Work

Balancing production maximization and equipment degradation: Experimental application of a health-aware controller

Year: 2024

Type: article

Abstract: This study implements a Health-Aware Controller (HAC) in an experimental rig that emulates an oil well system. The operational goal for the system is to maximize production, while ensuring that the degradation in each of the wells is controlled, such that no critical equipment breakdown happens before a planned maintenance stop. Health-aware control systems combine production optimization techniques with equipment health monitoring and they were recently proposed to systematically address the intuitive trade-off between optimizing production and minimizing equipment degradation. As such the controller is intended to maximize the system’s economic performance, while proactively preventing unexpected breakdowns. However, so far the concepts for health-aware control have been tested only in simulations. The main contribution of this paper is to implement and test a Health-Aware Controller on a physical system in lab conditions. Our experimental results confirm the in-silico findings on health-aware control that show that it is able to realize a balance between decreasing the equipment degradation and maximizing the system production, and has the potential to improve the system performance considering both revenues and maintenance costs. We further discuss several implementation aspects and highlight some implementation challenges that can be faced by other practitioners. • An experimental setup for Health-Aware Controller (HAC) tests is introduced. • HAC is formulated using a nonlinear model predictive control (NMPC) paradigm. • Equipment health considerations and economic operational objectives are combined. • A simplified model for degradation dynamics is included in the formulation. • Important practical implementation challenges are discussed.

Source: Control Engineering Practice

DOI: 10.1016/j.conengprac.2024.106193

Authors: José Matias, Johannes Jäschke

Institution: Norwegian University of Science and Technology

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,920