Liquefaction Cycle Research Articles

Pre-cooling systems in modern hydrogen liquefaction

The research work presents recommendations for choosing a circuit design for low-capacity hydrogen liquefaction plants with a production rate up to 20 kg/h or 0,48 tpd (ton per day). Main design criteria considered are specific energy cost, as well as capital costs and overall characteristics of the system. A review of theoretical and real hydrogen liquefaction cycles is presented. Mathematical models of different circuits are built up considering real parameters of the typical equipment. The advantages and disadvantages of certain solutions are identified, efficiency trends in terms of hydrogen liquefaction plants are analysed. According to the obtained results of the research main of the circuits for low-capacity hydrogen liquefaction plants are selected.

Investigation and thermodynamic analysis of hydrogen liquefaction cycles: Energy and exergy study

The use of hydrogen as a clean fuel has drawn the attention of many scientists due to the problem of energy and environmental pollution caused by fossil fuels. One of the important requirements for expanding the use of hydrogen is the investigation and thermodynamic analysis of liquefaction cycle; this includes the thermodynamic investigation of different cycles of hydrogen liquefaction in pre-cooling and cryogenic cooling. Thermodynamic analysis comprises an examination of the cycle's energy and exergy, as well as the equipment employed. In this research, three liquefaction cycles with different pre-cooling cycle and cryo-cooling cycle have been evaluated. The use of organic Rankine cycle (ORC) and liquefied natural gas (LNG) has also been applied in the cycles and the arrangement of the equipment. Simulations and analyzes have been done in Aspen HYSYS V12. The results show that in the pre-cooling process of cycles 1, 2, and 3, the amount of useful exergy is 49.87 %, 58.87 %, and 61.21 %, respectively, which means that the third cycle uses the input exergy better. Also, in the pre-cooling process of cycles 1, 2, and 3, the amount of exergy loss is 33.86 %, 26.77 %, and 19.73 %, respectively, which means that the third cycle has less exergy loss in the pre-cooling process. The findings indicate that in each of the three cycles, over 50 % of the input exergy is wasted in the cryo-cooling process. Value of specific energy consumption (SEC) for cycle 1,2, and 3 is equal to 6.605 kWh/kgLH2 , 6.601 kWh/kgLH2 and 6.618 kWh/kgLH2, respectively. The three cycles under examination had COP values of 0.19945, 0.19936, and 0.19884, in that order. Also, the values for EXE cycles 1, 2, and 3 are 45.816 %, 45.883 %, and 45.797 %, respectively. Analyzing the energy and exergy of liquefaction cycles is a good step toward increasing cycle efficiency, identifying weak places, and altering cycles to improve efficiency.

Enhancing concentrated photovoltaic power generation efficiency and stability through liquid air energy storage and cooling utilization

Amid escalating climate concerns, particularly global warming, there is a significant shift towards renewable energy sources. Concentrated Photovoltaics (CPV) are at the forefront of this transition due to their high efficiency and clean energy generation capabilities. However, CPV cell stability and reliability are compromised by high operating temperatures, necessitating effective cooling solutions. This study proposes a novel coupled Concentrated Photovoltaic System (CPVS) and Liquid Air Energy Storage (LAES) to enhance CPV power generation efficiency and mitigate the challenges of high cell temperatures and grid integration. The research introduces an innovative process employing the cell liquefaction cycle for LAES, utilizing surplus cooling capacity to maintain CPV cells at optimal temperatures. A comprehensive thermodynamic analysis optimizes the coupled system’s operation and evaluates its economic benefits. The integrated system improves generation efficiency and economic viability of CPVS, resulting in a 24.41 % increase in photovoltaic module efficiency and a 2.03 % increase in overall rated power output. This leads to a 56.59 % increase in annual revenue for a 50 MW CPVS. Importantly, the findings demonstrate that integrating LAES with CPVS not only enhances solar energy utilization and economic benefits but also significantly contributes to the sustainability of clean, low-carbon energy systems, inspiring a greener future.

Hydrogen pressure-based comparative and applicability analysis of different innovative Claude cycles for large-scale hydrogen liquefaction

The Claude cycle is the preferred option for large-scale hydrogen liquefaction. However, the thermophysical properties of hydrogen vary substantially around the critical temperature and are strongly affected by hydrogen pressure. This leads to the fact that the classic Claude cycle is not universal for hydrogen at different pressures. Three innovative Claude cycles have been proposed. The applicability of three hydrogen liquefaction cycles is evaluated from specific energy consumption (SEC) perspective. The classic Claude cycle (Cycle 1) is suitable for the liquefaction of hydrogen with pressure above 3500 kPa, and the SEC for the liquefaction of hydrogen at 3500 kPa is 5.02 kWh/kgLH2. The Claude cycle (Cycle 2) with two cross-arranged refrigeration cycles is suitable for hydrogen with pressure between 2200 and 3500 kPa, and the SEC is 4.98 kWh/kgLH2 for the liquefaction of hydrogen at 2500 kPa. The Claude cycle (Cycle 3) with a split-flow refrigeration cycle is suitable for hydrogen with pressure less than 2200 kPa, and the SEC for the liquefaction of hydrogen at 1500 kPa is 5.27 kWh/kgLH2. Furthermore, the reasons for the SEC and applicability of three cycles being affected by hydrogen pressure are investigated by means of parametric analysis, heat exchanger performance analysis, and exergy analysis.

Triple-objective optimization using ANN+NSGA-II for an innovative biomass gasification-heat recovery process, producing electricity, coolant, and liquefied hydrogen

A novel thermal integration approach is introduced for a biomass-driven gas turbine power plant that generates electricity, coolant, and liquefied hydrogen. The designed scheme encompassed an organic flash cycle, a bi-evaporator ejector refrigeration unit, a high-temperature water electrolyzer for hydrogen production, a multi-effect desalination cycle for supplying water for electrolysis process, and a Claude cycle for hydrogen gas liquefaction. The designed system's importance comes back to using biomass feedstock as the input fuel and utilizing the hydrogen liquefaction method. In addition to the possibility of deploying the system in remote areas, it provides the opportunity for hydrogen storage in a smaller volume for more accessible storage and transportation. On the other hand, using a comparative method for selecting an environmentally friendly fluid for the heat recovery subsystem is another crucial aspect of the present study from the environmental aspect. It is found that R161 is the appropriate choice among the seven studied working fluids. Subsequently, a comprehensive evaluation of the entire system's thermodynamic and environmental aspects is performed using an intelligent process. By considering energy and exergy efficiencies along with CO2 emissions as the objective functions, a thorough sensitivity analysis and a triple-objective optimization are carried out. Hence, artificial neural networks for the objectives are developed and integrated into the NSGA-II optimization method. Employing LINMAP decision-making, the values of objective are attained, exhibiting 39.6 % for energy efficiency, 36.1 % for exergy efficiency, and 631.7 kg/MWh for CO2 emissions. Considering the optimum solution, the proposed system is capable of producing electricity, cooling, and liquefied hydrogen with capacities of 4526 kW, 1875 kW, and 21.22 m3/day, respectively. Additionally, the scenario yields an exergoenvironmental index of 0.579 and an exergetic stability index of 0.61. and a liquefied hydrogen generation rate of 21.22 m3/day.

Life cycle cost assessment of geothermal energy assisted hydrogen liquefaction for sustainable and renewable energy applications: Case study and adaptation for Afyon geothermal power plant

This study presents a comprehensive Life Cycle Cost Assessment (LCCA) of geothermal-assisted hydrogen liquefaction, explicitly focusing on the Afyon Geothermal Power Plant (AGPP) as a case study. The study evaluates the economic viability and sustainability of integrating geothermal energy into hydrogen liquefaction. The results of the LCCA contribute to the ongoing discourse on sustainable energy solutions, offering a nuanced understanding of the economic considerations associated with geothermal-assisted hydrogen liquefaction. The study serves as a model for assessing the economic feasibility of similar systems, fostering informed decision-making in the pursuit of cleaner and more economically sustainable energy pathways. This study has conducted a technical and economic investigation of liquid hydrogen production using geothermal heat and electricity effects. Geothermal water pre-cools the hydrogen by providing heat to the absorption system. Then, the electricity generated in the geothermal plant is used to work in the liquefaction cycle. The generated electricity is used to liquefy hydrogen in the liquefaction cycle. The capacity of the electricity generated from the AGPP produced here is 2621 kW. The proposed system can be achieved by pre-cooling unit H2 up to −30 °C at 120 °C and 150 kg/s geothermal source. In the liquefaction cycle, 0.84 kg/s H2 can be liquefied. As a result, the total energy efficiency of the proposed system can be calculated as 32.4% and exergy efficiency as 15.4%. The system's net present value (NPV) was calculated as 65,320,000 $. When life cycle cost analysis was performed using the levelized annual cost (LAC) method, the project's levelized cost was calculated as 7,673,000 $/yr. The unit cost of the liquid hydrogen produced is 1.252 $/kg. This system's discounted payback period (Ndpp) is calculated as 3.48.

Theoretical helium liquefaction rate based on the temperature-distributed refrigeration method in regenerative refrigerators

Helium liquefaction is important for applications of cryogenic engineering, helium recovery and purification, and so on. Regenerative refrigerators are generally applied for small-scale applications because of their relatively high efficiency, competitive compactness, etc. However, the liquefaction efficiency (the ratio of the input power of the ideal liquefaction cycle to that of the practical cycle) of helium is low. In this paper, a novel method called temperature-distributed refrigeration to improve the performance of helium liquefiers by utilizing distributed refrigeration power is proposed. The theoretical liquefaction efficiency by applying this method in the regenerative cycle is analyzed. The analysis assumes that there is no radial thermal resistance during the transmission of refrigeration power. The working pressure of helium in the refrigerator is in a wide reduced pressure range of 4.0–61.7, with the liquefied helium maintained at 0.1 MPa. The results indicate that the theoretical maximum liquefaction rate reaches 50.5 L/d (1.5 W at 4.2 K). This liquefaction rate is 2–3 times that of liquefying with only the cold ends. Meanwhile, the liquefaction efficiency increases with the working pressure, reaching a theoretical maximum of 44.5 % at a reduced pressure of 61.7. It is the highest possible efficiency for a two-stage regenerative refrigerator. This method should provide a reference for the optimization of small-scale helium liquefaction systems.

Thermo-environmental multi- investigation and ANN-based optimization of a novel heat integration criteria system integrated with a marine engine generating liquefied hydrogen

Marine transportation is a significant contributor to overall energy consumption among transportation sectors and is responsible for producing a considerable amount of greenhouse gases. A potential solution for alternative applications involves implementing combined heat integration, thereby enabling the production of additional utilities and mitigating emissions. The current work introduces an innovative heat integration process for a marine engine, focusing on implementing an optimal thermal matching technique to minimize overall irreversibility in producing liquefied hydrogen and coolant. The process incorporates an organic flash-bi-evaporator cooling cycle, a humidification dehumidification desalination, a polymer electrolyte membrane water electrolysis process, and a Claude cycle. The generated freshwater is delivered to the electrolyzer to produce gaseous hydrogen. This product and the cooling for freezing are utilized in the Claude cycle for hydrogen liquefaction. The study utilizes an advanced thermo-environmental multi-criteria investigation and optimization, considering sensitivity analysis and optimization based on artificial intelligence method. The optimization process incorporates the training and testing of artificial neural networks, NSGA-II method, and TOPSIS decision-making. The primary objective functions include exergetic efficiency and carbon dioxide emission. The findings demonstrate that the specified objectives are computed to be 0.121 and 2.67 kg/MWh, correspondingly. Besides, this condition exhibits a liquefied hydrogen flow rate of 6.44 L/h and a cooling output of 43.61 kW, showing an energy efficiency of 0.1145. Also, the total exergy destruction rate associated with the arranged structure is 124.5 kW. Furthermore, the optimum state reveals an exergoenvironmental index of 0.840 and an exergetic stability factor of 0.869.

Optimization of Mixed Fluid Cascade Natural Gas Liquefaction Plant Feed Stream Pressure

This paper presents the results of a study on optimising the feed stream pressure of the Mixed Fluid Cascade natural gas liquefaction cycle. The objective is to optimise the pressure of the feed stream to distribute heat effectively over the cooling stages and reduce the energy cost of the cycle. To achieve the goal, the following tasks have been done: constructing the mathematical model of the cycle, per-forming the calculation experiment for different values of the feed stream pressure to determine energy characteristic values, and determining the optimal values of feed stream pressure. The flow characteristics of the working substances in the cycle are determined based on the Peng–Robinson–Stryjek–Vera equations of state and Benedict–Webb–Rubin. The mass flow of the working substance is calculated for each cycle's stage heat load, at both constant and different values of the working point temperatures. The distribution of heat load for liquefying 1 kg of natural gas by stages is displayed, and achievable working point temperatures of the feed flow are determined. The energy consumed by the compressor units and the amount of compression heat dissipated are calculated. The study's main finding is that the optimal feed pressure in the Mixed Fluid Cascade cycle is higher than the standard pressure. The significance of the obtained results lies in the significant reduction of energy costs of the Mixed Fluid Cascade cycle at a feed stream pressure of 13 MPa.

Optimized integration of solar energy and liquefied natural gas regasification for sustainable urban development: Dynamic modeling, data-driven optimization, and case study

As sustainable urban communities continue to seek alternative energy sources, this study explores the inclusion of renewable resources in the energy mix. With a focus on liquefied natural gas (LNG) regasification, parabolic trough solar collectors, dual-loop power cycles, proton exchange membrane electrolysis, and hydrogen liquefaction cycle, this research conducts a comprehensive examination of an integrated system. The primary objective is to provide a diverse range of valuable outputs, including electricity, liquefied hydrogen, desalinated water, and cooling for coastal areas. Through careful analysis of regional geographic features, San Francisco emerges as a suitable location due to its favorable solar radiation intensity and existing LNG transportation infrastructure. The findings reveal that the integrated system has the potential to deliver approximately 5750.4 MWh of cooling, 14988.85 MWh of electricity, and 1,491,084 m3 of fresh water, thereby significantly contributing to the city's utility demands. In terms of data-driven optimization, artificial neural networks serve as intermediary mechanisms to establish correlations between the developed code and the optimization algorithm. The results demonstrate that the optimized system exhibits notable improvements in production capacity. Moreover, significant reductions in the levelized cost of electricity (0.19 Cent/kWh), fresh water (1.22 Cent/m3), and hydrogen (0.08 $/kg) are observed. Although the optimized case entails a 16.11% higher cost rate compared to the base system, it is projected to generate a 25.59% higher profit over its operational lifespan. Additionally, the payback period of the optimized system is shortened by 6.31%, making it an attractive long-term investment for sustainable urban development.

Constrained multi-objective optimization of helium liquefaction cycle

The helium cryo-plant is an indispensable subsystem for the application of low temperature superconductors in large-scale scientific facilities. However, it is important to note that the cryo-plant requires stable operation and consumes a substantial amount of electrical power for its operation. Additionally, the construction of the cryo-plant incurs significant economic costs. To achieve the necessary cooling capacity while reducing power consumption and ensuring stability and economic feasibility, constrained multi-objective optimization is performed using the interior point method in this work. The Collins cycle, which uses liquid nitrogen precooling, is selected as the representative helium liquefaction cycle for optimization. The discharge pressure of the compressor, flow ratio of turbines, and effectiveness of heat exchangers are taken as decision parameters. Two objective parameters, cycle exergy efficiency, ?ex,cycle, and liquefaction rate, ?L , are chosen, and the wheel tip speed of turbines and UA of heat exchangers are selected as stability and economic cost constraints, respectively. The technique for order of preference by similarity to the ideal solution (TOPSIS) is utilized to select the final optimal solution from the Pareto frontier of constrained multi-objective optimization. Compared to the constrained optimization of ?ex,cycle, the TOPSIS result increases the ?L by 23.674%, but there is an 8.162% reduction in ?ex,cycle. Similarly, compared to the constrained optimization of ?L, the TOPSIS result increases the ?ex,cycle by 57.333%, but a 10.821% reduction in ?L is observed. This approach enables the design of helium cryo-plants with considerations for cooling capacity, exergy efficiency, economic cost, and stability. Furthermore, the wheel tip speed and UA of heat exchangers of the solutions in the Pareto frontier are also studied.

Exergy and energy analysis of a hybrid natural Gas/Hydrogen liquefaction cycle combined with methanol production plant

This paper presents an innovative renewable multi-generation system designed to simultaneously produce various fuels, including biomass-based methanol, liquid hydrogen, and liquid natural gas. This system is part of a broader strategy aimed at streamlining different processes within a comprehensive plant, enabling the simultaneous production of multiple biomass-based fuels at a single location. The implementation of this system in regions where natural gas liquefaction plants coexist with abundant forest resources allows for the efficient production of all required fuels. In this process, a new configuration leverages to regenerate the waste heat of methanol synthesis process in the diffusion absorption refrigeration cycle integrated to the natural gas liquefaction process to maximize energy and exergy efficiencies. Additionally, three hydrogen liquefaction processes are evaluated to identify the most suitable option based on performance, efficiency, and power consumption. The investigations reveal that the combined mixed refrigerant cycle (CMRC) emerges as the ideal choice for hydrogen liquefaction process, characterized by lower power consumption and a higher coefficient of performance. The energy and exergy efficiencies of biomass gasification and methanol synthesis processes are 87.54% and 80.41%. The energetic coefficient of performance of the natural gas liquefaction process and CMRC is 0.9 and 0.2209. The specific power consumption of natural gas liquefaction process is 0.225 kWh/kgLNG, and the specific power consumption of CMRC is 5.462 kWh/kgLH2. The natural gas liquefaction process is responsible for the highest portion of the total exergy destruction, with 32%.

An innovative mixed refrigerant hydrogen liquefaction cycle to store geothermal energy as liquid hydrogen

An innovative mixed refrigerant hydrogen liquefaction cycle to store geothermal energy as liquid hydrogen

An innovative process design of seawater desalination toward hydrogen liquefaction applied to a ship's engine: An economic analysis and intelligent data-driven learning study/optimization

Integrating heat recovery applications with heavy engines during transportation is critical for achieving sustainable production and reducing the irreversibility associated with the power production process of the engine. In this regard, the current paper introduces a novel waste heat recovery method utilizing the hot flue gas released by a 1-MW ship's engine to yield liquefied hydrogen from seawater desalination while simultaneously meeting the ship's air-conditioning requirement. In addition, a techno-economic analysis and an advanced feasibility study/optimization are conducted. The process employs reverse osmosis desalination to generate freshwater fed to a water electrolyzer for hydrogen production. The needed power is supplied from an organic flash cycle coupled with an ejector-based bi-evaporator refrigeration cycle. The first cooling loop of the refrigeration cycle offers air-conditioning, while the low-temperature loop yields cooling for the Claude hydrogen liquefaction cycle. A comprehensive feasibility assessment is conducted from the thermodynamic, economic, and environmental viewpoints, and accordingly, an intelligent data-driven learning approach is implemented for performing different multi-criteria optimization scenarios. This approach uses an artificial neural network with a multi-objective grey wolf optimization method. The LINMAP method is employed to reach the most favorable scenario and identify the optimal solution. The findings indicate that the flash temperature attains the highest mean sensitivity index measured at 0.377. Moreover, the best optimization scenario is associated with the exergy efficiency, CO2 emission reduction, and liquefied hydrogen cost as objective functions, calculated at 11.39 %, 22.31 kg/MWh, and 10.25 $/kg, respectively.

Techno-economic-environmental study and artificial intelligence-assisted optimization of a multigeneration power plant based on a gas turbine cycle along with a hydrogen liquefaction unit

According to the literature, there is a research gap concerning the practical heat recovery in solar-driven systems that have the capability to produce liquid hydrogen. In this study, an innovative combination of dual-loop solar-driven organic Rankine cycle and liquefied natural gas (LNG) heat recovery is designed to produce and liquefy hydrogen. The proposed system includes parabolic trough solar collectors (PTSCs), a Rankine cycle, a dual-loop organic Rankine cycle, LNG regasification process, proton exchange membrane (PEM) electrolyzer, and Claude hydrogen liquefaction cycle. A data-driven method is developed to analyze the system from techno-economic and environmental perspectives. The results show that LNG energy recovery improves the liquefaction work by as much as 7.96 kWh/kgH2. It is also concluded that the optimum compaction pressure range for the liquefaction cycle is 4.67 MPa, which is associated with better results. In these conditions, liquefaction work, liquefaction Coefficient of Performance (COP), and liquefaction exergy efficiency are 164.6 kJ/kg, 0.157 and 17.05%, respectively. To find optimum operating conditions, a supervised learning approach is applied to the developed code and the trained network is optimized using the genetic algorithm (GA). The optimization results reveal that a 10.98 $/h increase in total cost rate causes an 18% improvement in hydrogen production rate.

Techno-economic analysis and optimization of green hydrogen production and liquefaction with metal hydride storage

A techno-economic analysis and three-objective optimization are reported for a system that uses a renewable energy source (RES) for hydrogen production and liquefaction in which all the produced hydrogen enters the liquefaction cycle. The system works in three phases; hydrogen liquefaction, hydrogen storage and fuel cell power generation. Every second, the mass of produced hydrogen from the electrolyzer is calculated and immediately injected into the liquefaction cycle. This happens for 3600 s during each hour and for 12 h during the day. To reduce the execution time and increase the accuracy, six different artificial neural network (ANN) models have been used, which are optimized using genetic algorithm (GA). Decision variables for optimization include solar irradiation intensity, ambient temperature, heat exchanger efficiency, and isothermal compressor output pressure. The objective functions are chosen to be total exergy efficiency, liquefaction efficiency, and total cost rate, and the optimal values of the objective functions are found to be 19.3%, 19.8%, and 10.6 $/hr, respectively. The objective functions are illustrated in terms of daily time, as well as decision variables. Also, the effect of liquefaction efficiency and specific work consumption on liquefaction cycle parameters and electrolyzer temperature have been investigated. The results show that in the range of compressor output pressure between 5 and 15 MPa, for every n % increase in heat exchanger efficiency, the liquefaction efficiency increases by (n/2) % and the amount of liquid hydrogen production at steady-state becomes 0.162 kg/h. It is also found that if the storage system is turned off and all the produced power is given to the liquefaction system, the mass of liquefied hydrogen will increase by 17% at the end of the day.

Energy, exergy, and economic analysis of an innovative hydrogen liquefaction cycle integrated into an absorption refrigeration system and geothermal energy

Liquid hydrogen is a solution to store and transport electricity which is produced by renewable energy sources like geothermal energy. In this paper, an organic Rankine cycle (ORC) and proton membrane electrolyzer (PEM), water-ammonia absorption refrigeration cycle, and a hydrogen liquefaction cycle are simulated and analyzed to store the geothermal energy as liquid hydrogen. The power produced by the ORC is used to produce hydrogen gas in PEM, additionally, cold energy from the absorption refrigeration cycle is used to precool the hydrogen gas to −26.9 °C. The novelty of this study is based on using cold energy from an absorption refrigeration system in the pre-cooling part of the hydrogen liquefaction cycle to convert geothermal energy to liquid hydrogen, this method leads to a reduction in power consumption. Specific energy consumption (SEC), figure of merit (FOM), and coefficient of performance (COP) of the final case in this study are 8.81 kWhkg−1, 0.83, and 0.49 respectively. The proposed cycle is compared with similar cycles available in the literature. The specific liquefaction cost (SLC) is 1.84 $kg−1. The minimum selling price is estimated to be 2.11 $kgLH2−1 with the payback time of three years.

Effects of solvent phase recycling on microalgae liquefaction in ethanol: Bio-oil production and nitrogen transformation

Liquefaction of microalgae in ethanol offers an eco-friendly bio-oil alternative, but solvent recycling is crucial for sustainability due to extra costs. In this work, Chlorella vulgaris was liquefied in supercritical ethanol at 260 °C, and the solvent phase (SP) separated from bio-oil was recovered and reused. Five liquefaction cycles were performed at identical temperature and pressure conditions to investigate the effects on oil production and nitrogen transformation. The findings demonstrated a gradual increase in water content in recycled SP. Ethanol-water co-solvent as the reaction medium promoted the decomposition and re-polymerization of protein in raw material, thus increasing the bio-oil yield (76.84 %) and higher heating value (33.53 MJ/kg) to some extent. Simultaneously, the relative nitrogen content of bio-oil rose from 8.03 % to 8.52 %, predominantly in the form of nitrogen heterocycles. The potential pathway for nitrogen conversion was revealed, which establishes a theoretical basis for the subsequent denitrification of bio-oil.

MODERNIZATION OF THE NATURAL GAS LIQUEFACTION CYCLE AT GAS DISTRIBUTION STATIONS

The possibility of developing the liquefied natural gas (LNG) market in the Russian Federation can be realized by increasing the projects of low-tonnage LNG production. The development of LNG production projects at gas distribution stations should be a priority, since in this case it is possible to implement various production schemes: expander, throttle or cycle with a vortex pipe. To assess the efficiency of the use of low-tonnage LNG production at gas distribution stations (GDS), the dynamics of changes in maximum and minimum pressure values in the main gas pipeline, as well as the distribution of GDS by seasonal change in pressure drop and throughput are presented. To assess the operability and applicability of the work, a design model of the natural gas liquefaction throttle cycle was built and numerical modeling of its operation was carried out at seasonal fluctuations in pressures and GDS costs, which showed a strong dependence on these fluctuations and a low degree of natural gas liquefaction. In order to further modernize the liquefaction cycles of natural gas, modeling was carried out according to the turbo expander cycle, which also showed a dependence on seasonality, but a higher degree of liquefaction of natural gas, which during modeling reached more than 8%. The final stage was the construction of a design model of the combined technology — throttling and turbo expander cycles, as well as numerical modeling, which showed the possibility of increasing the liquefaction of natural gas at GDS to 19%, but the impact of seasonal fluctuations also remains significant. Summing up, it can be said that the work has worked out options for the transition from the throttle cycle to the throttle-expander and turbo-expander, as a result of which it was concluded that the operation of each cycle is effective, due to which it is possible to expand the LNG production network at the GDS at various input pressures, flow rates and the required degree of liquefaction of natural gas.

A new optimization method of energy consumption for dynamic boil-off gas

Related research on refrigerant optimization based on the dynamic BOG has yet to be proposed. This study offers a new dynamic optimization method for boil-off gas (BOG) re-liquefaction system to improve the system's energy efficiency. The performance of C3MR (the propane precooling mixed refrigerant cycle) is analyzed based on the steady state model of leading liquefaction equipment in the process simulation in Aspen HYSYS software. And the principle of the energy consumption variation law of propane precooled compressor and mixed refrigerant compressor, based on the actual possible disturbance range obtained in the natural gas liquefaction process, is applied to construct the multi-objective optimization model with minimum energy consumption and maximum heat exchanger efficiency. Finally, the dynamic responses of disturbances were achieved and discussed with the aid of GA (Genetic Algorithm) and PSO (particle swarm optimization). The optimized performance of the dynamic liquefaction cycle is 34.8% better than the steady condition system in terms of energy efficiency, and the maximum energy consumption can be reduced by 50.60%. The advantage of the proposed optimization framework is its adaptability to other dynamic liquefaction processes. It has practical reference significance for the cost control of the BOG re-liquefaction recovery project.