Aspen HYSYS Research Articles

Cryogenic-membrane separation process for helium extraction and ethane co-production from natural gas

In order to address the defects of high energy consumption and low efficiency in the single cryogenic separation process for helium extraction, this paper integrate post-expansion nitrogen cycle refrigeration with membrane separation to establish a novel natural gas cryogenic-membrane helium recovery process. Based on four mainstream processes for ethane recovery from natural gas, an optimal ethane recovery method is selected and incorporated into the proposed cryogenic-membrane helium recovery process. The process is then simulated using ASPEN HYSYS software. The effects of operating parameters on critical equipment energy consumption, helium product concentration and ethane recovery were evaluated. The simulation results indicate that in the proposed helium recovery process, the concentration of refined helium reaches 99.6 %, and the ethane recovery rate is 90.13 %. Compared to the single helium extraction process, the total compressor energy consumption in our process is reduced by 23.10 %, and the integrated energy consumption is reduced by 20.4 %.

Techno‐economic and carbon footprint analyses of steam Rankine cycle

AbstractSteam Rankine cycle (SRC), which is mainly utilised in power generation sector, faces external irreversibility in its daily operation causing inefficiency in the system. To address this issue, reheat Rankine cycle (RHRC) and regenerative Rankine cycle (RRC) have been widely studied and implemented in power plants to improve thermal efficiency and reduce external irreversibility of Rankine cycle. This study investigates the implementation of different RRC configurations in a combined heat and power plant, including RRC with modified thermal deaerator, RRC with open feed water heater (OFWH) and closed feed water heater (CFWH). A base case simulation model was first constructed using commercial simulation software Aspen HYSYS for the basic SRC system based on actual plant data. Various scenarios were then evaluated for their profitability and sustainability through techno‐economic analysis (TEA) and carbon footprint analysis (CFA). From both analyses, the scenario of RRC with CFWH showed the greatest long‐term potential, generating the highest annual profit of $ 771 691 and carbon footprint reduction of 14.63%, while RRC with modified thermal deaerator showed the greatest potential in the short run with the highest return of investment (ROI) of 201.51% and shortest payback period (PBP) of 0.50 year.

Optimizing lean and rich Vapor compression with solar Assistance for post-combustion carbon capture in natural gas-fired power plant

The integration of post-combustion capture (PCC) CO2 technologies with power plants and high-emission industrial processes has become imperative for reducing carbon emissions and mitigating environmental impacts. This study focuses on addressing the significant energy consumption associated with PCC and explores the potential of using solar thermal energy to optimize configurations, thereby enhancing sustainability and reducing dependency on conventional power sources. Simulate various configurations under steady-state conditions using Aspen HYSYS V11 and Aspen Economic Evaluation software. The results indicate a substantial reduction in energy consumption to 2.1 GJ/ton CO2 after the initial optimization steps. The key innovations involve the integration of Lean Vapor Compression (LVC), Solvent Split Flow (SSF), Rich Solvent Recycle (RSR), and Rich Vapor Compression (RVC). Further optimization was conducted by integrating a reboiler with parabolic trough collectors (PTC), to reduce energy penalties. The LVC + RVC + RSR + SSF + PTC configuration results reveal a notable improvement in exergoeconomic factors, ranging from 3.13% to 7.8%, highlighting the economic feasibility of the optimized configurations. Simultaneously, the energy penalty decreased by 11.1%, signifying enhanced sustainability in the proposed PCC system. This study contributes to the gap in research by demonstrating the efficacy of solar thermal energy in optimizing PCC configurations, providing a pathway towards more sustainable and energy-efficient solutions for carbon capture in high-emission industrial sectors. The findings offer insights for researchers, engineers, and policymakers aiming to address the pressing issue of carbon emissions from industrial processes.

Liquid carry-over control using three-phase horizontal smart separators in Khor Mor gas-condensate processing plant

AbstractLiquid carry-over phenomenon, where liquid droplets escape with gas, is a prevalent operational challenge in gas–liquid separators. Liquid carry-over leads to foaming issue and performance reduction within separator downstream absorption processes. In order to ascertain whether liquid carry-over is inducing foaming within the gas sweetening process’s absorption tower at Iraq’s Khor Mor gas-condensate processing plant, this study initiated an evaluation of the liquid droplet size distribution in the gas product and gas/liquid separation efficiency of upstream Alpha, Bravo #1, Bravo #2, and Charlee separators that feed into the aforementioned tower by using the Horizontal Vessel and ProSeparator correlations in Aspen HYSYS v.11 software. The study revealed that Alpha and Bravo #2 experienced liquid carry-over issues. In response to these challenges, an innovative smart separator design, referred to as an adjustable separator, was proposed, employing the Arnold-Stewart semi-empirical procedure. This design allows for easy adjustment to accommodate changes in feed composition and operational conditions, mitigating the liquid carry-over problem. The smart design demonstrated a significant increase in gas/liquid separation efficiency for Alpha separator, improving by 31.11% under current conditions and 77.65% under future conditions. Similarly, under both current and future conditions, the smart design of Bravo #2 separator yielded an enhancement of 21.31% and 28.23%, respectively. These results suggest that the smart design can be considered an optimal solution for controlling liquid carry-over. This innovation not only maintains high gas/liquid separation efficiency but also prevents foaming, offering a robust solution for ensuring sustained operational performance and productivity in gas–liquid separation processes.

Improving Heat Efficiency of Aniline Production Process by Modifying Heat Transfer Fluid Looping System in Heating and Cooling Process

The nitrobenzene hydrogenation process is a highly exothermic reaction which causes the product temperature will increase and then needs to be cooled down with a heat exchanger. The modification process is needed to improve the heat efficiency of the nitrobenzene hydrogenation process by utilizing the heat from reactor product. Heat transfer fluid is used as a medium of heat transfer for both heating and cooling, so that will create a looping heat transfer process. The looping system of heat transfer fluid receives heat from reactor product and then reuses the heat to reheat the recycled hydrogen and for other utilities, so additional energy for these processes is not required. The process modification was simulated using Aspen HYSYS and the comparison of heat efficiency between the basic and the modified process is calculated using the net-energy formula. The results obtained that the net-energy (NE) value for both basic and modified process is 96,714,359.832 kJ/h and 9.869 kJ/h. This shows that the modified process has better energy efficiency compared to the basic process as the net-energy value is closer to zero. Therefore, this modification increases the heat efficiency of the aniline production through nitrobenzene hydrogenation process. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Reducing Energy Consumption in the Synthesis of Dimethyl Ether (DME) from Methanol Dehydration by Modifying Heat Transfer Unit Using Aspen HYSYS

In the pursuit of a transport sector free from fossil fuels, the incorporation of Dimethyl ether (DME) emerges as a commendable ecological substitute. The DME is a synthetically produced serves as a viable alternative to conventional fuels such as diesel and liquified petroleum gas (LPG). The Dimethyl Ether (DME) production is carried out by catalytic dehydration of methanol over an acidic zeolite-based catalyst. The technological process for the DME synthesis was simulated using Aspen HYSYS based on the combined operating parameters of the reaction dynamic model for the methanol dehydration reaction. This paper attempts to evaluate a modification in dehydration of methanol process to reducing energy consumption by modifying heat transfer unit. The heater and cooler heat transfer units were converted into heat exchangers (HE) by utilizing the output of the process that can be used for other processes so that energy consumption is reduced. The temperature of reaction and the heat transfer unit are modified to reduce energy consumption from 11.850 MMBtu/h to 7.6291 MMBtu/h by changing one heater and one cooler with two heat exchangers. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Improving Energy Efficiency with Heat Exchanger and Optimizing Operating Conditions for Sorbitol Production from Dextrose

Sorbitol is a sugar alcohol which has a molecular formula of C6H14O6. The production of sorbitol by the hydrogenation process is carried out at high pressure so that a large amount of energy is required. The use of considerable energy in the production process supports the need for innovation for energy efficiency in the production of sorbitol from dextrose, which is expected to help increase sorbitol production so that it can meet market needs. The innovation carried out here is to change the operating conditions with the help of Ru/ASMA@AC catalyst so that the temperature required during the reaction is low. In addition, modifying the use of heat exchanger units so that the heat generated during operation is reused. These innovations were simulated using Aspen HYSYS software. The results of the simulation proved to be able to improve energy efficiency by reducing the performance of compressors and coolers used during production and saving considerable energy. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

THERMODYNAMIC CONSIDERATIONS FOR THE APPLICATION OF LIQUEFIED NATURAL GAS AS TRANSPORTATION FUEL

This study aims to analyze the thermodynamic considerations for the application of liquefied natural gas (LNG) as a transportation fuel. It is designed to solve transport fuel problems and tackle public health issues associated with vehicular engine emission, and vehicle fuel shortages. It considers the economic and technical viability of applying low-temperature fluid as fuel in an LNG-fuel Spark Ignition Engine through computer simulation. Process design configuration was achieved using the User Define Function (UDF) in fluid simulation software (ANSYS Fluent), Finite Element Analysis and Aspen Hysys. 12 kg capacity volume cryogenic tank design was done through Autodesk inventor with an initial temperature of -162 0C and pressure of 1 bar and ambient temperature application ranges of 20 0C to 40 0C. The ANSYS simulation result showed a thermodynamic effect of heat and mass transfer rate range of 1.6% to 7% W/m 2k concerning the time taken by the tank to absorb heat through temperature ranges of -155 0C to 250 0C. Also, 0.04kg/s was the total volume of boil-off gas (BOG) produced, while the maximum exhaust gas emissions recorded was 0.254kg/s. The advantage of this technology was the minimal volume of greenhouse gases released during combustion relative to gasoline and diesel, signifying less pollution, and buttressing the applicability and adoption of LNG-Fuel as a transport fuel.

High ethane content enables efficient CO2 capture from natural gas by cryogenic distillation

Cryogenic CO2 capture technique is superior to other CO2 capture methods in terms of potential environmental protection, because it uses no chemical solvent or membrane materials that need regular replacement. However, the freezing problem of CO2 makes it difficult to achieve CO2 capture through simple gas–liquid separation, and the existing cryogenic CO2 capture techniques often require a specially designed separation equipment. In addition, cryogenic CO2 capture is generally considered a method with high energy consumption due to the need for refrigeration. This study proposes a cryogenic distillation-based CO2 capture method for natural gas with high ethane content, such as shale gas and oilfield associated gas. This method utilizes the azeotropic characteristics of CO2 and ethane to avoid CO2 freeze-out and the energy consumption for CO2 capture is minimized through process integration with natural gas liquefaction and ethane recovery. The proposed system uses the propane precooled mixed refrigerant process to provide the required refrigeration, and it is simulated in Aspen HYSYS and optimized by a combined method of a genetic algorithm and sequential optimization. The results show that the proposed process can remove up to 17 mol% of CO2 to less than 50 ppm. With extractive distillation, 99.50 % of the ethane in the feed gas is recovered with a purity of 99.50 mol%. The optimal results corresponding to the maximum allowable CO2 content to avoid its freeze-out (with a solubility margin of 500 ppm) show that when ethane content is 2–20 mol%, the specific power consumption of the system is about 0.43 kWh/Nm3(NG) with an exergy efficiency higher than 50 %. The proposed process achieves energy-saving and efficient CO2 capture method for natural gas with high ethane content.

A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO[formula omitted]

The stability and control of process industries have historically faced obstacles due to inherent uncertainties in their operations. This work focuses on leveraging machine learning models as a surrogate to facilitate real-time optimization of operating conditions of the direct hydrogenation of CO2 to enhance methanol production while minimizing exergy losses. Numerous studies have explored steady-state exergy analysis and the impact of various process conditions on methanol production. However, there is a notable absence of research investigating the influence of exergy destruction on methanol production under dynamic conditions. Initially, a commercial software Aspen HYSYS was utilized to design, model, and simulate the methanol synthesis plant. Exergy analysis was conducted to quantify the process exergy losses, exergy efficiency, and potential areas for improvement. The process model transitioned to a dynamic mode by incorporating ±5% uncertainty into critical operating conditions, i.e., temperature, pressure, and molar flow rate, simulating real-world variability and resulting in a dataset of 370 samples. Two machine learning models; the Gaussian process regression (GPR) and artificial neural network (ANN) model were developed using the data samples to predict the process exergy losses and molar flow rate of methanol produced. To enhance the predictive capabilities of these deployed models, Bayesian optimization was employed for hyperparameter tuning. The developed models were employed as surrogates in multi-objective genetic algorithm (MOGA) environments with the aim of maximizing the production of methanol with minimum exergy losses under uncertainty. The optimized process conditions, derived from MOGA-based methods, underwent cross-validation using the Aspen model. This analysis revealed that the methanol synthesis plant exhibited an exergy loss of 2425 kW, an exergy efficiency of 97.78%, and an improvement potential of 53.74 kW. The GPR and ANN models demonstrated high correlation coefficients (R2) of 0.9928 and 0.9375, and root mean square error (RMSE) values of 38.93 and 1.122, respectively. Incorporating these machine learning models as surrogates within the optimization frameworks notably outperformed the base case, achieving maximum methanol production while minimizing exergy losses in the process.

Artificial neural network modelling of natural gas dehydration process

A multi-input–multi-output artificial neuron network (MIMO-ANN) model has been developed for process monitoring and improvement on a natural gas glycol dehydration process. The MIMO-ANN model was based on a steady-state process simulation model constructed in commercial software Aspen HYSYS. A set of training data was generated with the converged simulation model for the training of the MIMO-ANN model in Python. The process input of the model includes lean glycol recirculation rate and purity along with wet gas inlet pressure. On the other hand, the process output considered includes dehydrated gas water and aromatics content, dehydrated gas hydrate formation temperature and water dew point, stripping gas flow rate as well as reboiler duty. The overall mean squared error (MSE) of the MIMO-ANN model was calculated as 1.79. The best-fit line that highly overlaps with a 45° diagonal line is constructed with a correlation coefficient (R2) score of more than 0.999 for all studied process output testing datasets. The mean absolute percentage error (MAPE) of the predicted outputs is generally less than 1%, except for dehydrated gas water dew point (16.63%) and stripping gas flow rate (11.55%), due to error predictions attempted on pseudo-zero values. This successfully displays an exceptional predictive performance of the MIMO-ANN model developed in this work which can be further deployed as an online dashboard for real-time monitoring tool.

Computer-aided framework for the design of entrainer for palm process residue recovery

Oleochemical industry produced substantial palm process residue during hydrogenation process of fatty acid/methyl ester, containing medium- to long-chain fatty alcohols and alkanes with overlapping boiling points. Recovering fatty alcohols for sale and utilizing alkanes for jet fuel, lubricants, and gasoline is beneficial. However, separating them through conventional distillation (CD) is challenging and expensive due to the difficulty in achieving the necessary temperature differentials for phase changes, especially with high reflux ratio required. To address this challenge, extractive distillation (ED) with an entrainer, designed through computer-aided molecular design (CAMD), was employed. However, implementing ED with a high boiling entrainer is challenging due to the high boiling point of the target components. Therefore, the design of the entrainer focused on the relative volatilities (α) of involved components and associated utility costs. Then, Aspen HYSYS was employed to evaluate the performance of the designed entrainer through product purity and economic analysis. For the separation of C10OH and n-C12, octane and dimethylcyclohexane were generated as entrainers. Dimethylcyclohexane entrainer demonstrated superior separation and generated higher profit than octane entrainer. For the separation of C12OH and n-C14, n-C15 was generated as entrainer to replace the infeasible CD. Therefore, the study concluded that ED with tailored entrainer can effectively replace CD for separating fatty alcohols from alkanes, demonstrating significant industrial feasibility.

Prediction and optimisation of gasoline quality in petroleum refining: The use of machine learning model as a surrogate in optimisation framework

AbstractHardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery.

Post-combustion carbon capture process modeling, simulation, and assessment of synergistic effect of solvents

Post-combustion carbon capture appears to be a promising solution to reduce the emission of carbon dioxide (CO2) from power plants that generate electricity using either coal or natural gas. In addition, carbon capture process efficiency, capacity, and energy consumption have become challenging against the performance of the capture process. However, synergistic effect due to solvents blend has gained attention to reduce the process energy consumption and enhance process efficiency. In this study, blends of methyldiethanolamine (MDEA) and piperazine (PZ) at different concentrations were investigated using a validated post-combustion capture process model using Aspen HYSYS. Results were compared with 30 wt% monoethanolamine (MEA) as reference case. The effective process variables are concentration of solvents, the amount of water and solvent in the makeup section, viscosity of solvent, energy consumed in different process stages, and the amount of lean solvent flow rate. These variables were studied against fixed process variables using rate-based model. The study shows that using (43 wt% MDEA/7 wt% PZ) for post-combustion carbon capture needs 2.53 MJ/kgCO2 regeneration energy for 88.5% process efficiency compared to 4.003 MJ/kgco2 for 30 wt% MEA without the need for any process modifications. In addition, it was found that solvents synergistic effect contributes to resolving the drawbacks of post-combustion capture that will enable the high utilization of the process and contribution to reduce the consequences effect of climate change. Therefore, the study will help policymakers, industries and encourage researchers towards the large-scale commissioning of blended solvent -based post-combustion capture process.

Plant wide modelling and thermodynamic optimization of a petroleum refinery for improvement potentials

In this work, plant wide modelling of a petroleum refinery was conducted for the optimization. An Aspen HYSYS® model of a petroleum refinery was used for collection of the information to perform the thermodynamic (exergy) analysis. The plant wide model consisted of alkylation unit, reformer, naphtha hydrotreater, catalytic cracker, diesel hydrotreater, isomerization unit, hydrocracker, and kerosene hydrotreater. Seventeen (17) independent input streams and twenty-eight (28) output streams of the plant were considered for the analysis. The physical and chemical exergies of the streams were summed for assessing the overall exergy efficiency, exergy destruction, and improvement potentials of the plant. Exergy efficiency of the plant was 91.38 % with exergy destruction of 704054.64 kW and exergetic improvement potential of 60707.50 kW. The current study gives an insight into the plant-wide analysis ranging from physical exergies of the process streams and utilities to the chemical exergies of the fuels (product) produced in the refineries.

Strategic CO2 management in the energy and petroleum sector for the production of low-carbon LNG: A Qatar Case Study

The imperative to mitigate industrial CO2 emissions amidst global climate change has led to the development of innovative strategies that align economic growth with environmental sustainability. This study introduces a comprehensive CO2 allocation and utilisation framework, designed to reduce the environmental impact on the production of Liquefied Natural Gas (LNG). Demploying a multi-objective Linear Programming (LP) approach, the study introduces a system that dynamically allocates CO2 from a major source to several sinks, each with unique characteristics and requirements. Focusing on the State of Qatar, a major player in the global LNG market, advanced simulation tools, such as Aspen HYSYS and QASR Simulator are utilised to define the parameters of these sinks, ensuring precise and efficient CO2 allocation. An integral component of the model is the incorporation of a carbon tax, considered for both the source and the sinks. The results demonstrate that the profit maximisation objective generated a $23.6 billion annual profit, although with high emissions of 23.37 Mt/year. In contrast, the emission minimisation objective curbed emissions to 19.01 Mt/year at a profit of $1.4 billion. The multi-objective approach garnered $18.6 billion annually with emissions at 22.37 Mt/year. The CO2/LNG metric was used to gauge LNG's environmental footprint. Objective 1 yielded a ratio of 0.3033, while objective 2 achieved the best score at 0.2463. The multi-objective approach balanced both, with a ratio of 0.2905. These findings illuminate the feasibility of optimising industrial practices for producing low-carbon LNG through deliberate CO2 allocation and within a circular economy framework.

Dynamic Modelling and Simulation of A Three-Phase Gravity Separator

Many studies have investigated the crude oil separation process’s separation mechanisms, size, and design, employing horizontal 3-Phase Gravity Separators in depth. There are, however, very few articles on their dynamics, modelling, and simulation. Understanding its dynamic behaviour will aid in designing and tuning the device that can manage water level, oil level, and gas pressure in response to feeding variations. This Scientific Paper gives a complete mathematical analysis, modelling, and simulation of a crude oil separation process using a horizontal 3-Phase Gravity Separator using Mathworks Matlab R2016b-x64 and Aspen Hysys V10. Bishoy’s Equations, which were constructed, will assist in operating this gadget, locating various variables, and observing the effect of modifying variables on the system’s variables. The rationale for this study was developed in response to the small number of articles discovered, which may be a covert issue held up by large oil companies, as well as the complicated equations related to this process that remain unsolved, and to monitor what is happening in this complex dynamic process. As a result, this Research Paper is unique and novel, and it can be compared to the work done by some authors who did not solve the obtained differential equations and did not go further in Aspen Hysys Modeling and Simulation, whereas this paper provides everything related to a three-phase gravity separator, including changing of variables and observing the effect on the system when those variables were modified. Furthermore, separators are designed with internal baffles to promote laminar flow and increase separator efficiency. However, it has been assumed that there are no baffles here, which is a significant problem, but with the help of these equations, the horizontal three-phase gravity separator can be operated at its maximum efficiency. The equations determined the following variables: The height of gas, water, oil, the height of oil when it jumped the weir, the pressure of the gas (in and out), water pressure (in and out), oil pressure (in and out), and the effect of increasing (control valve’s stem position) and decreasing Qin (inlet volumetric flowrate) on these variables have all been studied. This article discovered that increasing the control valve stem position and decreasing the inflow volumetric flowrate of both oil and water was highly unsafe and caused significant variations in the system’s heights and pressures using Matlab. The Aspen Hysys analysis optimally separates the oil, gas, and water to determine material, energy streams properties, and compositions. As a result, this complex dynamic behaviour was observed, and no additional articles were discovered that addressed this subject. This process monitoring will determine the best conditions for flawless separation, with the selectivity of the desired product or products as the primary goal. This research can revolutionize the way people think about oil and gas extraction and processing and benefit colossal oil and gas firms in Europe, Asia, and Africa.

Onboard pre-combustion carbon capture with combined-cycle gas turbine power plant architectures for LNG-fuelled ship propulsion

This paper investigates the concept of pre-combustion carbon capture (Pre-CCS) with a combined cycle gas turbine (CCGT) propulsion system to achieve energy efficient carbon capture onboard a Liquid Natural Gas fuelled vessel. A basic CCGT model with energy efficiency of 51.6 % when using LNG as fuel was integrated with a Pre-CCS system composed of a steam methane reformer, water–gas shift reactors, pressure swing adsorption (SMR-PSA), and liquid CO2 storage. Various waste heat utilisation schemes integrated with the CCGT-reformer were modelled in Aspen HYSYS. The modelling of the waste heat recovery networks as energy streams integrating between various processes and unit of operations has facilitated a closed-loop simulation of various systems. It was concluded that a proper utilisation of the high-grade heat from the gas turbine (GT) exhaust and from the reformer is crucial to improve the overall energy efficiency and carbon capture rate, exemplified by a scheme where the heating needs for the SMR and the cooling needs from the water–gas-shift reactors are fully integrated. When optimised, the integrated system had an overall energy efficiency of 41.5 % and 43.2 % (i.e. an efficiency loss of 10.2–10.5 percentage points from the base CCGT system) accompanied with specific GHG emission reduction of 51.2 % and 52.3 % for operation of the GT at a turbine entry temperature of 1700 K and 1850 K, respectively. Intermediate levels of reforming, where the GT uses a mix of LNG and reformate gases, were also simulated, showing an almost linear evolution from the 100 % LNG feed to the GT to the 100 % reformate case. Such a strategy could be used for gradual evolution to meet the required CO2 reduction timeline. It was determined that the 100 % reformate case provides the highest CO2 capture rate. Estimates of other emissions from the GT assuming typical combustor residence times for the whole range of fuel blends were also carried out, showing that the overall greenhouse gas emission is dominated by CO2, given that the GT emits negligible N2O and unburnt methane. Some capital cost estimates were also made to determine the unit price of hydrogen fuel produced onboard, suggesting that the CAPEX was not prohibitive. These findings support the pre-combustion CCS − CCGT integrated system as a promising long-term decarbonisation and propulsion strategy to comply with emissions regulations, displaying good performance in terms of cost, energy consumption, and CO2 reduction. The scalability and adaptability of the proposed system for existing and new-built ships across the decarbonisation timeline is also discussed.

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

Study on a novel hydrogen liquification process applying mixed-refrigerant for pre-cooling and cryogenics

Reducing the energy consumption of hydrogen liquefaction process is an urgent issue to improve the economy of liquid hydrogen transportation. A novel large-scale hydrogen liquefaction system, with a capacity of 100 TPD, is proposed in this paper. The pre-cooling system is composed of a mixed-refrigerant (MR) refrigeration cycle and a CO2–NH3 cascade refrigeration cycle. Due to the auxiliary pre-cooling effect of the CO2–NH3 cascade cycle, temperature of the pre-cooled hydrogen can be as low as −200.9 °C. A modified MR Joule Brayton cycle with an additional compressor is used as the cryogenic system to cool the hydrogen from −200.9 °C to −252.2 °C. Besides, a four-stage ortho-to-para conversion (OPC) process is utilized. Performance of the novel system is investigated and optimized using the Aspen HYSYS software. According to the results, the lowest specific energy consumption (SEC) of the proposed process is 6.15 kWh/kgLH2 and the exergy efficiency is as high as 67.4%. Based on the energy analysis, the best system coefficient of performance is 0.1751 with a 96.3% of p-H2 in the liquid hydrogen. In addition, compared to the initial conceptual system, the proposed system exhibits significant performance enhancements, a 20% reduction in SEC and a 27.9% increase in exergy efficiency. A sensitivity analysis is conducted to assess the impact of refrigerant composition and hydrogen pressure on the system, resulting in the determination of the optimal proportions of refrigerant components and the optimal inlet pressure. Results of the study can provide theoretical reference for the further development of large-scale hydrogen liquefaction technology.