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Y. Xu et al.
algorithm and HYSYS to present a systematic stochastic optimization method for a dual
mixed-refrigerant process.
Heuristic algorithm have already applied to optimize the purification plants. Biyanto
et al. [14] developed acid gas sweetening process by Aspen HYSYS. They aimed to
minimalize the energy consumption on a condenser and re-boilers in regenerator process. Least Squares - Support Vector Machine is established to optimize the process and
the energy consumption reduced by 50%. Ma et al. [15] developed the steady-state natural gas purification process model by using the process simulation software PROMAX.
Then they established the optimization model of purification system energy consumption by using BP neural network and GA. By optimizing the operating parameters of a
high sulfur natural gas purification plant the comprehensive energy consumption reduced
by 12.7%. Qi Li [16] established a natural gas purification device energy optimization
model based on Aspen HYSYS and genetic algorithm. The optimization target is the
unit energy consumption of the purification. Though this model, the energy consumption
of the purification device has been reduced by 12.8% and the economic benefits have
been significantly improved. Ji Ning et al. [17] established the dehydration unit model
of the natural gas purification plant through Aspen HYSYS, and used GA to optimize
the operation parameters of the dehydration unit. When the product gas quality meets
the design standards, the accuracy of the above simulation model is high, and the unit
energy consumption of the natural gas dehydration device can be reduced by 17% compared with the design value. With the help of Aspen HYSYS, Wang et al. [18] applied
optimization models and optimization methods to calculation and parameter optimization of the desulfurization unit of the purification plant of Petro China Southwest Oil
and Gas Field Company. The objective function is energy consumption of the pump
and regeneration tower. The energy consumption of the desulfurization section of the
purification device is reduced by 12.35% compared to before optimization.
However, most of studies only considered the part of the purification plant and lacked
an analysis of the whole purification plant. Ma et al. analyzed the purification plant as a
whole and they aimed at the lowest energy consumption. However, from the perspective
of this article, the emission of sour gas can’t be ignored. We should also consider the effect
of sour gas on the environment while reducing energy consumption. Sulfur production
is included in the energy performance index, which will help control acid gas emissions.
Therefore, this paper introduces energy performance index and considers the impact of
sulfur production on the total energy consumption of the purification plant.
2 Methodology
2.1 Energy Performance Index
Energy performance index (EnPI) [19] is defined as total energy consumption, energy
consumption per unit of processing capacity, and energy consumption per unit of product
output.
Y. Xu et al.
algorithm and HYSYS to present a systematic stochastic optimization method for a dual
mixed-refrigerant process.
Heuristic algorithm have already applied to optimize the purification plants. Biyanto
et al. [14] developed acid gas sweetening process by Aspen HYSYS. They aimed to
minimalize the energy consumption on a condenser and re-boilers in regenerator process. Least Squares - Support Vector Machine is established to optimize the process and
the energy consumption reduced by 50%. Ma et al. [15] developed the steady-state natural gas purification process model by using the process simulation software PROMAX.
Then they established the optimization model of purification system energy consumption by using BP neural network and GA. By optimizing the operating parameters of a
high sulfur natural gas purification plant the comprehensive energy consumption reduced
by 12.7%. Qi Li [16] established a natural gas purification device energy optimization
model based on Aspen HYSYS and genetic algorithm. The optimization target is the
unit energy consumption of the purification. Though this model, the energy consumption
of the purification device has been reduced by 12.8% and the economic benefits have
been significantly improved. Ji Ning et al. [17] established the dehydration unit model
of the natural gas purification plant through Aspen HYSYS, and used GA to optimize
the operation parameters of the dehydration unit. When the product gas quality meets
the design standards, the accuracy of the above simulation model is high, and the unit
energy consumption of the natural gas dehydration device can be reduced by 17% compared with the design value. With the help of Aspen HYSYS, Wang et al. [18] applied
optimization models and optimization methods to calculation and parameter optimization of the desulfurization unit of the purification plant of Petro China Southwest Oil
and Gas Field Company. The objective function is energy consumption of the pump
and regeneration tower. The energy consumption of the desulfurization section of the
purification device is reduced by 12.35% compared to before optimization.
However, most of studies only considered the part of the purification plant and lacked
an analysis of the whole purification plant. Ma et al. analyzed the purification plant as a
whole and they aimed at the lowest energy consumption. However, from the perspective
of this article, the emission of sour gas can’t be ignored. We should also consider the effect
of sour gas on the environment while reducing energy consumption. Sulfur production
is included in the energy performance index, which will help control acid gas emissions.
Therefore, this paper introduces energy performance index and considers the impact of
sulfur production on the total energy consumption of the purification plant.
2 Methodology
2.1 Energy Performance Index
Energy performance index (EnPI) [19] is defined as total energy consumption, energy
consumption per unit of processing capacity, and energy consumption per unit of product
output.