AUTHOR=Ahmad Ashfaq , Qadeer Kinza , Naquash Ahmad , Riaz Fahid , Hasan Mudassir , Qyyum Muhammad Abdul , Lee Moonyong TITLE=Particle Swarm-Assisted Artificial Neural Networks for Making Liquefied Natural Gas Processes Feasible Under Varying Feed Conditions JOURNAL=Frontiers in Energy Research VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.917656 DOI=10.3389/fenrg.2022.917656 ISSN=2296-598X ABSTRACT=

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.