Case Study on the Application of an Advanced Control and Optimization System for a Fine Chemicals Unit at a Certain Factory
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Case Introduction
Zhejiang Bangye Technology has employed multivariable model predictive control to design and develop an advanced control system for a fine‑chemical plant. Following the implementation of APC, the plant’s automation rate has exceeded 95%, fluctuations in key process parameters have been reduced by more than 50%, product quality has become significantly more stable, plant consumption has declined, and operators’ workload has been substantially alleviated.
Figure 1 shows the feed‑control performance. Because this unit is equipped with multiple recovery systems and reactors, it is essential to maintain a specified ratio of fresh to recycled feed and stable feed pressure. Exceeding the allowable feed‑pressure range poses significant safety risks, while deviations in the feed‑ratio can lead to operational difficulties in downstream units. Under manual operation, feed pressure fluctuates considerably, and maintaining the desired feed ratio proves challenging. For such multivariable processes with strong coupling and interdependencies, an APC system automatically predicts changes in key process variables based on its model and proactively adjusts control inputs, ensuring that both feed pressure and feed ratio remain within the required operating limits. In the figure, the red line represents feed pressure, the blue line denotes the feed ratio, and the dashed lines indicate the respective process operating ranges. As shown, after APC implementation, both feed pressure and feed ratio exhibit marked improvements.
Figure 2 shows the APC’s control performance for key process variables during production rate adjustments. When the setpoint for the production rate (in red) is changed, the advanced control system automatically adjusts the feed rate (in black). Even under significant changes in the production rate, the system maintains nearly constant temperatures (in blue) and pressures (in green) within the unit. This demonstrates that, during production rate adjustments, the APC can effectively ensure the stability of critical process parameters, thereby reducing the defect rate caused by parameter fluctuations.
Figure 3: VOA Online Predictive Control Process. This APC system, while stabilizing key process parameters, achieves tight product‑quality control by incorporating a Virtual Online Analyzer (VOA) to predict product quality. The VOA provides real-time estimates of product quality, which, when integrated with a model predictive controller, enable effective quality control. The VOA is calibrated using trusted laboratory analysis samples to mitigate model bias and the effects of unmeasurable disturbances. Figure 3 illustrates the process architecture for implementing online predictive control via the VOA.
Figure 4 shows the product quality control performance after implementing VOA. Based on the product specifications, the plant sets control targets, and APC automatically adjusts key process parameters in response to VOA estimates, thereby ensuring product quality. Following the deployment of APC, actual product quality variability has decreased, instances of excessive purity have been reduced, and unit energy consumption has declined significantly.
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