Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

JOURNAL OF CLEANER PRODUCTION

卷:271

Article

Blackburn, Landen D.¹  Tuttle, Jacob F.¹  Powell, Kody M.^1,2 

[1] Univ Utah, Dept Chem Engn, Salt Lake City, UT 84112 USA

[2] Univ Utah, Dept Mech Engn, Salt Lake City, UT 84112 USA

关键词: Real-time optimization;    Cooling tower;    Machine learning;    Neural network;    Supply-side management;    Particle swarm optimization;   

DOI  :  10.1016/j.jclepro.2020.122175

来源: Elsevier

【 摘 要 】

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R-2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. This proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free