Multi-objective optimization of high-sulfur natural gas purification plant

Abstract

There exists large space to save energy of high-sulfur natural gas purification process. The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption and further improve the production rate of purified gas. A steady-state simulation model of high-sulfur natural gas purification process has been set up by using ProMax. Seven key operating parameters of the purification process have been determined based on the analysis of comprehensive energy consumption distribution. To solve the problem that the process model does not converge in some conditions, back-propagation (BP) neural network has been applied to substitute the simulation model to predict the relative parameters in the optimization model. The uniform design method and the table U 21 (10 7) have been applied to design the experiment points for training and testing BP model. High prediction accuracy can be achieved by using the BP model. Nondominated sorting genetic algorithm-II has been developed to optimize the two objectives, and 100 Pareto optimal solutions have been obtained. Three optimal points have been selected and evaluated further. The results demonstrate that the total comprehensive energy consumption is reduced by 13.4% and the production rate of purified gas is improved by 0.2% under the optimized operating conditions.

An energy‐efficiency evaluation method for high‐sulfur natural gas purification system using artificial neural networks and particle swarm optimization

“…Based on the previous work in the laboratory 44 and the results of other researchers, 45 we preliminarily selected the process parameters affecting the energy consumption performance of the plant operation (Table 6). Hence, the historical operating data of this plant were collected from DCS, from January 2019 to September 2019 with an interval of 1 hour.…”

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

“…The smoothed PSA process model has a Hammerstein-Wiener block structure composed of three blocks connected in series: a static nonlinear input block, a linear dynamic block, and a static nonlinear output block (Figure 6). The nonlinear input and output blocks were built with ten linear segments of a piecewise defined Equations ( 8) and (10), respectively. The linear model is a transfer function with two zeros and three poles (Equation ( 9)).…”

“…The linear model is a transfer function with two zeros and three poles (Equation ( 9)). The linear model has an Output Error structure with a sampling time of 1 s. The final model is shown in Equations ( 8)- (10).…”

“…In the regeneration stage, a low pressure close to vacuum pressure is used, which causes the water molecules to detach from the adsorbent, freeing the site in the adsorbent. It is essential to mention that the PSA process model is represented by Partial Differential Equations (PDEs), and this makes it a complex system due to its high non-linearity [10,11]. Due to the complexity of the process explained by its cyclical nature and produce bioethanol, advanced studies are required to obtain purities greater than 99% wt of ethanol.…”

A Framework for Design and Operation Optimization for Utilizing Low-Grade Industrial Waste Heat in District Heating and Cooling

“…In the process industry, high-grade and medium-grade waste heat can be effectively utilized by HENs. In addition, these two grades of waste heat can also be recovered for steam production by a waste heat boiler [6]. However, since heat sinks that need low-grade heat are rare, most low-grade waste has to discharge into the environment, causing serious energy waste [7].…”