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On the way to the cyber-physical lab

Requirements for smart laboratory instruments and interconnected systems in the modular lab of the future

Dr. Michael Maiwald (Bundesanstalt für Materialforschung und -prüfung (BAM))

Laboratories tend to be central hubs for chemical, biotechnological, pharmaceutical or foodstuff production. They play a key role in research and development, chemical analysis, quality assurance, maintenance and process control. For process development and optimization, process analytical technology (PAT) has proven to be a powerful tool to improve our understanding of processes, increase productivity, reduce waste and costs and shorten processing times.

Central analytical laboratories presently possess the scope and depth of chemical-analytical capabilities to support basic research, whereas in-process analytical laboratories are of utmost importance for the life cycle of processing facilities, being able to cover process development, scale-up support and process implementation, due to their proximity to the applications and the production environment. During production, in-process controls are often performed in these laboratories because many processing facilities are not yet fully equipped with the analytical technologies needed for such online processes [1, 2].

While many specialized laboratories with a substantial sample throughput have long been highly automated and have optimized their routine tasks, this is unfortunately not the case in most of the above mentioned laboratories due to the considerable complexity of the ever changing research challenges and unexpected problems arising in everyday production. Could they use digitization profitably without impairing creativity and diversity?

Industry 4.0 was designed for the manufacturing industry. Now the concept has reached the analytical lab.

Fig. 1 The digital twin is generally a container for model descriptions of the physical world (“operative instance”), e.g. of a laboratory. Things like samples, calibrated analytical methods or analytical results each carry their own digital description, such as sample information that can be obtained via barcode or a calibration or data evaluation model. What is new about Industry 4.0 is that improved models can be created on the basis of available data (red arrow at the bottom), which can in turn be used by laboratories (red arrow at the top), even for similar processes at other locations.

The aim of Industry 4.0 (see info box) is to make production processes more flexible through improved knowledge management using computers. To achieve this, a digital image of all products and production processes is used. A data container of this kind is called a “digital twin” (see Fig. 1). It interconnects the properties and requirements of products in the same way as the current settings and production formulas of the machines with their digital images. The machines, in turn, can use this to select the optimal production process for each individual product and start it at the most suitable time. This allows business processes to be optimized, and even completely new business models to be created.

“If old Daimler knew what Daimler already knows ...”

The wisdom of this German expression can be applied to any other big business for which, due to its size and complexity, knowledge management is enormously challenging. Such companies inevitably go about solving problems that have been solved before because that solution was not preserved. Collecting knowledge and making it available is often not as easy as it seems.

However, digitization can help immensely. For example, it is possible to use the collected digital images of all the products and production processes as the basis for further production facilities, even though facilities need not be similar at all for a variety of reasons. However, there is the opportunity to transfer and share basic principles.

A “stupid” production machine knows only itself. A “clever” production machine at least knows everyone else in the company and can learn from them. A “wise” production machine, however, knows all the production machines of its type and can learn from these. But that is not all: its manufacturer can optimize it. That does not necessarily require confidential information about the products manufactured on the machine. All this can also be hugely beneficial for the laboratory and its instrumentation, for the analytical methods used and for calibration.

Fully integrated and intelligently interconnected systems and processes

These days, analytical laboratories are primarily collections of isolated analytical techniques. They tend to be rigid in terms of loading samples and gathering analytical results. They are often integrated into a laboratory information management system (LIMS) and automated, in which case they are inflexible in their use, however. Other analytical techniques provide flexibility but take up a lot of time and effort for system maintenance throughout the process, from method development to data transfer. It would therefore be useful to combine the advantages: to use analytical instruments flexibly in terms of space and time, to combine them in new ways but to still maintain an overview of the data and the qualification of the instruments – a sort of modular laboratory with fully integrated and intelligently interconnected systems and processes.

This was already asserted in the first part of this series of articles [3]. In combination with simplified device communication, the “Module Type Package” (MTP) standard has been proposed as a possible solution for simplification. It could be used for a manufacturer-independent, functional description of analytical method automation (e.g. the integration into laboratory environments). The standard was developed in the process industry in order to integrate complex, self-contained units (modules) into automation environments, e.g. process control systems, using simple drag and drop operations.

What has now come into focus is the targeted selection of suitable analytical methods, sample preparations and calibrations from the knowledge pool and the transfer of these processes to the analytical instruments. An Industry 4.0 concept will find the appropriate solution if it is available in the knowledge pool. For example, a chromatographic method can be proposed and the suitable separation column found if similar separation problems have existed and been solved before.

What is cyber-physical?

Fig. 2 Integration into cyber-physical systems (CPS) requires smart laboratory instrumentation with certain properties.

However, the digital representation of all products and production processes as an optimal knowledge database, available to all, is only one part of an Industry 4.0 solution. What is new is the exchange and the feedback of information in both directions: from the products and production processes into the digital knowledge database and – on the basis of the collected data and the resulting optimized production recipes – vice versa as well. Lee [4] put this in a nutshell in his following definition: “Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa.”

For example, a chromatographic method would suggest an optimization if there is a better way to solve the separation problem available elsewhere. In order to achieve this, smart laboratory instruments (similar to sensors [5]) should possess the properties described in Figure 2.


In technological terms, the cyber-physical lab requires IT and measurement know-how to be combined. Users or service providers must provide not only the measurement technology, but also a suitable data and IT infrastructure for implementation in the laboratory or company in order to collect and transfer data and know-how. Implementing edge computing, IT platforms, cloud services or hybrid cloud concepts, together with suitable security structures, are solutions that have already started to become available. However, defining and harmonizing the wide variety of data formats and ensuring secure data transfers are the biggest challenges. This will require close cooperation between specialists from different disciplines and professions.



Industry 4.0 is the intelligent interconnection of industrial machines and processes with the help of information and communication technology. Using intelligent networks can provide companies with many potential benefits, including flexible manufacturing, convertible factories, customer-centered solutions, optimized logistics, better use of data, predictive maintenance and recycling management systems that save resources.

The “Platform Industry 4.0” is a project of the German federal government's High-Tech Strategy 2020 action plan [6].


Category: Laboratory Management | Smart Lab

[1] Eisen, K., Eifert, T., Herwig, C., Maiwald, M. (2020) Current and future requirements to industrial analytical infrastructure – part 1: process analytical laboratories, Anal. Bioanal. Chem., 412, 2027-2035
[2] Eifert, T., Eisen, K., Maiwald, M., Herwig, C. (2020) Current and future requirements to industrial analytical infrastructure – part 2: smart sensors, Anal. Bioanal. Chem., 412, 2037-2045
[3] Maiwald, M. (2020) The internet of things in the lab and in process – The digital transformation challenges for the laboratory 4.0, 2020 Apr 01, https://q-more.chemeurope.com/q-more-articles/313/the-internet-of-things-in-the-lab-and-in-process.html (Ger.: Das Internet of Things in Labor und Prozess – Herausforderungen des digitalen Wandels für das Labor 4.0, 2020 Apr 01, https://q-more.chemie.de/q-more-artikel/313/das-internet-of-things-in-labor-und-prozess.html)
[4] Lee, E. A., Cyber physical systems: design challenges. EECS Dep., University of California, Berkeley. 2008.https://www2.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-8.pdf (accessed on 2020 May 26)
[5] Maiwald, M. (2018) Voll integrierte und vernetzte Systeme und Prozesse – Perspektive: Smarte Sensorik, Aktorik und Kommunikation, ATP Magazin 60(10), 70–85, DOI: 10.17560/atp.v60i10.2376
[6] BMWi, https://www.plattform-i40.de/PI40/Navigation/EN/Home/home.html (acessed on 2020 Jun 03)

Date of publication: 21-Jul-2020

Facts, background information, dossiers

  • Lab 4.0
  • modular lab
  • chemical analysis
  • automation environment
  • cyber-physical systems

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  • Authors

    Dr. Michael Maiwald

    Michael Maiwald, born in 1967, is head of the division Process Analytical Technology at Bundesanstalt für Materialforschung und -prüfung (BAM) in Berlin, Germany. He is physico-chemist who in 1994 graduated from Ruhr-University Bochum, Germany, where he also received his doctorate. Subseque ... more

    Dr. Björn Meermann

    Björn Meermann, born in 1982, studied chemistry at the University of Münster and obtained his doctorate in 2009 in the working group of Prof. Dr. Uwe Karst. This was followed by a postdoctoral period of almost two years at the University of Ghent (Belgium) in the working group of Prof. Dr. ... more

    Dr. Martina Hedrich

    Martina Hedrich studied chemistry at Freie Universität Berlin (FUB), where she also received her doctorate in inorganic chemistry in the field of X-ray structure analysis. During her postdoc at the Hahn Meitner Institute in Berlin, she dedicated her work to trace analysis in human tissue sa ... more

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