Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm

Tsoumalis, Georgios; Bampos, Zafeirios N.; Chatzis, Georgios V.; Biskas, Pandelis N.; Keranidis, Stratos

doi:10.1016/j.apenergy.2021.117256

Cited by 15 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper [30] proposed applying hour-operated model GA for the optimized thermal energy storage (TES) operation. The authors of paper [31] ease the strength and fatigue damage for the jacket offshore platform SPD19A in the analysis that incorporates GA optimization analysis to the respective CF-optimization and WCF-optimization. The paper [32] developed the optimization algorithm for reloading fuel by using a hybrid teaching-learning GA.…”

Section: Literature Reviewmentioning

confidence: 99%

Metaheuristic-based PID Control: Evaluating the Effect of Parameter Settings

Chew

Wong

Juwono

et al. 2023

Preprint

View full text Add to dashboard Cite

Proportional-Integral-Derivative (PID) control method has been utilized in many industrial control applications, which has its limitations for the optimized control. Manually calculated controller tunings by using fixed formulas are only adequately to give basic control but not well-competent to non-linear process with various operating levels. Optimization analysis via metaheuristic approach has substantially obtained the best solutions and has proven its credibility to provide the better controller values. Nevertheless, the optimization analysis might provides bad result if the determinant parameter settings are not properly adjusted during the optimization analysis. This paper objectively presents a direct way to determine the best determinant parameter settings for the optimization analysis, whereby the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are chosen for the research analysis. In detail, parameter settings that include upper and lower bounds (UB, LB), maximum iteration (MaxIt), population size (nPop), mutation rate (mu) and damping ratio (wdamp) are analyzed. Ultimately, analysis results are compared with manually calculated controller tunings by using Level Process Control Training Module, SE-207. Both performance indexes and response curves showed that PSO and GA were better performed than the conventional tunings. While comparing optimization analyses, PSO reacted more aggressively than GA corresponding to slight overshoots but both optimizations produced very close error values. In conclusion, metaheuristic approach with proper parameter settings produces better control tunings as for the controlled loop demands aggressive control action would prefer PSO, and in contrast, a stabilized control prefers to apply GA for the nonlinear process.

show abstract

Section: Literature Reviewmentioning

confidence: 99%