Deterministic scenarios guided K-Adaptability in multistage robust optimization for energy management and cleaning scheduling of heat transfer process

The heat transfer process is of paramount importance for energy management and heat recovery, but the inevitable uncertain fouling poses significant challenges to sustainable energy-saving efforts. This study aims to solve the energy management and cleaning scheduling problems under fouling uncertainty. To achieve this, a novel multistage Robust Optimization (RO) method based on the Deterministic Scenarios Guided K-Adaptability (DSGKA) strategy is proposed. The process scheduling problem is initially tackled through long-period Mixed Integer Optimal Control Problems (MIOCPs) with deterministic scenarios. Subsequently, considering the slow transition characteristics of energy efficiency degradation caused by fouling, the candidate paths generation algorithm guided by deterministic MIOCPs is developed. Additionally, the K-Adaptability method is employed to segment decision-dependent uncertainty sets into finite partitions, facilitating the resolution of the multistage RO problem through worst-case analysis. In theory, the rationality of the proposed guidance algorithm is established. Simulation results on a heat exchanger network are also provided to demonstrate the effectiveness of the DSGKA scheme. By transforming the original multistage RO problem into a finite-dimensional optimization problem solvable with intelligent heuristic algorithms, the proposed method adeptly manages nonlinearity in constraint equations, thereby improving its applicability for equipment maintenance scheduling across various slow transition industrial processes.