Department of Electronics and Instrumentation Engineering, National Institute of Technology Silchar, Silchar, Assam, India.
Department of Basic Science and Humanities (Mathematics), Techno College of Engineering Agartala, Agartala, Tripura, India.
Environ Sci Pollut Res Int. 2024 Jul;31(35):48423-48449. doi: 10.1007/s11356-024-34418-z. Epub 2024 Jul 20.
The effectiveness of an aquaponic system significantly relies on the habitat provided for both the fish and plants. As an integral component of aquaponics, hydroponic cultivation benefits greatly from the controlled environment of a greenhouse. Within this environment, factors such as temperature, carbon dioxide levels, humidity, and light can be carefully adjusted to maximize plant growth and development. This precise regulation ensures an ideal growing environment, fostering the flourishing of plants and contributing to the overall success of the aquaponic ecosystem. This study presented a control approach for an aquaponic greenhouse system. It aims to keep the greenhouse climate parameters (temperature, CO concentration, and humidity) at their ideal levels. The proposed control strategy is a two-layered mechanism in which the first layer presents an optimization framework using particle swarm optimization (PSO) algorithm to give the setpoints for the controller, and the second layer demonstrates a constrained discrete model predictive control (CDMPC) strategy to maintain the desired trajectories received from the optimization layer. To validate the results obtained using PSO, this study incorporates genetic algorithms (GA) and assesses their performance in comparison. Given similar computational efficiency and low computational time for both algorithms, the optimal values identified by particle swarm optimization (PSO) are adopted as the setpoints. Two performance criteria, relative average deviation (RAD) and mean relative deviation (MRD), are derived to evaluate the tracking performance of the proposed CDMPC controller under external disturbances. A comparison of the proposed CDMPC with the PI controller is also offered. According to the comparison results, our proposed CDMPC performs better than the PI controller with lower RAD values (temperature, 1.1315; CO concentration, 0.9225; humidity, 2.547) and MRD values (temperature, 0.315; CO concentration, 0.25; humidity: 1.013). The controller is validated to be efficient by its strong control performance, highlighted by robustness, efficient setpoint tracking, and adequate disturbance rejection. This novel approach might prove to be a useful technique for developing environmental control strategies that can be used for potentially boosting production rates of aquaponic greenhouse systems, maximizing profitability, and reducing labor needs. By maintaining optimal conditions, it can enhance ecosystem health, improve yields, and streamline operations, paving the way for greater system performance and sustainability.
水培系统的有效性在很大程度上取决于为鱼类和植物提供的栖息地。作为水培系统的一个组成部分，水培栽培在温室的受控环境中受益匪浅。在这种环境中，可以仔细调整温度、二氧化碳水平、湿度和光照等因素，以最大限度地促进植物生长和发育。这种精确的调节确保了理想的生长环境，促进了植物的繁荣，并为水培生态系统的整体成功做出了贡献。本研究提出了一种水培温室系统的控制方法。其目的是将温室气候参数（温度、CO2 浓度和湿度）保持在理想水平。所提出的控制策略是一种两层机制，第一层使用粒子群优化（PSO）算法呈现一个优化框架，为控制器提供设定点，第二层呈现一个受约束的离散模型预测控制（CDMPC）策略，以保持从优化层接收到的期望轨迹。为了验证使用 PSO 获得的结果，本研究还纳入了遗传算法（GA）并评估了它们的性能比较。由于这两种算法具有相似的计算效率和低计算时间，因此采用粒子群优化（PSO）确定的最优值作为设定点。为了评估在外部干扰下提出的 CDMPC 控制器的跟踪性能，导出了两个性能标准，相对平均偏差（RAD）和平均相对偏差（MRD）。还提供了与 PI 控制器的比较。根据比较结果，与 PI 控制器相比，RAD 值（温度为 1.1315；CO2 浓度为 0.9225；湿度为 2.547）和 MRD 值（温度为 0.315；CO2 浓度为 0.25；湿度为 1.013）较低的提出的 CDMPC 控制器具有更好的性能。该控制器通过其强大的控制性能（包括鲁棒性、高效的设定点跟踪和充分的干扰抑制）得到了验证，是有效的。这种新方法可能是开发环境控制策略的有用技术，该策略可用于潜在地提高水培温室系统的产量，提高盈利能力并减少劳动力需求。通过维持最佳条件，可以增强生态系统的健康，提高产量并简化操作，为系统的更大性能和可持续性铺平道路。
