Hierarchical batch-to-batch optimization of cobalt oxalate synthesis process based on data-driven model

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In this work, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is proposed to realize the effective optimization of a new synthesis process. Optimization efficiency is especially crucial for batch processes to improve the product quality and maximize the economic benefits. The traditional hierarchical optimization method can achieve a better effect, but it may lead to low efficiency since it requires more iterations. To further improve the optimization efficiency of a new batch process with high operational cost, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is proposed in this work. By introducing the linkage between hierarchies, the available information transmitting between hierarchies is addressed to assist and accelerate the modelling and optimization process. A performance assessment criterion based on the prior knowledge of similar processes is also proposed to further improve the optimization effect. Finally, the performance of the proposed method is verified through a simulation study of the cobalt oxalate synthesis process.

“…By carrying out the experiment at each sample, the result of final product quality can be acquired. The relationship between DSFs and the response output can be fitted as follows [18] :…”

Section: Rsm Based On Dodementioning

confidence: 99%

Section: Hierarchical-linked Batch-to-batch Optimization Strategy Bas...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical‐linked batch‐to‐batch optimization based on transfer learning of synthesis process

Chu

Wang

Wang³

et al. 2023

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Section: Introductionmentioning

confidence: 99%

“…[15] For the sake of obtaining superior optimization performances of the batch synthesis process, Jia et al further proposed a hierarchical optimization method for synthesis process operation. [16] Although batch-to-batch optimization can handle the instability factors such as NCO mismatch and disturbance between batches, it cannot solve the disturbance of batch process during operation. [6] Moreover, as the reactions in a single batch may vary dynamically along the operating time, [17] the optimization strategy within a batch is necessary, Yabuki and Macgregor proposed a mid-course correction (MCC) strategy, [18] which is to use decision points to divide the current batch process and make corresponding processing when running to the decision point, fill in data or anti-disturbance operation, so as to realize the optimization control within the batch.…”

Section: Introductionmentioning

confidence: 99%

Integrated operation optimization strategy for batch process based on process transfer model under disturbance

Chu

Wang

et al. 2022

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An integrated operation optimization strategy based on the process transfer model (PTM) is proposed in this work, which combines the batch-to-batch optimization method and the within-batch optimization method. The datadriven tool with the joint-Y partial least squares (JYPLS) model is utilized to transfer rich information from a similar old process to the new process to assist the establishment of the model of the new process. However, differences invariably exist between similar batch processes, which can bring about a fateful necessary condition of optimality (NCO) mismatch. Although the traditional batch-to-batch optimization method can overcome the problem of plantmodel mismatch between batches, it is helpless to deal with the problem of mismatch and disturbance during a single batch operation. For the sake of settlement of problems, the within-batch optimization method is introduced. The main advantages of the integrated operation optimization strategy are (i) the plant-model mismatch and disturbances within the batch or during batches can be solved, (ii) the suboptimal results of batch-to-batch optimization can be further compensated, and (iii) the optimization performance is better by discretizing the control profile into several intervals. Taking the cobalt oxalate synthesis process as a simulation study, the superior performance of the proposed strategy is illustrated.

Efficient cobalt oxalate synthesis process optimization via second‐order modifier adaptation with transfer learning

Chu,

Chai,

Wang

et al. 2023

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This paper proposes a second‐order modifier adaptation optimization based on the transfer model, which aims to improve the optimization efficiency. Although the traditional modifier adaptation strategy adds a bias to the model to meet the necessary condition of optimality (NCO), approximating only to the first‐order may neglect some key higher‐order information. Making use of as much higher‐order information as possible is an effective way to improve the efficiency of optimization. For this issue, by introducing the second‐order information into the modifier adaptation method to compensate the mismatch of higher‐order information during the optimization, the compensation of the mismatch between model and plant can get accelerated, thus improving the optimization efficiency. To overcome the difficulty of insufficient data for new process modelling, a process transfer model is also used to fit the relationship between manipulated variables and final product quality. The simulation study of the cobalt oxalate synthesis process shows that this strategy has better optimization efficiency.