Optimizing Furnace efficiency for Factory of Future using Cooperative Games

TL;DR

This paper presents a cooperative game theory approach to optimize furnace efficiency in petrochemical industries, aiming to reduce energy consumption and improve operational performance.

Contribution

It introduces a novel cooperative game formulation for multi-variable furnace optimization, comparing it with standard multi-objective algorithms.

Findings

Cooperative game approach effectively balances multiple control variables.

The method outperforms NSGA-II and RNSGA-II in optimization tasks.

Significant energy savings potential demonstrated.

Abstract

Approximately 75% of energy used in petrochemical and refining industries is consumed by furnaces. Operating furnaces at optimal conditions results in huge amounts of savings. In this paper, we model the furnace efficiency optimization as a multi-objective problem involving multiple interactions among the controlled variables and propose a cooperative game based formulation for the factory of future. The controlled variables are Absorbed Duty and Coil Outlet Temperature. We propose a comprehensive solution to select the best combination of manipulated variables (fired duty, throughput and coil inlet temperature) satisfying multiple criteria using a cooperative game theory approach. We compare this approach with the standard multi-objective optimization using NSGA-II and RNSGA-II algorithms.