Decision support system for a heat-recovery section with equipment degradation

作者:

Highlights:

• Real-time optimization of shared resource utilization improves daily operation.

• Degradation due to fouling is included to suggest the optimal cleaning task.

• Data based models for the heat transfer coefficient are built.

• Converting raw information into the right managerial actions is key.

• Model-based MINLP exploits the complex nature of real industrial problems.

摘要

In the framework of Industry 4.0, decision support systems (DSS) are an essential part in the process of converting information into managerial actions. This paper proposes a specific DSS to improve the daily operation of an industrial heat-recovery section (a network of heat exchangers) in a fiber-production factory. In this process, the aim is to optimize the resource utilization in real time while satisfying a set of production constraints. The operational decisions to be taken by the network operators is to set up the heat-source allocation to heat exchangers. Furthermore, the heat transfer decreases over time due to fouling in the exchangers, so an additional decision to take is which exchanger to clean and when. The proposed model-based DSS builds upon a rigorous mathematical representation of the network, integrating continuous operation with the discrete decisions on maintenance. Then, a mixed-integer nonlinear optimization, solved in real time, drives the analysis and choice phases to fulfill product specifications according to an economic criterion. In this way, the proposed DSS not only provides the user with a right allocation of heat sources to exchangers, but also suggests which of them are potentially beneficial to be cleaned.

论文关键词:Mathematical modeling,Maintenance decisions,Heat recovery,Real-time optimization,Industrial DSS

论文评审过程:Received 8 January 2020, Revised 29 July 2020, Accepted 29 July 2020, Available online 3 August 2020, Version of Record 19 August 2020.

论文官网地址:https://doi.org/10.1016/j.dss.2020.113380