Enhancing the performance of a parallel nitrogen expansion liquefaction process (NELP) using the multi-objective particle swarm optimization (MOPSO) algorithm

Mofid, Hossein; Jazayeri-Rad, Hooshang; Shahbazian, Mehdi; Fetanat, Abdolvahhab

doi:10.1016/j.energy.2019.01.087

Cited by 37 publications

(20 citation statements)

References 44 publications

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“…Through L 2 and L 3 , the location of k 2 can be also obtained, as is shown in Equation 20. Therefore, according to R, s a (x a , y a ) and s b (x b , y b ), the positions of k 1 and k 2 can be calculated by Equation (19) and Equation (20). If d ab is equal to 2R, there is only one intersection point between communication ranges and its location is ( x a +x b 2 , y a +y b 2 ).…”

Section: A Selection Of Potential Visiting Pointsmentioning

confidence: 99%

“…The distribution of node s 1 , s 2 , s 3 and s 4 is shown in Figure 5(a). Firstly, all intersection points (k 1 ∼k 12 ) are calculated by Equation (19) and Equation (20). In the communication range of node s 1 , intersection points with the highest coverage level are k 2 , k 3 , k 4 and k 5 , as is shown in Figure 5(b).…”

Section: A Selection Of Potential Visiting Pointsmentioning

confidence: 99%

“…Traditional multi-objective methods transform multiobjective into a single objective, which is inefficient. For example, the weighted summation method needs to adjust parameters to obtain approximate Pareto optimal solutions [20]. However, some heuristic algorithms such as PSO can solve this problem.…”

Section: ) Multi-objective Optimizationmentioning

confidence: 99%

“…For general multi-objective optimization algorithms, it may require a very high computational time. However, heuristic algorithms such as multi-objective particle swarm optimization (MOPSO) can solve similar problems and supply the entire Pareto set of optimal solutions [20], [21]. In this paper, we propose an energy-efficient trajectory planning algorithm (EETP) based on MOPSO to solve this multi-objective problem, as is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Yang

2019

IEEE Access

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In wireless sensor networks (WSNs), collecting data with mobile sinks is an effective way to solve the ''energy hole problem''. However, most of existing algorithms of mobile sinks ignore the load balance of rendezvous nodes, which will significantly shorten the network lifetime. Moreover, most mobile sinks are usually required to visit locations of sensor nodes without taking advantage of their communication ranges. Therefore, this paper proposes an energy-efficient trajectory planning algorithm (EETP) based on multi-objective particle swarm optimization (MOPSO) to shorten the trajectory length of the mobile sink and balance the load of rendezvous nodes. EETP aims to reduce the delay in data delivery and prolong the network lifetime. To shorten the trajectory length of the mobile sink, we design a mechanism to select potential visiting points within communication overlapping ranges of sensor nodes, rather than locations of sensor nodes. Additionally, according to trajectory characteristics of the mobile sink, we design an effective trajectory encoding method that can generate a trajectory containing an unfixed number of visiting points. The simulation results show that the proposed EETP is superior to existing WRP, CB and the MOPSO-based algorithm, in terms of delay in data delivery, network lifetime and energy consumption. INDEX TERMS Mobile sink, MOPSO, load balance of rendezvous nodes, trajectory planning.

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Section: A Selection Of Potential Visiting Pointsmentioning

confidence: 99%

Section: A Selection Of Potential Visiting Pointsmentioning

confidence: 99%

Section: ) Multi-objective Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Yang

2019

IEEE Access

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“…If the problem space is considered with d dimensions and particles, the ith position particle at the ith position, X i (x i1 , .., x id ), has a velocity of V i = (v i1 , .., v id ). The best performance of each particle in the swarm is P i (p i1 , .., p id ) [37]. Each particle attempts to improve its position, velocity, and distance with respect to the best particle.…”

Section: Multi-objective Particle Swarm Optimization (Mpso)mentioning

confidence: 99%

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

et al. 2019

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The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation.

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Reducing CO₂ emissions and process cost in vinyl acetate/acetic acid/acetic anhydride ternary mixture via heat pumps with heat exchange network

Ming

Fan

et al. 2021

Asia-Pacific J Chem Eng

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A systematic design and optimization method for vinyl acetate/acetic acid/acetic anhydride ternary system separation processes is proposed. Herein, four separation schemes including the conventional distillation, the inter‐column mechanical vapor recompression heat pump distillation (IMVRHP), the double mechanical vapor recompression heat pumps distillation (DMVRHPs), and DMVRHPs with heat exchange network (DMVRHPs‐HEN) are conducted. The optimal separation process is determined by minimizing the total annual cost (TAC) and CO2 emission. As a result, the three designed mechanical vapor recompression heat pump distillation (MVRHP) processes show better performance in all aspects than conventional distillation process. Specifically, the DMVRHPs‐HEN scheme saves 89.75% of the CO2 emission and 0.09% of the TAC compared with conventional distillation process. More important, the TACs of DMVRHPs‐HEN are decreased by 22.21% and 57.69% compared with those of conventional distillation process when the payback period is 5 and 8 years, respectively. This research is expected to provide theoretical guidance for the energy saving scheme of vinyl acetate/acetic acid/acetic anhydride ternary system.

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Enhancing the performance of a parallel nitrogen expansion liquefaction process (NELP) using the multi-objective particle swarm optimization (MOPSO) algorithm

Cited by 37 publications

References 44 publications

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

Reducing CO₂ emissions and process cost in vinyl acetate/acetic acid/acetic anhydride ternary mixture via heat pumps with heat exchange network

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Enhancing the performance of a parallel nitrogen expansion liquefaction process (NELP) using the multi-objective particle swarm optimization (MOPSO) algorithm

Cited by 37 publications

References 44 publications

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Energy-Efficient Trajectory Planning Algorithm Based on Multi-Objective PSO for the Mobile Sink in Wireless Sensor Networks

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

Reducing CO2 emissions and process cost in vinyl acetate/acetic acid/acetic anhydride ternary mixture via heat pumps with heat exchange network

Contact Info

Product

Resources

About

Reducing CO₂ emissions and process cost in vinyl acetate/acetic acid/acetic anhydride ternary mixture via heat pumps with heat exchange network