TQS Integration, an international software consultancy company specializing in the provision of system architecture and application design, engineering, and system integration, today announced that they have been certified as a Google Partner with Google Cloud Partner Advantage.

Achieving Partner status means that TQS and Google can now share their combined knowledge and advanced technologies to provide the perfect end to end experience for their customers, from edge computing to automation, data historian to advanced data analytics and more.

“Obtaining Partner Status is a huge step into the future. It means recognition of our unique advanced technical expertise, and the ability to mutually share our learnings across a larger customer base with the Google team” says Rory Sheehan, Global Strategic Account Manager.

As a start, TQS and Google will focus their partnership on the Pharma / Lifesciences Industry where they have many mutual customers. Combined, their expertise and systems will allow customers to access manufacturing data, R&D data, operational data, and predictive data to streamline operations and quality.

“Our services will allow data sharing in a much wider capacity, both within the business and externally where required. With this partnership program and all our ongoing TQS developments, we are bringing customers, industries and partners into the future of manufacturing and that future is fuelled by data.” says Rory.

TQS has always been recognised for providing customers with an unrivalled service and advanced engineering, and this new Google partnership will continue to advance them into the future of Industry 4.0, IIoT and Edge Computing.

About TQS Integration

TQS Integration is a global data solution company specializing in the provision of system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with extensive client base in the Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.

For information, please contact us.

Machine Learning (ML) has seen an exponential growth during the last five years and many analytical platforms have adopted ML technologies to provide packaged solutions to their users. So, why has Machine Learning become mainstream?

Let’s take a look at Technically Multivariate Analysis (MVA). While many algorithms have been widely available for a long time, MVA is still considered a subset of ML algorithms. MVA typically refers to two algorithms:

As such, MVA has become a de facto standard in manufacturing batch processing and others. Some typical use cases are:

In principle, industrial datasets are not different from other supervised or unsupervised learning problems and they can be evaluated using a wide range of algorithms. Multivariate Analysis was preferred because it offered global and local explainability. MVA models are multivariate extensions of the well understood linear regression that provide weights (slope) for each variable. This enables critical understanding and optimization of underlying process dynamics which is a very important aspect in manufacturing.

NEW CHANGES IN INDUSTRIAL MACHINE LEARNING

In the past, many ML algorithms were considered black box models, because the inner mechanics of the model were not transparent to the user. These model types had limited utility in manufacturing since they could not answer the WHY and therefore lacked credibility.

This has very much changed. Today, model explainers in ML are a very active field of research and excellent libraries have become available to analyze the underlying model mechanics of highly complex architectures.

The following shows an example of applying ML technologies to a typical MVA project type. In the original publication (https://journals.sagepub.com/doi/10.1366/0003702021955358 ), several preprocessing steps have been studied together with PLS to build a predictive model. All steps were performed using commercial off the shelf software that manually worked the analysis.

Using ML pipelines, the same study can be structured as follows:

pipeline=Pipeline(steps= [('preprocess', None), ('regression',None)])
preprocessing_options=[{'preprocess': (SNV(),)},
                       {'preprocess': (MSC(),)},
                       {'preprocess': (SavitzkyGolay(9,2,1),)},
                       {'preprocess': (make_pipeline(SNV(),SavitzkyGolay(9,2,1)),)}]

regression_options=[{'regression': (PLSRegression(),), 'regression__n_components': np.arange(1,10)},
                    {'regression': (LinearRegression(),)},
                    {'regression': (xgb.XGBRegressor(objective="reg:squarederror", random_state=42),)}]
param_grid = []
for preprocess in preprocessing_options:
    for regression in regression_options:
        param_grid.append({**preprocess, **regression})
search=GridSearchCV(pipeline,param_grid=param_grid, scoring=score, n_jobs=2,cv=kf_10,refit=False)

This small code example manages to test every combination of prepossessing and regression steps, then automatically select the best model. [A combination of SNV (Standard Normal Variate), 1st derivative and XGBoost showed the highest cross validated explained variance of 0.958].

The transformed spectra and the model weights can be overlaid to provide insights into the model mechanics:

Conclusion

Multivariate Analysis (MVA) has been successfully applied in manufacturing and is here to stay. But there is no doubt that Machine Learning (ML) data engineering concepts will be widely applied to this domain as well. Pipelines and autotuning libraries will ultimately replace the manual work of selecting data transformation, model selection and hyper parameter tuning. New ML algorithms and Deep Learner, in combination with local and global explainer, will expand Manufacturing Intelligence and provide key insights into Process Dynamics.

Special Thanks

Thanks to Dr. Salvador Garcia-Munoz for providing code examples and data sets.

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Detailed equipment & batch data models set up by pharmaceutical and biotech companies have enabled the creation of equipment centric machine learning (ML) models for example, batch evolution monitoring. The next step is to extend the existing equipment centric models and create process or end-to-end models.

The challenge is that the current data models do not fully support the extension:

·        Equipment models are based on the ISA-95 structure and reflect only the physical layout of the manufacturing facilities.

·        Batch Execution Systems (BES) are integrated using ISA-88 and entail only equipment that is controlled by the batch execution system. Often BES systems are set up to execute single unit procedures and subsequent processing steps are executed separately.

·        Management Execution Systems (MES) typically map the entire process and material flow but as a level 3+ system is difficult to integrate into a data modelling pipeline.

·        There are also facilities that use paper-based process tracking instead of MES\BES, which makes traceability even more challenging.

Batch-to-Batch traceability can quickly become very complex especially when many different assets are involved. The following shows an example of a reactor train in a biotech facility:

It shows all the different product pathways from reactor ‘01’ to the final processing step, as an example in red: 01, 11, 22, 33, 44. At any moment in time, the other reactors are either being cleaned or used for a parallel process.

Such a process is difficult to model in a BES or MES system and real time visibility or historical analysis is very challenging. This is especially true if subsequent processing steps are to be included (Chromatography, Fill and Finish, ....)

The missing link to model the different pathways is to integrate each transfer between reactors or equipment. OSIsoft AF offers the AF Transfer model that is fully integrated in the AF system. AF Transfer event can be defined with the out-the-box properties:

·        Source Equipment

·        Destination Equipment

·        Start Time

·        End Time

The AF Transfer model has a lot of the same features that AF event frames offer. Transfers can be templated and through the in-and-outflow ports defined in different granularities.

Once the transfer between equipment has been defined, batches can be traced back in real time with or without using the batch id. This is possible through the equipment and time context of the transfer model:

In this case, starting from the end reactor ‘44’ all previous steps can be retraced by going backwards in time and using the source-destination equipment relationships

The implementation requires a data reference to configure each transfer. The configuration user interface requires the following attributes:

·        Destination Element: Attribute of the destination Element

·        Name: Name of the transfer

·        Optional: Description, Batch Id and Total

The result is transfer logs can be matched up to the corresponding unit procedures by time and equipment context as shown below:

As shown in this example, the end time of transfer log 'Transfer Id S7MZUDGK' matches the start time of unit procedure: "Batch Id WNJ6H99R". The entire pathway can now be reconstructed in one query.

Conclusion

The sequence of discrete processing events such as unit procedures can be modelled using the OSIsoft AF Transfer class. The resulting transfer logs allow retracing the process backwards in time by using the source-destination relationship of the transfer model. Modelling the process flow is key to expanding equipment centric ML models.

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Have you ever wondered if it were possible to predict process conditions in manufacturing? Know what is likely to happen before it actually happens in your business processes? Digital Twin might just be your answer.

There are several different definitions of Digital Twins or Clones and many use them interchangeably with terms such as Industry 4.0 or the Industrial Internet of Things (IIOT). Fundamentally, Digital Twins are digital representations of a physical asset, process or product, and they behave similarly to the object they represent. The concept of Digital Clones has been around for some time. Earlier models were based on engineering principles and approximations, however they required very deep domain expertise, were time consuming and were limited to a few use cases.

Today Digital Clones are virtual models that are built entirely by using massive historical datasets and Machine Learning (ML) to extract the underlying dynamics. The data driven approach makes Digital Clones accessible for a wide range of applications. Therefore, the potential for Digital Twins is enormous and includes process enhancements\optimization, equipment life cycle management, energy reductions, safety improvements just to name a few.

Building digital clones require:

1.      A large historical data set or data historian

2.      High data quality and sufficient data granularity

3.      Very fast data access

4.      A large GPU for the model development and real time predictions

5.      A supporting data structure to manage the development, deployment, and maintenance of ML models

The following shows the application of a Digital Twin to a batch process example. The model is built with 30 second interpolated data using a window of past data to predict future (5 min) data points:

So, what’s all the hype of Digital Clones? Well, not only are they able to predict process conditions, they also provide explanatory power on what drives the process - the underlying dynamics. The following dashboard shows a replay of this analysis including the estimate of the model weights:

Conclusion

In summary, the availability of enterprise level data historians and deep learning libraries allow Digital Clones to be implemented on the equipment and process level throughout manufacturing. The technology allows a wide range of applications and offer an insight into the process dynamics that were not previously available.

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Multivariate Analysis (MVA) is a well-established technique to analyse highly correlated process variables. It is well known in batch, but also successfully applied in discrete or continuous processing. In comparison to single variable applications, for example statistical process control, MVA has shown to be superior in the detection of process drifts and upsets. In practice, the implementation of MVA requires two different data structures or models:

Event Frames are usually autogenerated from the batch execution system (BES) and reflect the logical\automation sequences for recipe execution. Both AF Elements and Event Frames are  being used to create MVA models and calculate statistics. Below is an example of a multivariate model that combines the autogenerated Event Frame “Unit Procedure” and process variables in the Element: “Bio Reactor 0”:

This type of analysis is  typically used for batch-to-batch comparison (T2 and speX statistics) and batch evolution monitoring in the pharmaceutical, biotech and chemical Industry.

Challenge

One of the shortcomings of using automation phases is that they  seldom  line up with time frames that are critical for the underlying process evolution (process phases). Often there is a mismatch in the granularity, process phases are either longer or shorter in duration compared to the automation phases. Also start and end might be based on specific process conditions, for example temperature, batch maturity, online measurements and others. The mismatch between automation and process phases causes misalignment in the MVA model and a broadening of the process control envelopes. . The resulting models are often not optimal.

Solution

SEEQ has developed a platform that excels in creating time series segments as well as time series data cleansing and conditioning. The platform provides several different approaches to define very precise start and end condition.  The following show the definition of a new capsule based on a profile search that solely focuses on the process peak temperature:

These capsules can be utilized in other applications through an API and blended with other PI data models to create very precise multivariate models:

Benefits

Multivariate Analysis is a powerful method to analyze highly correlated process data. It depends on  equipment\process models and time series segments. OSIsoft PI provides data models for both. And typically time segments are automatically populated from a BES or MES systems. SEEQ provides new capabilities to create highly precise time segments called capsules, that refines the MVA analysis and creates meaningful process envelops. The integration is seamless since both systems provide powerful API’s to their time series data and models. The resulting MVA models target specific process phases that can be used to create improved process control limits or regression analysis.

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Most biotech and pharmaceutical companies are adopting Process Analytical Technology (PAT) in manufacturing to provide real time operational insights that allows better control and leads to higher yields, purity, and\or shorter cycle times.

However, to integrate PAT data into an existing data infrastructure has been challenging. Because each PAT – also a single measurement – is a spectrum consisting of a list or array of values (e.g. time, channel, value) that cannot be stored in a classical data historian.

This graph shows you several spectra forming a multi-dimensional time series:

integrate PAT data

Spectra are often stored in SQL type databases as plain tables, separate from the other manufacturing data stored in the historian. The main problem with this approach is the loss of equipment and batch context. This is problematic to any subsequent analysis.

So why are spectra stored separately? Because most industrial data historian store values as simple time series in different data types, typically bool, int, float and string. Each time series point is a tuple of a timestamp and a single value (scalar). For PAT and other use cases, it would be required to extend the existing data shapes to accommodate vectors, matrices, and tensors:

There are many use cases for these data structures:

In the OSIsoft Asset Framework, extending the Historian is accomplished by deploying a new source and linking it to a time series database that supports time-based vectors, matrices, and tensors:

The RAMAN spectra are attributes on the unit\vessel or located on the RAMAN equipment. Therefore, extending the existing OSIsoft AF data model allows the measurements to be analyzed in the present batch or event frame context:

Conclusion

Classical historians have been developed for scalar time series information. This has worked for most sensor data type, but they cannot accommodate higher dimensional time series information. The solution is to extend the existing historian with databases that allow a more flexible schema. This results in better utilization of existing equipment and batch context that enables context specific analysis.

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