The tricalcium neutralization process (TNP) is a key recovery process in the citric acid production process. The amount of calcium carbonate added needs to be controlled to ensure the pH of the reactants within the target range at the terminal of every batch. But the random initial pH has a great influence on the stability of the terminal pH. In this work, an iterative learning control (ILC)‐based control strategy is proposed to optimize the addition of calcium carbonate. First, the terminal iterative learning control (TILC) is performed and the optimal input of the process can be obtained. Then, an initial disturbance compensation controller optimized by ILC is proposed. The results of the TNP control experiments demonstrate that the suggested control strategy can suppress the disturbances and achieve terminal pH control.
Tricalcium neutralization process (TNP) is one of the key recover
process in citric acid production process. In this process, the amount
of calcium carbonate added needs to be controlled to ensure the pH of
the reactants within the target range at the end-point of every batch.
But the random initial pH of the fermentation broth impacts a lot on the
stability of the end point pH. In this work, an ILC-based control
strategy is proposed to optimize the addition of calcium carbonate.
First, the ILC is performed and the optimal input of the process can be
obtained after several batches. Second, an initial disturbance
compensation controller optimized by ILC is proposed. An experimental
platform for TNP is designed, and the results demonstrate the proposed
control strategy can achieve the end point pH control and suppress the
disturbance by the indeterminate initial pH value.
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