Single Mixed Refrigerant Research Articles

Single mixed refrigerant biomethane liquefaction plant integrated with solar energy: Dynamic simulation for the decarbonization of the heavy road transport sector

This works proposes a dynamic thermoeconomic analysis of a liquefied biomethane production plant to meet the fuel demand of a fleet of heavy duty trucks in the south of Italy. The biomethane is obtained from the upgrading of the biogas produced by means of anaerobic digestion through a plug flow reactor fed by organic fraction of municipal solid waste. The upgrading of the biogas is realized using a three-stage membrane compression process, producing a 96 % pure biomethane. The biomethane liquefaction is realized using a single-mixed refrigerant process and compared to a Linde cycle process. The whole system is assisted by solar energy to reduce the fossil energy consumption of the process and feed-in tariffs are considered as funding policy. The models for the anaerobic digestion, the biogas upgrading, and the biomethane liquefaction are in detail developed in MatLab. The anaerobic digestion model is based on the ADM1 biological model, integrated with a suitable heat transfer model. The biogas upgrading model is based on a simplified Fick model. The liquefaction model is based on an equivalent two heat-exchangers model, taking into account the transient heat transfer. All the components are then integrated in TRNSYS to perform the dynamic simulation for one operating year of the whole system. Results from the thermoeconomic analysis are outstanding in terms of profitability, showing a payback period of less than 2 years and a Net Present Value of the system of 402 M€. The great environmental impact is also confirmed by a Primary Energy Saving of 91 % and a dramatic reduction of 86 % of the CO2 equivalent emissions.

Transcritical [formula omitted] precooled single mixed refrigerant natural gas liquefaction process: Exergy and Exergoeconomic optimization

This study proposes a two-stage transcritical CO2 pre-cooled single mixed refrigerant process to investigate the potential improvement in exergy efficiency and exergoeconomic performance. Two different objective functions, namely maximizing exergy efficiency and minimizing the unit cost exergy of LNG, were optimized using the particle swarm optimization algorithm to compare the performance of the CO2 pre-cooled single mixed refrigerant process with single mixed refrigerant process. The results of the first objective function indicate a higher exergy efficiency of the CO2 pre-cooled single mixed refrigerant process, with an increment of 7.56%. This improvement can be attributed to not only the lower total power consumption but also the better matching of the temperature-difference composite curve, particularly the location of the pinch temperature at the warm end of the heat exchanger. Additionally, the exergy destruction of the CO2 pre-cooled single mixed refrigerant process was 30.12% lower than that of the single mixed refrigerant process. The second objective function demonstrated that the CO2 pre-cooled single mixed refrigerant process had a unit exergy cost value of 7.46 $/GJ, which was 11.98% lower than the single mixed refrigerant process. In conclusion, the proposed process contributes to energy saving throughout the LNG chain. Previous studies on the transcritical CO2 precooling cycle were limited to only one-stage configuration paving the way for thermodynamic efficiency enhancement by modifying the design of the process. This study also considers the effects of two objective functions maximizing exergy efficiency and minimizing the unit exergy cost of LNG on the optimization of decision variables which has not been discussed yet in the literature. Moreover, innovative exergoeconomic analysis presents a comprehensive criterion for evaluating economic performance, providing guidance for the initial construction and sustainable development of LNG plants.

A novel hydrogen liquefaction process using dual mixed cryogenic refrigeration system: Energy, exergy, and economic analysis

In conventional hydrogen liquefaction plants, the liquefaction process consumes high portion of about 30 % of the liquid hydrogen (LH2) energy content. In addition, existing LH2 technology uses pure or single mixed refrigerants, which are susceptible to freezing problems. To address these challenges, this study introduces an innovative hydrogen liquefaction process utilizing dual mixed refrigeration (DMR) system. This process incorporates two interactive mixed refrigerants in two integrated refrigeration loops, offering flexible capability to handle large-scale capacities with minimal energy consumption and free of freezing problems. The proposed process achieved extremely low specific energy consumption (SEC) of 3.732 kWh/kgLH2, 48 % lower than the average SEC of the large-scale single mixed refrigerant (SMR) systems (7.128 kWh/kgLH2). Also, its exergy efficiency (59.65 %) is 33 % higher than the average exergy efficiency of the SMR-based systems (44.89 %). Furthermore, the proposed DMR-based process reduces the levelized cost of liquid hydrogen production to 1.89 $/kgLH2, which is 70 % lower than the small-scale processes and 21 % lower than the average cost of the large-scale SMR-based systems. This study of the dual mixed cryogenic refrigeration approach provides new methodology and guidelines for future research to significantly improve the technical and economic feasibility of LH2 production, making it competitive with other energy storage and transportation options.

Enhancing Energy Efficiency and Reliability in Floating LNG Operation: A Hydrofluoroolefin-Based SMR Cycle with Thermo-Economic Assessment and Uncertainty Analysis

Natural gas liquefaction is the most energy-intensive technique in floating liquefied natural gas (FLNG) operations. Hence, a hydrofluoroolefin-based single mixed refrigerant (SMR) technology was proposed to achieve energy-efficient and eco-friendly operations. The study was perfomed to enhance the SMR process by substituting expansion valves with hydraulic turbines to improve process reversibility and enhance energy efficiency. The proposed process was optimized using a sine–cosine concept-based algorithm that tuned the design variables which yielded an overall compression energy consumption of 0.2501 (kWh/kg-LNG), resulting in a 21.43% saving as compared to the conventionally used mixed refrigerant-based process. This resulted in a reduction of about 38.81% in overall exergy destruction. The economic analysis results draw curtains that the process saves about 26.02% of total annualized cost as compared to conventional study. Owing to the appropriate mixed refrigerant component mixture ratio and process design improvement. To determine the process reliability under uncertain conditions the optimal compression power and minimum approach temperature were determined. Further, uncertainty analysis reveals that the methane and butane refrigerant flow rates along with evaporation, and condensation pressure, were among the most influential parameters that influence the process most that may affect the whole operation in uncertain process inlet conditions. In summary, our approach presents immense promise for achieving energy efficiency, environmental responsibility, and economic viability in FLNG operations, offering a robust solution for the challenges of offshore plants.

A novel small-scale biomethane liquefaction process: Assessment through a detailed theoretical analysis

The use of biomethane as fuel in the transport and power generation sectors is widely considered an effective tool to pursue the goals of carbon neutrality and energy security. Therefore, it is very important to promote not only the production and upgrading processes of biomethane from carbon neutral biomass, but also biomethane liquefaction processes, which can allow effective transportation of biomethane, thanks to the fact that the density of the liquid is higher than that of the compressed gas. Biomethane liquefaction plants can benefit from the technologies already used for the liquefaction of natural gas, but the size of biomethane liquefaction plants must be significantly smaller because of the limited quantities of biomethane generated by production and upgrading plants. Biomethane liquefaction plants have still limited diffusion; the most used technologies for the biomethane liquefaction are the Reverse Brayton cycle, the Lynde cycle, and the heat exchange with liquid Nitrogen. Single mixed refrigerant (SMR) processes, which are very effective for the liquefaction of natural gas, have a high potential for the biomethane liquefaction too, but the high specific energy consumption and high plant costs (mainly caused by typical multi-flow heat exchangers employed) are the major issues associated with SMR processes when applied to the very small size of biomethane liquefaction plants, characterised by very low biomethane flow rates. The aim of this paper is to propose a novel SMR process for biomethane liquefaction, which can overcome the above-mentioned issues, contributing towards the implementation of SMR processes for the liquefaction of biomethane and providing a valid alternative to currently used biomethane liquefaction plants; the proposed solution consists in using simple pipe-in-pipe heat exchangers in place of the more complex multi-flow heat exchangers. The validity of this solution is theoretically assessed using a detailed thermodynamic model based on well-stablished equations. Using this tool, it is shown that, by adjusting crucial parameters, such as the molar composition of the refrigerant, the heat exchange is satisfied with an affordable length of the heat exchangers, negligible pressure drops and good levels of the coefficient of performance with reference to such a small-scale application. The predicted specific energy consumption (accounting also for the biomethane compression and pre-cooling) for a biomethane flow rate of 0.03 kg/s is of the order of 0.8 kWh/kgCH4, which is a very good performance level compared to current commercial biomethane liquefaction processes.

Advanced natural gas liquefaction and regasification processes: Liquefied natural gas supply chain with cryogenic carbon capture and storage

Natural gas combined cycle power plants generate substantial amounts of CO2. 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 CO2 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 CO2 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 CO2 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.

Comparison of mixed refrigerant cycles for natural gas liquefaction: From single mixed refrigerant to mixed fluid cascade processes

Comparison of mixed refrigerant cycles for natural gas liquefaction: From single mixed refrigerant to mixed fluid cascade processes

Performance analysis of a boil-off gas re-liquefaction process for LNG carriers

With the growth of LNG demand and rapid rise in natural gas prices, it is of great significance to develop an efficient boil-off gas (BOG) re-liquefaction process for LNG carriers. This paper proposes a BOG re-liquefaction process using the single mixed refrigerant (SMR) cycle, and comprehensively compares it with the typical Mark III process. The processes were rigorously simulated with Aspen HYSYS, and performed process optimization in terms of specific energy consumption (SEC) as the objective function using the genetic algorithm method. Sensitivity and exergy analysis were also conducted to assess process variables and irreversibility. Furthermore, the processes were performed economic analysis including the total capital cost (TCC), total operating cost and total annualized cost (TAC). The results show that the SEC and major equipment exergy loss of the proposed process have 529.4 kWh/ton and 465 kW, which are 50.34% and 40.88% lower than the Mark III process. Additionally, the TAC for proposed process presents 4.07 million USD/year, compared to 6.62 million USD/year for the Mark III, resulting in a 38.55% annual savings.

Novel dual-mixed refrigerant precooling process for high capacity hydrogen liquefaction plants with superior performance

Liquid hydrogen is a superior alternative for the current energy storage methods and energy carriers as it has higher energy density and cleanliness. However, hydrogen liquefaction is an energy-intensive process. In particular, the precooling process of hydrogen consumes a tremendous portion of about 30 % of the total compression power of the plant. Several previous studies introduced various pure-refrigerant and single mixed refrigerant (SMR) precooling processes, however, their specific energy consumption (SEC) still very high especially at large-scale capacities. Therefore, this study presents a novel, efficient, and large-scale dual-mixed refrigerant (DMR) process to precool the hydrogen from 25 °C to -192 °C at a pressure of 21 bar. New heavyweight-based mixed refrigerant MR1 and lightweight-based mixed refrigerant MR2 are developed for the DMR process using a new-proposed systematic approach. The proposed DMR process is capable of handling a wide range of hydrogen flow from 100 TPD to 1000 TPD with SEC of 0.862 kWh/kgH2Feed, which is 20.33 % lower than the most competitive SMR process available in the literature. Based on the sensitivity analysis, further optimization of the DMR operating parameters reduced the SEC to 0.833 kWh/kgH2Feed at an optimal capacity of 500 TPD. Furthermore, the COP of the new process is improved by 14.47 % and the total annualized cost is reduced by 12.24 %. Compared to five other technologies that use the pure-refrigerant and other SMR precooling processes, the DMR reduces the SEC by 39.0 % to 63.0 %. The novel precooling process presented herein has the potential to drive the development of large-scale hydrogen liquefaction processes.

A New Re-Liquefaction System for Dynamic Boil-Off Recovery Based on Single Mixed Refrigerant

A New Re-Liquefaction System for Dynamic Boil-Off Recovery Based on Single Mixed Refrigerant

Process design and optimization of single mixed-refrigerant processes with the application of deep reinforcement learning

Deep reinforcement learning approach is considered for the design and optimization of SMR (Single Mixed Refrigerant) cycles with which the refrigeration power is minimized. Deep Q-Network (DQN) agent is programmed with Python®, which is interacted with a process simulator UniSim Design®, as an environment, through the delivery of decision with MATLAB®. The optimization goal is achieved by designing a reward function such that specific power requirement is minimized, subject to the constraint of minimum temperature difference for the heat transfer. The case study focuses on the design of the SMR cycle for the liquefaction of lean and rich natural gas feeds. GA (Generic Algorithm) optimization framework is also constructed and applied for the case study. Almost the same or similar computational performance between the DQN and the GA methods is observed for finding optimal solutions. For the case study, the specific power required for the liquefaction process is reduced by 15.7% and 13.4% through DQN optimization for the cases of lean and rich feeds, respectively, compared to base cases. The case study clearly demonstrates the applicability of the reinforcement learning which can effectively deal with the optimization problem having complex interactions and high nonlinearities.

Synthesis of N2-hydrocarbon refrigerant composition for maximum LNG production in PRICO processes

• Cryogenic technologies mitigate pollutants from NG flaring derived from fracking. • An optimized small LNG plant is a cost-effective approach to NG recovery. • Proposed physics-based approach optimizes LNG plant performance. • Application of thermodynamic principles contributes to accelerated convergence. • The proposed approach selects refrigerant mixture for optimal LNG production. Several technologies are being researched to address the challenges of cryogenic heat transfer in liquefied natural gas (LNG) production. Single mixed refrigerant (SMR) or dual mixed refrigerant (DMR) systems have advantages vs. pure refrigerant-based systems (e.g., cascade or inverted Brayton cycle with nitrogen) due to compact design suitable for small-scale units. However, the selection of mixed refrigerant composition is a challenge. Several methods have been published, typically using heuristic and computational techniques; with power consumption, efficiency, and/or exergy as the figure of merit. This paper presents an alternative approach for the determination of optimum refrigerant mixture composition based on nonlinear thermodynamic equations relating heat exchanger composite curves and mass flow of LNG produced. Three case studies demonstrate the effectiveness of the approach, indicating that single mixed refrigerant systems can be used for cost-effective LNG production while improving LNG production per unit of refrigerant flow rate by as much as 6.5% vs. conventional approaches. The research is applicable to current needs of the gas extraction industry because it helps increase LNG production in small-scale cryogenic systems, thereby enabling economic gas recovery and reducing flaring during fracking operation, as is typical in e.g., the Bakken formation of North Dakota.

A generalized disjunctive programming model for multi-stage compression for natural gas liquefaction processes

The primary driver of operating costs in natural gas processes is the energy consumption of the compression system. Multistage compression configurations are commonly employed and hence play a vital role in optimization of natural gas processes. In this study, a generalized disjunctive programming model for multistage compression is formulated. The model is useful for both synthesis and optimization of multistage compression configurations. By using this approach, we further seek improvements in shaft work savings. The model relies on thermodynamic equations and is designed to minimize the consumption of shaft work. The model is handled by employing the logic-based branch and bound algorithm, eliminating the need for explicit conversion into a MINLP, which in turn leads to improved convergence and faster computational performance. The model solution yields optimal pressure levels, and hence stage shaft work consumptions. A case study of multistage compression for a prior optimized single mixed refrigerant (SMR) process obtained from literature is used to test the proposed model. The model’s outcomes are validated through simulation using the Aspen Hysys software. Savings in shaft work of atmost 0.0088%, 0.4433%, and 1.2321% are obtained for the two, three, and four stage compression systems respectively against the optimized base cases from literature.

Energy and economic comparison of five mixed-refrigerant natural gas liquefaction processes

The demand for liquified natural gas grows as it is an energy resource that has more flexible means of transport than non-liquefied natural gas, and is more eco-friendly than other fossil fuels. This paper focused on comparing five different mixed-refrigerant liquefaction processes to determine which one is the most efficient in energetic and economic terms for four different natural gas processing scales (20,000 kg/h and 2, 4, and 8 MTPA of natural gas feed). Simpler processes with one or two refrigerant cycles (single mixed refrigerant, propane pre-cooled mixed refrigerant, and dual mixed refrigerant) and more complex processes with three cycles (mixed fluid cascade and AP-X) were analyzed. All processes were simulated in Aspen HYSYS, and the energy consumption was optimized by a communication between the simulator and an optimization algorithm based on Particle Swarm Optimization coded in MATLAB. The economic analysis was performed using the CAPCOST technique and the rule of six-tenths for upscaling. In the best-optimized scenario, the results show that the most complex process, AP-X, consumes the lowest amount of energy (0.2367 kWh/kg-LNG) while the simplest one, the single mixed refrigerant, consumes the highest value (0.2561 kWh/kg-LNG). In the economic analysis, the results indicate that energetically better processes may not provide a higher economic return even on large scales.

Multi-objective simulation–optimization via kriging surrogate models applied to natural gas liquefaction process design

A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both power consumption and heat exchanger area utilization. The Pareto solutions of the single-mixed refrigerant (SMR) and propane-precooled mixed refrigerant (C3MR) processes are compared to determine the suitability of each process in terms of energy consumption and heat exchanger area. Kriging models and the ɛ-constraint methodology are used to sequentially provide simple surrogate optimization subproblems, whose minimizers are promising feasible and non-dominated solutions to the original black-box problem. The surrogate-based ɛ-constrained optimization subproblems are solved in GAMS using CONOPT. The Pareto Fronts achieved with the surrogate-based framework dominate the results from the NSGA-II, a well-established meta-heuristics of multi-objective optimization. The objective functions of non-dominated solutions go as low as 1045 and 980.3 kJ/kg-LNG and specific UA values of 212.2 and 266.9 kJ/(°C kg-LNG) for SMR and C3MR, respectively. The trade-off solutions that present the minimum sum of relative objectives are analyzed as well as the dominance of C3MR over SMR at low power consumption values and conversely at low heat exchanger area utilization.

Optimization and analysis of a hydrogen liquefaction process: Energy, exergy, economic, and uncertainty quantification analysis

To reduce the specific energy consumption (SEC) of the hydrogen liquefaction process, a conceptual design of a hydrogen liquefaction process that integrates a modified natural gas single mixed refrigerant process and a hydrogen expansion cycle. The energy efficiency of the process is improved by operating each refrigeration cycle at different pressures. The process is optimized using a particle swarm optimization algorithm. Furthermore, the uncertainty levels of the SEC and the minimum internal temperature approach of the heat exchanger with varying parameters are investigated by applying uncertainty quantification (UQ). The results indicate SEC is 5.963 kWh/kg with 52.61% exergy efficiency. The thermodynamic evaluation shows that the coefficient of performance and figure of merit are 0.2218 and 0.526. The UQ analysis reveals that the SEC is strongly influenced by changing the flow rate and pressure of the sub-cooling refrigerant, with a distribution of SEC between 5.479 and 6.451 kWh/kg. Finally, the economic evaluation yields a total annualized cost of $25.42 million/y. This study is expected to provide an insight into designing an energy-efficient hydrogen liquefaction process by introducing a natural gas mixed refrigerant process.

Particle Swarm-Assisted Artificial Neural Networks for Making Liquefied Natural Gas Processes Feasible Under Varying Feed Conditions

Natural gas (NG) has been widely recognized as a cleaner fuel compared to other fossil fuels. Reserves of NG are typically located in remote areas, and their conditions and compositions vary geographically. The NG from such areas is transported in the form of liquefied natural gas (LNG). Liquefying NG is highly complex. Generally, the process is designed to be carried out under fixed natural gas (NG) conditions; hence, it may not perform well under variable NG conditions. Considering this issue, the use of an artificial intelligence approach, rather than the conventional optimization one, was investigated to make the LNG process feasible under variable NG conditions. This study is the first in this research area to train an artificial neural network (ANN) using the particle swarm optimization (PSO) algorithm as a learning method. The developed PSO-ANN model was used to predict the decision variables of a single mixed refrigerant (SMR) LNG process for its feasible design under varying NG conditions. The correctness of the predicted set of decision variables (NG conditions) was verified by inputting them into Aspen HYSYS. The output of the SMR-LNG process was the overall power at a constrained minimum internal temperature approach (MITA) value, i.e., 1.0 ≤ MITA ≤3.0. The prediction results of the PSO-ANN model were compared with those of the classical ANN backpropagation learning method. The success rate of the proposed PSO-ANN model was 80%. Furthermore, the proposed model can make the LNG process feasible for a diverse range of temperature and pressure values. A feasible process with a better MITA value can also be achieved by tuning the model parameters.

Novel integrated energy-efficient dual-effect single mixed refrigerant and NGLs recovery process for small-scale natural gas processing plant

The design of natural gas liquefaction process operated at offshore regards space as a design variable and requests an integrated and compact process due to space limitation. This study proposes an integrated process for natural gas liquefaction and NGLs recovery based on single mixed refrigerant (SMR) cycle. The considered SMR refrigeration cycle functions as dual mixed refrigerant (DMR) to provide two separate refrigeration levels for natural gas precooling and subcooling. NGLs recovery section includes a conventional column and a heat integrated distillation column in indirect sequence to produce lean gas, ethane liquid, LPG and gasoline. Liquefaction operating parameters were optimized using genetic algorithm in search of optimal values for profitable process with optimal energy consumption. NGLs recovery section of the proposed process is flexible, operable in both ethane recovery and ethane rejection modes. Based on base case process, the proposed process lowers energy consumption by 14%. The key features of the proposed process are cold utility recovery through self-refrigeration and ethane liquid recovery.

Performance evaluation and comparison of commonly used optimization algorithms for natural gas liquefaction processes

The genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and BOX algorithm have been used in natural gas liquefaction process optimization. Three algorithms all can find a solution by adopting different strategies and computational efforts. Therefore, it is necessary to compare their performance. This article presents a performance comparison of the GA, PSO, and BOX algorithms for the optimization of four natural gas mixed refrigerant liquefaction processes. The results show that PSO has the best optimization performance, reducing the specific energy consumption (SEC) to 0.3233 kWh/kg, 0.2351 kWh/kg, 0.2489 kWh/kg, and 0.2427 kWh/kg for single mixed refrigerant (SMR), dual mixed refrigerant (DMR), propane pre-cooling mixed refrigerant (C3MR), and mixed fluid cascade (MFC), respectively. Furthermore, PSO also improved the exergy efficiency of the four processes to 35.34%, 48.59%, 45.90%, and 47.07%. The composite curve analysis shows that the heat transfer efficiency of the heat exchanger optimized by PSO is more efficient. The study also discovered that the total optimization performance of PSO and GA is better than BOX algorithm, and the GA optimization performance is second only to PSO. This research would greatly assist process engineers in making the right decision on process optimization to overcome energy efficiency challenges.

Design and optimization of the sustainable natural gas liquefaction process plant: A process system engineering approach

With the rapid development of the world economy and technologies, the energy demand is increasing. Based on the current regulations energy sectors need low-emission and low-carbon energy solutions. Natural gas (NG) is considered a green future fuel that is usually stored and transported as compressed gas or cryogenic liquid. The liquefied NG has 1/600th of its volume which helps in transportation. Liquefied natural gas (LNG) operations are the most energyintensive techniques. During refrigeration and liquefaction, the units of the LNG consume around ∼40−50% of the entire LNG supply chain energy. To date, the simplest single mixed refrigerant (SMR) cycle-based NG liquefaction plants have exergy efficiencies of around 26.97%. Moreover, the conventional SMR process utilizes a hydrocarbon-based mixed refrigerant (i.e., C1 to C3 and N2) mixture that is further harmful to the ecosystem. In this research work, it was found that the addition of eco-friendly hydro-fluoro-olefin (HFO123yf) with conventionally used refrigerants reduces energy consumption under the same conditions. Further hydraulic turbines were proposed to replace the expansion valves require for the Joule Thompson cooling effect that further increasing the process reversibility and energy efficiency. Hence, this research utilizes the optimization of the HFO based modified mixed refrigerant mixture (HSMR) for optimal operation of the LNG plant. The evaluated result shows that the proposed stochastic biogeography-based optimization (BBO) algorithm successfully reduces the overall energy consumption by 0.232 kW/kg-NG. This energy consumption is about 41.709 % compared to our base case study and it was 36% more efficient compared to the conventional SMR published study. This research further explores the sensitive decision variables that affect power consumption using the sensitivity analysis index method. The sensitivity indices may enable us to operate the plant in any abrupt changing or uncertain conditions when needed quick optimization using only a few key sensitive optimizing variables which are useful for real-time plant optimization. Moreover, the economic analysis of the proposed plant shows a total annualized cost of 6,158,000 USD with a 20% rate of return, per year.