Dynamic optimization of boiler for minimizing energy consumption in the intentionally transient process operation: effect of different interval number

• Published in: CHEMICAL PRODUCT AND PROCESS MODELING
• Main Authors: Rohman, Fakhrony Sholahudin; Alwi, Sharifah Rafidah Wan; Azmi, Ashraf; Er, Hong An; Termizi, Siti Nor Azreen Ahmad
• Format: Article; Early Access
• Language: English
• Published: WALTER DE GRUYTER GMBH 2024
• Subjects: Engineering
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001319444900001
• ISSN: 1934-2659; 2194-6159
• DOI: 10.1515/cppm-2024-0018

Certain manufacturing or industrial processes may involve variable conditions, and intentionally transient boiler operation allows optimal adaptation to these variations. This helps maintain efficiency and reduce energy consumption during different process phases. Transient operation is inherent during the start-up and intermittent phases in reaching the pressure required for boiler operation. Optimizing these transient states can reduce energy consumption. Dynamic optimization of boilers is crucial for several reasons, especially in industrial and power generation settings. Boilers are used to produce steam or hot water for various processes, and optimizing their performance can lead to increased efficiency, reduced energy consumption, and improved overall system reliability. The dynamic optimization problems were solved using the orthogonal collocation method. Three problem optimizations were considered in this study: minimize process time (P1), minimize energy consumption without optimized final time (P2), minimize energy consumption with optimized final time (P3). The control/decision variables applied were firing rate, Q, and water feed flowrate, qf. From the simulation results, the control trajectories of P3 were chosen to be the most effective control operation to achieve the minimum energy consumption for reaching target pressure, i.e., 10.2 MPa, with a reasonable intermittent unit time. In practice, the selection of the number of intervals is often determined through a combination of domain knowledge, computational resources, and the desired level of accuracy. Sensitivity analysis and testing with different interval sizes can help in understanding the impact of this parameter on the optimization results. A greater interval time will decrease energy consumption.