Run‐to‐run optimization of batch processes with self‐optimizing control strategy

2021

IEEE Access

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To predict key variables of complicated batch processes, the long short-term memory (LSTM) soft sensor is developed to deal with both data nonlinearity and dynamics. To extract proper historical samples and implement the real-time modeling scheme with model updating strategy, the just-in-time learning (JITL) algorithm is widely used at the data selection stage of LSTM soft sensor. However, the multiphase issue of batch processes are not considered for the conventional JITL-LSTM soft sensor. In this paper, a multiphase Mahalanobis distance based JITL framework is developed to integrate the phase recognition strategy into the similarity measurement and data selection scheme, by which an extra step of phase identification can be avoided and the accuracy of JITL can be significantly improved. Thus, batch samples from different operating phases can be recognized without an additional phase identification step. By the use of the Mahalanobis Distance based JITL-LSTM Soft Sensor, the probability of data mismatch can be significantly reduced so that the accuracy of quality prediction can be promoted. Two simulation cases are provided to verify the effectiveness of the proposed method consisting of a fed-batch reactor process and the penicillin fermentation process.

Section: A a Fed-batch Reactor Processmentioning

confidence: 99%

Improved Mahalanobis Distance Based JITL-LSTM Soft Sensor for Multiphase Batch Processes

2021

IEEE Access

Self Cite

“…Thus, the only manipulated variable is the feed rate of reactant B. The detailed mathematical model of the process is presented in Appendix B [1].…”

Section: Case Studies a A Fed-batch Reactor Processmentioning

confidence: 99%

“…Nowadays, the proportion of batch processes in modern industry is increasing rapidly due to the growing demand of high-value-added products (e.g., food, pharmaceuticals, semiconductors, polymers, etc.) [1]- [5]. It is noted that the quality of batch process products should be paid extreme attention to, although online quality variables are usually difficult to be measured.…”

Section: Introductionmentioning

confidence: 99%

LSTM Soft Sensor Development of Batch Processes With Multivariate Trajectory-Based Ensemble Just-in-Time Learning

et al. 2020

IEEE Access

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To implement the quality prediction scheme for batch processes, long short-term memory (LSTM) neural network is a feasible tool to handle with the process dynamics and nonlinearity. However, a global LSTM soft sensor suffers a decline in performance facing batch-to-batch variations. To overcome the batch diversity problem and take advantage of LSTM model, a multivariate trajectory based ensemble just-in-time learning strategy is proposed in this paper. Different trajectory based similarity measurements are designed to extract historical batch trajectories which share similar spatial positions and trends. For each selected trajectory, an online local LSTM soft sensing model is constructed and the real-time quality prediction result for each local model can be obtained. Then, a weighting parameter is determined for each model by cross validation. Bringing together quality prediction results from different local models, the ensemble prediction result can be finally figured out. Two case studies are carried out to prove the effectiveness of the proposed methodology including a fed-batch reactor and the fed-batch penicillin fermentation process.INDEX TERMS Batch production systems, Ensemble just-in-time learning, long short-term memory, multivariate trajectory analysis, soft sensor, quality prediction.

“…Batch processes are widely used in modern industrial applications due to the growing requirements of producing high-value products, such as food, plastics, pharmaceuticals, and biological and semiconductor materials [1][2][3][4]. Since the products of batch processes often require extremely high quality, the problem of quality-relevant monitoring has received increased attention in recent years.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

et al. 2020

Processes

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To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.