in decreasing cancer risk. This systematic review and meta-analysis aims to investigate the association between olive oil consumption, cancer risk and prognosis. Methods A systematic search was conducted in PubMed, EMBASE and Google Scholar databases (end-of-search: May 10, 2020). Pooled relative risk (RR) and 95% confidence intervals (95% CIs) were estimated with random-effects (DerSimonian-Laird) models. Subgroup analyses, sensitivity analyses and meta-regression analysis were also performed. Results 45 studies were included in the meta-analysis; 37 were case-control (17,369 cases and 28,294 controls) and 8 were cohort studies (12,461 incident cases in a total cohort of 929,771 subjects). Highest olive oil consumption was associated with 31% lower likelihood of any cancer (pooled RR = 0.69, 95%CI: 0.62–0.77), breast (RR = 0.67, 95%CI: 0.52–0.86), gastrointestinal (RR = 0.77, 95%CI: 0.66–0.89), upper aerodigestive (RR = 0.74, 95%CI: 0.60–0.91) and urinary tract cancer (RR = 0.46, 95%CI: 0.29–0.72). Significant overall effects spanned both Mediterranean and non-Mediterranean participants, studies presenting a multivariate and a univariate analysis and all subgroups by study quality. Conclusions Olive oil consumption seems to exert beneficial actions in terms of cancer prevention. Additional prospective cohort studies on various cancer types and survivors, as well as large randomized trials, seem desirable.

Investigation of The Driving Factors of Ecological Footprint in Malaysia

Malaysia’s growing trends in energy production related emissions throw doubt on the country's possibility of meeting the Paris Climate Change Agreement and SDG obligations. Taking into account Malaysia’s current growth pattern and climatic circumstances, this study evaluates the association between ecological footprint and its potential determinants: economic growth, oil consumption, renewable energy and domestic capital investment for the period between 1965 and 2017. The stationary nature of the parameters is investigated using the conventional unit root approach (ADF and PP unit root) and structural break unit root (ZA unit root). The bounds approach in combination with the critical approximation p-values of Kripfganz and Schneider (2018) established a cointegration association between the observed parameters. The ARDL approach uncovered that economic growth and oil consumption contribute to ecological footprint. Furthermore, renewable energy consumption and gross capital formation reduce the ecological footprint. The FMOLS and DOLS estimators were applied as the sensitivity analysis of the ARDL estimators. Furthermore, the spectral BC causality approach was also utilized to investigate the causal association between ecological footprint and its determinants.

A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

A Historical and Econometric Analysis of Energy Consumption and Industrial Output in Pakistan (1990-2019)

This paper examines the nexus of disaggregated energy consumption and industrial output in Pakistan. The annual time series data over the period 1990-2019 has been taken for current research. ARDL technique has been employed for empirical analysis. The results show that oil consumption, electricity consumption and gas consumption are positively and significantly connected with the industrial output in long run. Similarly, trade openness, labour and capital also have the same association with the industrial output and have significant outcomes in the long run. The results of Granger causality show that there exists a unidirectional causality from electricity consumption to industrial output. The study concludes that oil, gas and electricity are contributing a large share in industrial growth so that it would be made an effort to install the plants relevant with these energy sources to meet the affordable demand in the industry sector.

Deviations and errors review on measuring and calculating heavy fuel oil consumption and fuel stock onboard vessels equipped with volumetric fuel consumption flowmeters

A common way of measuring heavy fuel oil consumption on board a vessel is to use volumetric fuel flow meters installed at fuel systems inlets for each of the major fuel consumers. At each stage of the fuel processing cycle, certain mass fuel losses or deviations and calculation errors occur that are not counted accurately into fuel consumption figures. The goal of this paper is to identify those fuel mass losses and measuring/calculating errors and perform their quantitative numerical analysis based on actual data. Fuel mass losses defined as deviations identified during the fuel preparation process are evaporation of volatile organic compounds, water drainage, fuel separation, and leakages while errors identified are flow meter accuracy and volumetric/mass flow conversion accuracy. By utilizing statistical analysis of obtained data from engine logbook extracts from three different ships numerical models were generated for each fuel mass loss point. Measuring errors and volumetric/mass conversion errors are numerically analyzed based on actual equipment and models used onboard example vessels. By computational analysis of the obtained models, approximate percentage losses and errors are presented as a fraction of fuel quantity on board or as a fraction of fuel consumed. Those losses and errors present between 0,001% and 5% of fuel stock or fuel consumption figures for each identified loss/error point. This paper presents a contribution for more accurate heavy fuel oil consumption calculation and consequently accurate declaration of remaining fuel stock onboard. It also presents a base for possible further research on the possible influence of fuel grade, fuel water content on the accuracy of consumption calculation.