Operating Parameters Optimization of Natural Gas Purification Plant
277
Kundu et al. [2] used the data from Aspen HYSYS and the neural network to predict the
output data such as sales gas flow rate, pressure and temperature. Qeshta et al. [3] developed a liquefied petroleum gas (LPG) model by Aspen HYSYS. Though the model, they
determined the sensitivity of parameters affecting the process. The reboiler operating
pressure, rich MDEA temperature, reflux ratio and other parameters which indirectly
affect the extraction process were also determined. Liu and Karimi [4] developed a
triple-pressure reheat combined cycle gas turbine (CCGT) power plant model in Aspen
HYSYS. This is the first comprehensive model for simulating the part-load operation of a
triple-pressure reheat CCGT plant in the open literature. Saadi et al. [5] developed a LPG
model in Aspen HYSYS to analysis a LPG unit production. They have a contribution
on thermo-economic analysis and find that the most important exergy destructions are
detected in rectification columns. Al-Lagtah et al. [6] used Aspen HYSYS to simulate
and sensitivity analysis Lekhwair plant. This research reviews the current operation of
the plant considering the lean amine circulation flow rate, temperature and concentration,
and proposes some modifications to the existing plant to increase its profitability and
sustainability. The operating capacities of some equipment are reviewed to assess the
possibility of changing the operating parameters along with investigating the occurrence
of common operational problems like foaming. Cao et al. [7] studied the dynamic simulation of the propane-propylene distillation column by using Aspen HYSYS Simulator.
They analyzed the configuration of column in Aspen HYSYS and developed a feedback
control system for separated products quality control. Ahmadgurabi et al. [8] combined
Aspen HYSYS and MATLAB to design and implement an adaptive model predictive
controller on an industrial “dynamic” and “nonlinear” plant. The model has satisfactory
adjustment performance in the face of induced interference, obtain the control action
by minimizing the cost function. Alaei et al. [9] used Aspen HYSYS to simulate the
distillation tower and passed the data to MATLAB through the interface. The linear
model based on Autoregressive with external input and nonlinear model based on neural
network were established to identification and control the distillation column.
In the operation optimization of various factories, mathematical programming is
essential. Babaie and Nasr Esfahany [10] combined genetic algorithm and particle swarm
algorithm to optimize the tert-Amyl methyl ether production process. Total annual cost
was used as the objective function. They considered the mixture was isolated through
the pervaporation (PV) process, and all parameters related to reactive distillation and PV
as optimization variables. They used this optimization algorithm obtained the number
of heat exchangers between the two rectifying and stripping sections. Bayoumy et al.
[11] used Aspen HYSYS with MATLAB to produce a precise steady state simulation
based optimization of a whole green-field saturated gas plant as a real case study. They
merged sensitivity analysis and constrained bounding of the variables as a strategy.
The stochastic optimization algorithms such as genetic algorithm (GA) and particle
swarm optimization (PSO) techniques were used to solve the problem. They get the
best operating conditions by minimizing the total annual cost while maintaining at least
92% butane recovery as a process guarantee for the whole plant. Long, NVD et al. [12]
uses MATLAB to implement the PSO algorithm and then connects it to the HYSYS
distillation model. The results show that the structural variables, operating variables and
the operating cost are simply and effectively optimized. Yang et al. [13] used the PSO
277
Kundu et al. [2] used the data from Aspen HYSYS and the neural network to predict the
output data such as sales gas flow rate, pressure and temperature. Qeshta et al. [3] developed a liquefied petroleum gas (LPG) model by Aspen HYSYS. Though the model, they
determined the sensitivity of parameters affecting the process. The reboiler operating
pressure, rich MDEA temperature, reflux ratio and other parameters which indirectly
affect the extraction process were also determined. Liu and Karimi [4] developed a
triple-pressure reheat combined cycle gas turbine (CCGT) power plant model in Aspen
HYSYS. This is the first comprehensive model for simulating the part-load operation of a
triple-pressure reheat CCGT plant in the open literature. Saadi et al. [5] developed a LPG
model in Aspen HYSYS to analysis a LPG unit production. They have a contribution
on thermo-economic analysis and find that the most important exergy destructions are
detected in rectification columns. Al-Lagtah et al. [6] used Aspen HYSYS to simulate
and sensitivity analysis Lekhwair plant. This research reviews the current operation of
the plant considering the lean amine circulation flow rate, temperature and concentration,
and proposes some modifications to the existing plant to increase its profitability and
sustainability. The operating capacities of some equipment are reviewed to assess the
possibility of changing the operating parameters along with investigating the occurrence
of common operational problems like foaming. Cao et al. [7] studied the dynamic simulation of the propane-propylene distillation column by using Aspen HYSYS Simulator.
They analyzed the configuration of column in Aspen HYSYS and developed a feedback
control system for separated products quality control. Ahmadgurabi et al. [8] combined
Aspen HYSYS and MATLAB to design and implement an adaptive model predictive
controller on an industrial “dynamic” and “nonlinear” plant. The model has satisfactory
adjustment performance in the face of induced interference, obtain the control action
by minimizing the cost function. Alaei et al. [9] used Aspen HYSYS to simulate the
distillation tower and passed the data to MATLAB through the interface. The linear
model based on Autoregressive with external input and nonlinear model based on neural
network were established to identification and control the distillation column.
In the operation optimization of various factories, mathematical programming is
essential. Babaie and Nasr Esfahany [10] combined genetic algorithm and particle swarm
algorithm to optimize the tert-Amyl methyl ether production process. Total annual cost
was used as the objective function. They considered the mixture was isolated through
the pervaporation (PV) process, and all parameters related to reactive distillation and PV
as optimization variables. They used this optimization algorithm obtained the number
of heat exchangers between the two rectifying and stripping sections. Bayoumy et al.
[11] used Aspen HYSYS with MATLAB to produce a precise steady state simulation
based optimization of a whole green-field saturated gas plant as a real case study. They
merged sensitivity analysis and constrained bounding of the variables as a strategy.
The stochastic optimization algorithms such as genetic algorithm (GA) and particle
swarm optimization (PSO) techniques were used to solve the problem. They get the
best operating conditions by minimizing the total annual cost while maintaining at least
92% butane recovery as a process guarantee for the whole plant. Long, NVD et al. [12]
uses MATLAB to implement the PSO algorithm and then connects it to the HYSYS
distillation model. The results show that the structural variables, operating variables and
the operating cost are simply and effectively optimized. Yang et al. [13] used the PSO