An Analysis of the Greenhouse Gas Emissions by the Re-Liquefaction of Boil-Off Gas of LNG Storage Tank

The pressure in liquefied natural gas (LNG) storage tank continues to increase due to the heat transfer from ambient air to low temperature LNG, which raises safety concerns. Accordingly, there is increasing interest to explore the technical approaches capable of recovering Boil-Off Gas (BOG) and even eliminating the ventilation of LNG storage tank. This research numerically analyzed the greenhouse gas emissions of the re-liquefaction of BOG using the following four approaches: 1) a Claude cycle driven by electrical motor with the electricity produced by burning coal;2) a Claude cycle driven by a gas turbine fuelled by BOG released;3) a Claude cycle driven by a SI engine fuelled by gasoline;4) burning nature gas directly released by BOG. The impact of heat transfer coefficient, LNG tank configuration, size, and percentage of LNG stored in tank on the rate of BOG and energy needed for the re-liquefaction of methane vapor were investigated. The greenhouse gas emissions (GGE) was examined and compared. The data presented in this paper provide guideline for the management of pressure development in LNG storage tank.

Computer Aided Design for the Recovery of Boil-Off Gas from LNG Plant

Boil-Off Gas creation and usage has been a source of worry in Liquefied Natural Gas value supply chain. BOG is generated when there is temperature gradient between the environment and LNG temperature within the carrier tank, process lines or vessels. In this work, Computer Aided Design for the recovery of BOG from flare in an LNG Plant considered the dynamic nature of the BOG with minimized total energy consumption. A rigorous simulation based optimization model using HYSYS V8.8 was presented. Possible BOG scenarios were formulated in this report and considerations taken from the BOG scenarios to form the basic scope of this work. An Aspen HYSYS Software was used to develop a Process Flow Scheme (PFS) which was simulated using the BOG scenarios formulated. The BOG scenario temperatures considered were -15°C for Warm Ship analogy, -90°C for Cold Ship and -140°C for Normal Design Mode. Assumptions were also made on the feed into the developed PFS before quenching the various BOG temperatures. With HYSYS simulation at assumed constant inlet mass flow rate of 25,000 kg/s for BOG FEED, 6250 kg/s for LNG & LNG1 FEED, quenching at various BOG feed temperature -15°C, -90°C and -140°C, gave a meaningful output. The Mass flow rate recovered from Warm Ship at -15°C for Cold Product was 35,183 Kg/s and for Liquid Product 2317 Kg/s. For Cold ship at -90°C, the Cold Product recovered was 32,174 Kg/s and Liquid Product was 5326 Kg/s. Also, for -140°C, the Cold Product was 28,004 Kg/s and the Liquid Product was 9496 Kg/s. The Energy stream for the Compressor, Cooler and Pump in the Process Flow Stream (PFS) were observed in Table 5. At -15°C, the Compressor energy was 3.22E+07KJ/h, while the Pump energy was 3412KJ/h, and the Cooler gave 1.90E+07KJ/h. The results above showed that excessive BOG from Warm ship can be quenched and recovered for other end users rather than undue flaring of the gases. Extra work needs to be done to ensure minimal energy utilisation, optimal recovery and high efficiency of this developed model.

Analysis of Efficiency of the Ship Propulsion System with Thermochemical Recuperation of Waste Heat

One of the basic ways to reduce polluting emissions of ship power plants is application of innovative devices for on-board energy generation by means of secondary energy resources.The combined gas turbine and diesel engine plant with thermochemical recuperation of the heat of secondary energy resources has been considered.It is suggested to conduct the study with the help of mathematical modeling methods.The model takes into account basic physical correlations,material and thermal balances,phase equilibrium,and heat and mass transfer processes.The paper provides the results of mathematical modeling of the processes in a gas turbine and diesel engine power plant with thermochemical recuperation of the gas turbine exhaust gas heat by converting a hydrocarbon fuel.In such a plant,it is possible to reduce the specific fuel consumption of the diesel engine by 20%.The waste heat potential in a gas turbine can provide efficient hydrocarbon fuel conversion at the ratio of powers of the diesel and gas turbine engines being up to 6.When the diesel engine and gas turbine operate simultaneously with the use of the LNG vapor conversion products,the efficiency coefficient of the plant increases by 4%–5%.

废热热化学回收对气体运输船能效的影响分析

Enlarging the fleet of gas carriers would make it possible to respond to the growing demand for hydrocarbon gases,but it will increase carbon dioxide emissions.The International Maritime Organization(IMO)has developed the energy efficiency design index(EEDI)with the objective of carbon emission reduction for new ships.In this paper,thirty gas carriers transporting liquefied natural gas(LNG)and liquefied petroleum gas(LPG)and equipped with various types of main engines are considered.As shown by the calculation of the attained EEDI,2 of the 13 LPG carriers and 6 of the 17 LNG carriers under study do not comply with the EEDI requirements.To meet the stringent EEDI requirements,applying thermochemical regenerators(TCRs)fed by main engine exhaust gases is suggested.Mathematical modeling is applied to analyze the characteristics of the combined gas-turbine-electric and diesel-electric power plant with thermochemical recuperation of the exhaust gas heat.Utilizing TCR on gas carriers with engines fueled by syngas produced from boil-off gas(BOG)reduces the carbon content by 35%and provides the energy efficiency required by IMO without the use of other technologies.

Enhanced application for FSRU recondensing equipment during periods of low or no gas send out to minimize LNG cargo losses

Most modern floating storage and regasification units(FSRU)are fitted with recondensing equipment that feed condensed boil-off gas(BOG)to the regasification unit in addition to a stream of liquefied natural gas(LNG)extracted from the cargo tanks.Use of the recondenser during regasification operations reduces gas losses on FSRU.It does so by avoiding consumption of excess BOG,with no associated commercial benefit,in gas combustion units(GCU),steam dumps,flares etc.Here we consider the benefits of also using the recondenser in recirculation mode,returning condensed BOG to the cargo tanks in the form of slightly warmed LNG.Such recirculation can be beneficial during periods of low or no gas send out from the FSRU,often achieving significant reductions in gas losses,although it is not standard practice in the industry to do so.Once regasification is halted not much BOG is required by the FSRU engine room,so the vessel must handle this excess.By condensing the BOG to LNG and returning it to the cargo tanks,the significant volume reduction involved has the beneficial impact of slowing down tank pressure increase.The saturated vapor pressure(SVP)of the LNG,linked to its composition and temperature,plays a key role in the boil-off rate and resulting cargo tank pressure changes.Detailed analysis is provided to explain how using the FSRU recondenser in recirculation mode can be best exploited by considering the prevailing fill levels,temperatures and pressures in each of the cargo tanks,and returning the condensed LNG preferentially to certain tanks.FSRU efficiency can be improved,gas losses and emissions can be reduced,and more cargo sold by exploiting the capabilities of the FSRU recondenser in recirculation mode.Running the FSRU in recirculation mode requires no equipment modifications to standard recondensers,neither does it increase FSRU operating costs.

Construction and Optimization of Liquefied Natural Gas Regasification Cold Energy Comprehensive Utilization System on Floating Storage Regasification Unit

In this paper, the efficient utilization of liquefied natural gas(LNG) vaporization cold energy in offshore liquefied natural gas floating storage regasification unit(FSRU) is studied. On the basis of considering different boil-off gas(BOG) practical treatment processes, a cascade comprehensive utilization scheme of cold energy of LNG based on the longitudinal three-stage organic Rankine cycle power generation and the low-grade cold energy used to frozen seawater desalination was proposed. Through the comparative analysis of the effects of the pure working fluid and eight mixed working fluids on the performance of the new system, the combination scheme of system mixed working fluid with the highest exergy efficiency of the system was determined. Then, the genetic algorithm was used to optimize the parameters of the new system. After optimization, the net output power of the LNG cold energy comprehensive utilization system proposed in this paper was 5186 kW, and the exergy efficiency is 30.6%. Considering the power generation and freshwater revenue, the annual economic benefit of the system operating is 18.71 million CNY.

Predicting saturated vapor pressure of LNG from density and temperature data with a view to improving tank pressure management

Determining the saturated vapor pressure(SVP)of LNG requires detailed thermodynamic calculations based on compositional data.Yet LNG compositions and SVPs evolve constantly for LNG stored in tanks.Moreover,the SVP of the LNG in a tank influences boil-off rates and tank pressure trends.In order to make improved tank pressure control decisions it would be beneficial for LNG tank operators to be made more constantly aware of the SVP of the LNG in a tank.Machine learning models that accurately estimate LNG SVP from density and temperature inputs offer the potential to provide such information.A dataset of five distinct,internationally traded LNG cargoes is compiled with 305 data records representing a range of temperature and density conditions.This can be used graphically to interpolate LNG SVP.However,two machine learning methods are applied to this dataset to automate the SVP predictions.A simple multi-layer perceptron artificial neural network(MLP-ANN)predicts SVP of the dataset with root mean square error(RMSE)=6.34 kPaA and R^(2)=0.975.The transparent open-box learning network(TOB),a regression-free optimized data matching algorithm predicts SVP of the dataset with RMSE=0.59 kPaA and R^(2)=0.999.When applied to infill unknown LNG compositions the superior TOB method achieves prediction accuracy of RMSE~3kPaA and R^(2)=0.996.Predicting LNG SVP to this level of accuracy is beneficial for tank-pressure management decision making.