Browsing by Author "Qazeem Opeyemi Ogunlowo"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item COMPARATIVE ANALYSIS OF THE MICRO-ENVIRONMENTS OF SINGLE-SPAN TRIPLE-LAYERED GREENHOUSES UNDER DIFFERENT AIRFLOW SYSTEMS(Journal of Agricultural Engineering and Technology (JAET), 2023) Timothy Denen Akpenpuun; Qazeem Opeyemi Ogunlowo; Wook-Ho Na; Anis Rabiu; Misbaudeen Aderemi Adesanya; Prabhat Dutta; Ezatullah Zakir; Oluwasegun Moses Ogundele; Hyun-Woo LeeThe microclimate within indoor production systems is intricately linked to the macroclimate of their surrounding environment, resulting in dynamic fluctuations over time. This study investigated the variations in thermal conditions within two adjacent single-span tunnel greenhouses, each utilizing distinct airflow systems: one equipped with circular airflow fans (HUMITEM-GH) and the other with horizontal airflow fans (Conv-GH). Temperature (Ta), relative humidity (RH), vapor pressure difference (VPD), solar radiation (SR), and carbon dioxide (CO2) levels were monitored using standard methods, and data analysis was conducted using MINITAB software. Statistical analysis included descriptive statistics, analysis of variance (ANOVA), and t-tests. The results of the experiment showed that in the HUMITEM-GH and Conv-GH, the minimum daytime Ta, RH, VPD, SR, and CO2 were 6.24°C, 39.22%, 0.04 kPa, 0.37 W/m², and 318 ppm, and 5.57°C, 19.73%, 0.03 kPa, 0.69 W/m², and 355 ppm, respectively. Whereas the maximum daytime Ta, RH, VPD, SR, and CO2 were 26.81°C, 97.20%, 2.07 kPa, 520.74 W/m², and 2083 ppm, and 26.20°C, 97.79%, 2.25 kPa, 448.68 W/m², and 1514 ppm, respectively. In the same context, climate parameters at night ranged from 6.07°C to 23.77°C, 46.28% to 97.86%, 0.03 kPa to 1.57 kPa, and 344 ppm to 2669 ppm, respectively, for the HUMITEM-GH, and from 5.57°C to 20.84°C, 20.76% to 97.75%, 0.03 kPa to 1.73 kPa, and 356 ppm to 1081 ppm, respectively, for the Conv-GH. There were significant differences between the two greenhouses both in the daytime and nighttime, with the HUMITEM-GH having higher and near-optimal climate parameters than the Conv-GH. These results have shown that the newly developed HUMITEM air circulation fan has superior air circulation and flow capabilities compared to conventional air circulation fans. This information may be of interest to farmers, horticulturists, and researchers involved in protected crop/plant production, offering valuable guidance for optimizing greenhouse environments.Item Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach(Elsevier, 2024) Misbaudeen Aderemi Adesanya; Hammed Obasekore; Anis Rabiu; Wook-Ho Na; Qazeem Opeyemi Ogunlowo; Timothy Denen Akpenpuun; Min-Hwi Kim; Hyeon-Tae Kim; Bo-Yeong Kang; Hyun-Woo LeeThe control of indoor temperature in greenhouses is crucial as it directly impacts the crop’s thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can sometimes make HVAC system work at full capacity when only half that capacity is needed. In contrast, the proportional-integral-derivative (PID) controller, provides precise control based on its P, I, and D parameters. However, it lacks a formal design procedure for optimizing a specified objective function. Previous studies have utilized conventional PID tuning approaches to track room setpoint temperature for residential buildings, data centers, and office buildings, with limited research in greenhouse applications. To address this gap, this study proposes a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters, by tracking the setpoints and energy consumption of a greenhouse planted with tomatoes. This approach is different from the typical method of using the trained RL agent directly in HVAC controls. Through a self-made TRNSYS-Python cosimulation framework, the DRL agent interacts directly and in real time with the greenhouse and its plants. Consequently, optimized PID parameters were established and tested in the simulated environment. The resulting performance, in terms of both energy consumption and its ability to maintain the crop’s comfort temperature, was compared with the simulated on/off and manually tuned PID controllers. Compared to the on/off baseline control, the proposed PID optimized parameters reduce energy use by 8.81% to 12.99%, and the manually tuned PID parameters with the Ziegler-Nichols tuning method reduce energy use by 7.17 %. Additionally, the proposed method had a deviation of 2.07% to 3.13%, while the manually tuned PID controller and the on/off controller had deviations of 7.27% and 3.27%, respectively, from the minimum comfortable temperature. This study serves as a framework for improving the energy efficiency of greenhouse HVAC system operations.