CO2 Control System Design Based ON Optimized Regulation Model

Existing carbon dioxide concentration (CO 2 ) control systems commonly use a quantitative replenishment of CO 2 , without considering the effects of multiple environmental factors on a plant’s photosynthetic rate and its characteristic impact on CO 2 demand, resulting in improper control of CO 2 concentration. Accordingly, in this article, a nested combination experiment for the photosynthetic rate of cucumbers is presented. In order to obtain a continuous carbon dioxide response curve, the photosynthetic rate prediction model is established using the cucumber experimental data based on the support vector machine (SVM) algorithm. The network of the support vector machine photosynthetic rate prediction model is used as an optimal objective function and the improved artificial fish swarm algorithm (IAFSA) is employed to search for the saturation of CO 2 in a multidimensional nesting condition. Further, the optimal CO 2 regulation model based on multi-factor coupling is established using the results of the above-mentioned experiments. Moreover, XOR verification of the proposed model showed that the maximal relative error of the proposed optimal CO 2 regulation model is 3.898%. Consequently, using a wireless sensor network platform, a multi-sensor fusion-based CO 2 control system is realized and verified. The verification showed that the average relative error between the target CO 2 value and the actual CO 2 value is 2.88%. At the same time, the average photosynthetic rate (Pn) of the crop increased by 26.94% compared to the contrasting region, which proves that the proposed system can achieve a stable and reliable operation, greatly improving the efficiency of the environment of the facility.