Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

Oliveira, Rodrigo Rocha de; Ávila, Claudio; Bourne, Richard A.; Muller, Frans L.; Juan, Anna de

doi:10.1007/s00216-020-02404-2

Cited by 24 publications

(14 citation statements)

References 38 publications

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“…The preprocessing steps included the application of a moving average of consecutive NIR observations followed by standard normal variate (SNV) normalization. For a detailed description of the experimental procedure and the visualization of the spectral data, the reader is referred to (Avila et al, 2020;de Oliveira et al, 2020). Some batches were selected from the previous work and additional faulty batches were used for model validation.…”

Section: Process 1: Fluidized Bed Drying Of Pharmaceutical Granulesmentioning

confidence: 99%

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Oliveira

Juan

2022

Front. Anal. Sci.

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Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

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Section: Process 1: Fluidized Bed Drying Of Pharmaceutical Granulesmentioning

confidence: 99%

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Oliveira

Juan

2022

Front. Anal. Sci.

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“…erefore, the imbalance between supply and demand requires suppliers to improve customers' awareness of investment and risk management on the basis of sustainable business development. At the same time, under the background of the information age, it innovates the financial service model with the help of information technology [16,17].…”

Section: Economic Development Model Based Onmentioning

confidence: 99%

Rural Revitalization and Digital Financial Economic Development Model Based on Multimodal Sensor Fusion

Ping

2022

Mobile Information Systems

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Since the 19th National Congress of the Communist Party of China, the key to rural development has been to solve the problem of rural development. The “three rural issues” is a critical issue for the country’s economy. In addition, the implementation of the Rural Revitalization Strategy under the environment of data financial and economic development can be more convenient and efficient. However, the traditional sensor fusion technology still has the disadvantages of low efficiency and high cost in the application of Rural Revitalization and digital financial economic development model. In order to solve the shortcomings of this technology, this paper proposes a multimodal sensor fusion technology combined with BP neural network (BPNN, a commonly used feedback neural network) and Kalman filter (a linear filtering algorithm), and it constructs a rural economic development model combined with digital finance. This paper uses this technology to study the economic development model. Firstly, the research method is to visit and investigate the residents and basic economic situation of a village and then use the economic development model constructed in this paper to carry out the economic analysis of the BPNN model and Kalman filter model. It includes Mohr scatter diagram, economic income, industrial investment, product export, and household consumption. The results show that the Mohr scatter diagram of the village changes from the third quadrant (−0.7, −0.5) to the first quadrant (−0.7, 0.3); the difference between the economic income calculated by the Kalman filter algorithm and BPNN algorithm and the actual value shall not exceed 10%. The rural industrial investment index increased by 56.08, the product export index increased by 30.11, and the household consumption index increased by 13.10. It shows that the economic development model constructed in this paper has achieved good results.

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“…The underlying idea/notion in performing data fusion (DF) is that the result of the fused dataset will be more informative than the individual datasets. Thus, this procedure will provide a more enhanced overview of the studied system with a more in-depth understanding and data-driven decision-making [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

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The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

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Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

Abstract: Funding from the European Community's Framework program for Research and Innovation Horizon 2020-2020 under grant agreement number 637232, related to the ProPAT project is acknowledged. Funding from Spanish government under the project CTQ2015-66254-C2-2-P is also acknowledged.

Cited by 24 publications

References 38 publications

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Rural Revitalization and Digital Financial Economic Development Model Based on Multimodal Sensor Fusion

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

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