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[Display omitted] • Cryogenic CO 2 capture and storage (CCS) reduces power generation efficiency penalty. • Power consumption during natural gas liquefaction decreases by up to 67.17%. • Liquefied natural gas (LNG) supply chain with cryogenic CCS reduces energy waste. • Natural gas liquefaction and regasification are integrated based on cryogenic CCS. • The exergy efficiency in the LNG supply chain is increased from 18.18 to 46.39%. Natural gas combined cycle power plants generate substantial amounts of CO 2. Also, since natural gas is produced in limited sites, the liquefied natural gas (LNG) supply chain consumes considerable energy in transporting natural gas overwide. Herein, advanced natural gas liquefaction and regasification processes using an LNG supply chain with cryogenic CO 2 capture and storage (CCS) are proposed using Aspen Plus® V11. This solves three significant problems associated with conventional LNG supply chains. First, through the utilization of LNG cold energy, the cryogenic CCS process reduces the efficiency penalty of power generation from 14.34 to 4.45%. Second, solid CO 2 from cryogenic CCS is used as an additional cold source in the natural gas liquefaction process by conveying it to the liquefaction site via the returning LNG ship. As a result, the power consumption in the refrigerant cycle is reduced by up to 67.17% than single mixed refrigerant (SMR) process. Finally, the LNG supply chain with cryogenic CCS minimizes the sustained energy waste between the natural gas liquefaction and regasification stages owing to the geographical separation. LNG cold energy from the regasification stage is utilized in the form of solid CO 2 in the CCS and liquefaction stages. Consequently, the exergy efficiency in the overall LNG supply chain increases from 18.18 to 46.39%. The findings of this study can potentially provide environmentally friendly design guidelines for various LNG processes that use LNG cold energy in CCS and natural gas liquefaction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Amine CCS process is an energy-intensive system, increasing efficiency penalty. • Cryogenic CCS process is proposed to reduce efficiency penalty of NGCC power plant. • LNG cold energy is utilized to capture and store CO 2 in the solid phase. • The efficiency penalty is reduced from 14.34% to 3.51%. This study proposes a novel cryogenic CO 2 capture and storage (CCS) process using liquefied natural gas (LNG) cold energy in a natural gas combined cycle (NGCC) power plant. This study makes two major contributions to the literature. First, the cryogenic solid-phase CCS process using LNG cold energy can effectively reduce the efficiency penalty in NGCC power plants by solving the fundamental problems associated with the conventional CCS process: energy-intensive thermal treatment of the monoethanolamine-based absorption process and significant power consumption for compressing CO 2 to 150 bar. Second, the cryogenic CCS process requires minimal equipment installation in the NGCC power plant by integrating LNG cold energy utilization and CO 2 capture processes. The proposed cryogenic CCS process reduces the efficiency penalty from 14.34 % to 3.51 %, with a 99.93 % CO 2 capture rate. We believe this study will provide a novel guideline for reducing the efficiency penalty by overcoming the challenges of the conventional CCS process. [ABSTRACT FROM AUTHOR]

Power-to-methane (PtM) coupled with renewables requires an energy buffer to ensure a steady and flexible operation. Liquid CO 2 energy storage (LCES) is an emerging energy storage concept with considerable round-trip efficiency (53.5%) and energy density (47.6 kWh/m3) and can be used as both an energy and material (i.e., CO 2) buffer in the PtM process. Integration of LCES with the PtM process realizes co-production of methane and electricity, supports peak shaving of the power grid, and enhances profitability via the sale of electricity at peak hours. This study aims to design, optimize, and comprehensively evaluate the techno-economic performance of the PtM-LCES process using a renewable power mix of solar and wind. A process scheduling model is formulated to understand the impacts of storage sizing and power allocation on the process performance. Then, artificial neural network-based surrogate optimization was performed to establish a cost-optimal design. Investigation at Kramer Junction, California, found that the production cost of methane was 161.6 €/MWh with an efficiency of 76.2% and a renewables penetration of 78.7%. This cost could be reduced by improving LCES's performance and considering electricity price arbitrage in areas with high electricity prices. The electricity price arbitrage and improvement of LCES's performance could lower the cost to 83.8 and ∼60 €/MWh, respectively. Moreover, zero or negative carbon emissions can be achieved if the renewables penetration can be increased to 91.5%. Results indicate that the PtM-LCES process is both energy efficient and economically viable, through which renewable methane can be cost-competitive with fossil natural gas. • Power-to-Methane coupled with renewables via liquid CO 2 energy storage. • Process flexible operation achieved by a bidirectional connection with the grid. • Energy- and cost-efficient production of methane in a continuous manner. • Costs of methane in different scenarios range from 60 to 161.6 €/MWh. [ABSTRACT FROM AUTHOR]

• DNN data-driven model for steam methane reforming is developed. • Data preprocessing techniques were applied to improve the quality of datasets. • Four hyperparameters were optimized to obtain an accurate prediction model. • Process optimization was conducted with 387,320,489 cases under five constraints. • The operating conditions were optimized to achieve a thermal efficiency of 85.6%. The production and application of hydrogen, an environmentally friendly energy source, have been attracting increasing interest of late. Although steam methane reforming (SMR) method is used to produce hydrogen, it is difficult to build a high-fidelity model because the existing equation-oriented theoretical model cannot be used to clearly understand the heat-transfer phenomenon of a complicated reforming reactor. Herein, we developed an artificial neural network (ANN)-based data-driven model using 485,710 actual operation datasets for optimizing the SMR process. Data preprocessing, including outlier removal and noise filtering, was performed to improve the data quality. A model with high accuracy (average R 2 = 0.9987) was developed, which can predict six variables, through hyperparameter tuning of a neural network model, as follows: syngas flow rate; CO, CO 2 , CH 4 , and H 2 compositions; and steam temperature. During optimization, the search spaces for nine operating variables, namely the natural gas flow rate for the feed and fuel, hydrogen flow rate for desulfurization, water flow rate and temperature, air flow rate, SMR inlet temperature and pressure, and low-temperature shift (LTS) inlet temperature, were defined and applied to the developed model for predicting the thermal efficiencies for 387,420,489 cases. Subsequently, five constraints were established to consider the feasibility of the process, and the decision variables with the highest process thermal efficiency were determined. The process operating conditions showed a thermal efficiency of 85.6%. [ABSTRACT FROM AUTHOR]

• Novel waste energy recovery process for carbon capture and storage was developed. • Waste cold and hot energy from liquefied natural gas plant is recovered. • Net power generation can increased by 16% compared to base process. • Net profit can increased by 75% compared to base process. In an liquefied natural gas (LNG) power plant, the amine scrubbing and CO 2 liquefaction process are generally employed for CO 2 capture and storage (CCS) because it is suitable for large-scale plants. However, a large amount of hot and cold energy is required for CO 2 absorption, regeneration and liquefaction. As a solution, the waste LNG cold energy from the LNG regasification process and waste hot energy from natural gas combined cycle (NGCC), which are generally disposed into the seawater, can be recovered and utilized for the abovementioned purposes. Hence, this study suggested a novel process for waste hot and cold energy recovery of the LNG power plants for CCS. The suggested process model consists of the following four steps: LNG regasification, natural gas combined cycle, CO 2 capture and regeneration and CO 2 liquefaction. In the process model, the waste LNG cold energy is recovered at the lean amine cooler in the CO 2 capture process and each heat exchanger in the CO 2 liquefaction process. Furthermore, for CO 2 regeneration, the waste hot energy from NGCC is recovered at the stripper reboiler. The exergy and economic analyses were addressed to evaluate the economic feasibility of energy conversion of the proposed process. As a result, compared to the base process, the net power generation and exergy efficiency of the proposed process increased by 16% and 8%, respectively. In addition, the net profit of the proposed process increased by 75%, indicating high economic feasibility. The overall energy efficiency and economic feasibility using waste cold and hot energy were observed to increase, which resulted in decreased fuel usage. Therefore, we believe that the proposed approach can contribute significantly to the economic improvements and environmental protection efforts. [ABSTRACT FROM AUTHOR]