Feasibility Study of Decentralized Lubrication System Design Through Branch and Bound and Genetic Algorithm for Turbomachinery Trains

The lubrication system functions are crucial in a mechanical system, where friction needs to be controlled and managed. These functions include preventing excessive wear and tear, minimizing heat generation, and prolonging the machinery components’ lifespan. Improper lubrication can incur substantial costs in repairing damaged or replacing worn-out parts and unscheduled downtime. Although turbomachinery trains centralized circulating lubrication systems developed over half a century ago are reliable, they now require an updated design philosophy. These systems are challenged by issues such as low efficiency, complex piping networks, additional footprint space, and high costs. To address these issues, this study explores improvement methods employed in similar centralized industrial systems, like those in the energy and wastewater sectors. A common design trait of these systems is decentralization, transforming a large-scale centralized infrastructure into decentralized, smaller, localized units. These units can leverage local resources and distribute them according to specific requirements. Decentralization has demonstrated improvements in resilience, reliability, efficiency, cost-effectiveness, and environmental impact. Given the comparable challenges faced by centralized circulating lubrication systems, the feasibility of applying decentralization to lubrication systems was investigated. This paper introduces the concept of the decentralized lubrication system comprising multiple compact lubrication units that collectively satisfy the turbomachinery train lubrication demand. To identify the optimal decentralized combination of lubrication units, two optimization models using Branch-and-Bound and Genetic Algorithm techniques were developed. The objective of these models was to minimize three key parameters, namely footprint space, tank volume, and investment cost, while adequately meeting the overall lubrication demand (in terms of flow rate). The comparison between the optimized decentralized lubrication system and the existing centralized lubrication system revealed that the former offers significant improvements in the three key parameters. Furthermore, these optimization models demonstrated their adaptability by effectively identifying the optimal combinations of lubrication units for various lubrication demands.