To address poor mixing uniformity, unstable control accuracy, and limited flexibility in concentration adjustment during liquid fertilizer deep application, this study developed an online liquid fertilizer blending system integrating a Venturi injector, a helical static mixer, an electrical conductivity (EC) detection module, and a whale optimization algorithm (WOA)-optimized fuzzy PID controller. EC calibration tests, Simulink control simulations, and bench tests were conducted to evaluate the accuracy of calibration-based indirect concentration estimation, dynamic response, blending stability, and outlet uniformity. The EC calibration model used to indirectly estimate the preparation concentration of the tested fertilizer solution had a coefficient of determination of 0.997, with validation errors within 7.23%. Under the specified delay-free nominal plant model, the simulated overshoot and settling time of the WOA-optimized fuzzy PID controller were approximately 6.5% and 3 s, respectively. Bench tests of the system implementing this controller showed a blending error of 7.94%, an average measured response time of approximately 4.5 s, and a maximum spatial coefficient of variation of 8.95%. These findings provide a basis for developing integrated concentration-monitoring and feedback-control systems for on-demand liquid fertilizer blending in deep-application equipment.
No takes yet. Share an insight, caveat, or question.
Xing et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: