Optimization of load sharing for parallel compressors using a novel hybrid intelligent algorithm

Abstract

Compressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

“…Accordingly, there have been developed many WOA variants by hybridizing with different optimization algorithms from these categories [ 102 ]. The results show that hybridizing WOA with algorithms from these two categories can significantly enhance the canonical WOA's performance [ 103 – 105 ].…”

“…Evolutionary algorithms can approximate the optimal region early in the evolutionary process and maintain the population diversity in the final iterations using properties such as mutation, crossover, selection, self-adaptation, and external archives. These properties caused many hybrid WOA variants to be proposed, such as [ 103 , 112 , 113 ] since the introduction of WOA.…”

“…In solving the optimization of load sharing for parallel compressors problem, Li et al [ 103 ] show that WOA suffers from poor exploration ability while the differential evolution (DE) algorithm benefits from this ability. Hence, they developed the DWOA algorithm by hybridizing WOA and DE.…”

Multi-Optimization of Injection Parameters Affecting Emissions in a Diesel Engine Fueled with Fischer–Tropsch Fuel Based on NSGA-II

“…With the rapid development of numerical simulation technology, intelligence optimization technology − has been widely used in many aspects of engineering science, such as signal processing, automatic control, and machine design, and has shown a powerful ability to search optimally. − However, numerical studies on direct injection parameter optimization of common rail diesel engine are very limited due to the complexity in modeling the performance in relation to injection parameters. In previous studies, response surface methodology and NSGA-II were adopted to optimize the coupling effects between emissions and injection parameters in a diesel engine.…”

Minimum Energy Adaptive Load Sharing of Parallel Operated Compressors

“…The proposed algorithm was then tested for LSO on seriesconnected compressors. In publication [21], an algorithm for the control of a few compressors is presented, in which the problem is solved through mixed integer nonlinear programming. Many publications focus on the design of compressor controller and performance enhancement.…”