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In this study, a novel boil-off-gas handling process was proposed to optimize refrigerant mix and flow rate considering different boil-off gas rates under different weather conditions. The proposed design utilizes a nitrogen expander and mixed refrigerant cycle to generate subcooled liquified natural gas to cool the tank, preventing boil-off gas generation. The optimized design in terms of exergy destruction minimization is assessed in a multiple steady-state simulation based on seasonal boil-off gas and heat ingress rates which are predicted by a machine learning algorithm. Furthermore, an economic analysis of the proposed reliquefaction processes was conducted. The result shows that the conventional process has 46% and 8% lower exergy destruction compared to the proposed one for the nitrogen expander and mixed refrigerant cycle respectively. However, the proposed process outperforms the conventional process in terms of investment costs. The proposed process has 36% and 27% lower investment costs for the nitrogen and mixed refrigerant cycle compared to the conventional one. This leads to higher profit for the novel process and performs better in the face of uncertainty of liquified natural gas price. [Display omitted] • This study proposed a novel BOG handling process for minimizing capital costs. • This study offers BOG handling using subcooled LNG refrigerated by MR and N 2 cycle. • Utilizes ML for predicting BOG and heat ingress rates in varying weather conditions. • The study lowers capital costs by 36% and 27% compared to the traditional approach. [ABSTRACT FROM AUTHOR]

Landfill gas accounts for 18% of US greenhouse gas emissions. The energy wasted via venting/flaring methane in landfill gas can be valued at 7.5 billion USD (annually). This work presents a novel utilization concept, wherein landfill gas-powered refrigeration enables large-scale atmospheric water harvesting, via dehumidification. This work analyzes the potential of landfill gas-powered atmospheric water harvesting towards meeting the water requirements of oilfields located near landfills. Heat and mass transfer-based analytical modeling is used to estimate the seasonal water harvest, and techno-economic analyses are presented to quantify the benefits for US oilfields. This technology is seen to be attractive for the Barnett Shale (Texas) and Kern County (California), which can be served by 30 landfills each, and are located in hot-humid and water-stressed areas. Results show that landfill gas-powered water harvesting can meet 34% of water requirements (hydraulic fracturing) in the Barnett Shale and 12–26% of water requirements (enhanced oil recovery) in Kern County oilfields, respectively. Landfill gas projects are economically more viable in the Barnett as compared to Kern County. The impact of landfill gas-powered water harvesting on CO 2 e emissions from landfills is quantified. Constraints and challenges associated with water harvesting are discussed. Importantly, this waste-to-value concept has worldwide relevance since landfills co-exist with population centers. [ABSTRACT FROM AUTHOR]

This paper describes the results of the ejector refrigeration cycle using R600 as a working fluid. The evaporator, generator and condenser are assumed as heat exchangers that exchange heat with three external fluids. The evaporator heat capacity is fixed at 5 kW. Effects of temperature difference in the heat exchangers (ΔT) and generator pressure (P g ) on the coefficient of performance, generator and condenser heat rates, ejector entrainment ratio and the pump work are investigated. Engineering equation solver (EES) software is used for calculating the refrigerant properties. A computer program has been written in MATLAB environment is using neural network toolbox and genetic algorithm. New formulation obtained from ANN for this cycle is presented for calculating the target values. Accuracy of ANN model in terms of the root absolute fraction of variance (R) and the mean squared error (MSE) are evaluated. Also Particle Swarm Optimization (PSO) and Ant Colony Optimization ( ACOR) are used to find the maximum values of cycle performance. [ABSTRACT FROM AUTHOR]