粒子群最適化を用いた線形二次レギュレータ法による微細藻類成長の最適制御

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🔬研究者:LQR重み行列の自動調整にPSOを適用する手法と収束挙動の詳細は、生物プロセス制御の最適化研究に有用。

🏢実務担当者:微細藻類培養の生産性を高める制御設計の参考になるが、直接的な開示・調達実務への示唆は限定的。

🏛政策担当者:CCUSやバイオ燃料の技術実装支援策を検討する際、生物学的CO2固定の制御最適化が生産性向上に寄与する点を参考にできる。

Microalgae represent a significant and promising resource for a multitude of industrial applications. The lipids extracted from microalgae are used to produce biofuels, as dietary supplements for humans and animals, and as sources of vitamins or proteins in the pharmaceutical industry. Furthermore, microalgae serve as agents for carbon dioxide capture and wastewater treatment (Kumar et al., 2010). This study examines the optimal regulation of microalgal proliferation by controlling variables such as carbon dioxide and nutrients using the Linear Quadratic Regulator (LQR) methodology. Within the framework of the LQR approach, the weight matrices Q and R are commonly established through trial and error, a time-consuming process that does not guarantee a globally optimal solution. This manuscript advocates implementing Particle Swarm Optimization (PSO) to precisely determine the matrix values Q and R, thereby supplanting traditional manual tuning. PSO is a metaheuristic algorithm predicated on the collective behaviors observed in avian flocking and piscine schooling. Compared with the Firefly Algorithm previously applied to this problem, PSO offers a simpler velocity-position update mechanism, fewer algorithmic control parameters to calibrate, and a lower computational burden per iteration, while still maintaining a strong balance between exploration and exploitation of the search space. The principal novelty of this study lies in the application of PSO rather than the Firefly Algorithm to automatically tune the LQR weight matrices for the Thornton microalgae growth model, accompanied by an explicit, iteration-by-iteration account of the convergence behavior of the swarm, an aspect that has not been reported in prior LQR-based microalgae control studies. The use of PSO is expected to enhance microalgal growth, resulting in substantially increased biomass production. Simulation findings indicate that the LQR approach in isolation yields a 21.7% increase in algal concentration from the baseline concentration, whereas the LQR method, when augmented by PSO optimization, results in an approximate 64.5% enhancement in algal concentration from the initial level, thereby underscoring the advantage of PSO-assisted weight optimization over both the unoptimized LQR controller and the previously reported LQR–Firefly Algorithm combination.

• semanticscholar https://journal3.upgris.ac.id/index.php/aksioma/article/download/891/554first seen 2026-10-06 05:31:24