Nonisothermal Compressor Station Optimization

Abstract

A detailed mathematical model of compressor stations and pipes is essential for optimizing the performance of the gas pipeline system. Most of the available literature on compressor station modeling is based on isothermal solutions for pipe flow, which is inadequate for our purposes. In the present work, the pipe flow is treated as nonisothermal unsteady one-dimensional compressible flow. This is accomplished by treating the compressibility factor as a function of pressure and temperature, and the friction factor as a function of Reynolds number. The solution method is the fully implicit finite difference method that provides solution stability, even for relatively large time steps. The compressors within the compressor station are modeled using centrifugal compressor map-based polynomial equations. This modeling technique permits the designation of different models of compressors in the compressor station. The method can be easily extended to include other types of compressors. Using this mathematical model as a basis, a nonlinear programing problem (NLP) is set up wherein the design variables are the compressor speeds and the objective function to be minimized is the total fuel consumption. The minimum acceptable throughput is imposed as a constraint. This NLP is solved numerically by a sequential unconstrained minimization technique, using the mathematical model of the system for the required function evaluations. The results show that this approach is very effective in reducing fuel consumption. An application of this methodology for selecting the number of compressors to be shut down for the most fuel-efficient operation is also presented. Our results further indicate that station-level optimization produces results comparable to those obtained by network-level optimization. This is very significant because it implies that the optimization can be done locally at the station level, which is computationally much easier.

Optimization of a network of compressors in parallel: Real Time Optimization (RTO) of compressors in chemical plants – An industrial case study

“…Many authors and practitioners reported that it is difficult or impossible for different compressors in a compressor station to have identical characteristics and efficiencies [8,21,29,30]. Moreover, these characteristics and efficiencies change over time due to fouling and erosion [31], and nonuniform maintenance plans which result in dissimilar compressor maps for the same compressor at different time periods [10,32,33].…”

“…A few researchers [15,21,36] studied the optimal load sharing of compressors operating in parallel in order to minimize the fuel consumed by the gas turbines drivers. However, these works did not present an online application which considers the practical aspects of the implementation of the actual optimization, for example update of the maps and steady-state detection.…”

“…future information) is a scheduled maintenance of a compressor, which might influence the decisions for the current operation. The single-period optimization can be further classified as steady-state pipeline optimization [12,14,19,20], optimal load sharing [10,15,21] and optimal selection of compressors [10,13,16,18,22]. The steady-state pipeline optimization examines the optimal operation of the fuel cost minimization problem [14] considering information of one period [20].…”

Multi-Objective Genetic Algorithms for Chemical Engineering Applications

“…So the analysis of degrees of freedom gives five rotational speeds as independent variables. The set of inequality constraints is constituted by an upper bound as well as a lower bound for the pressures of the nodes (MAOP as upper bound and atmospheric pressure as lower bound), sonic velocity and erosional velocity in the role of upper bounds of the velocities through pipes, lower and upper bounds on the rotation speed of all compressors (166.7 and 250 rpm respectively [1]), a lower bound on compressor throughput in order to avoid the pumping phenomenon and an upper bound on compressor throughput to prevent the choking phenomenon. In total, there are 76 inequality constraints.…”

Preprocessing of raw data for developing steady-state data-driven models for optimizing compressor stations

“…Abbaspour et al [4] considered the optimization of compressor stations with single-stage compressors and they mentioned that a compressor station has dissimilar compressors in reality. Moreover, Han et al [5] used Compressor station with multi-stage centrifugal compressors in parallel.…”