LabanalyticsChallenges for the analytical lab of the future

Dr. Michael Nilles Dr. Martin Wende Dr. Tatjana Levy

LabVision, Ludwigshafen am Rhein, 08.05.2019

Lab of the Future – Impact on Analytics

08.05.20192

Automation

Digitalization

Process Development

Lab of the Future – Impact on Analytics

08.05.20193

Automation

Digitalization

Process Development

Process Development + OptimizationVision: autonomous self optimizing lab-/mini-plant

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experimentssupported by robotics

design of experiments

analytics

most critical!

optimization

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autonomous self optimizing lab-/mini-plant

datamanagement

critical

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Process Development + OptimizationVision: autonomous self optimizing lab-/mini-plant

Optimal control of reactionby realizing flexible concentration and temperature profiles

Model predictive control & optimization

R e a c t o r

E n e r g y

QIC

Raw materialCrude product

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Analytics: Challenges

Robustness of e.g. spectroscopic probes (temperature, chemical resistance)

Autosampling of liquids under high pressure

High resolution in complex matrices

Easy chemometrics

…

08.05.20196

Lab of the Future – Impact on Analytics

Automation

Digitalization

Process Development

08.05.20197

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The Elemental Analysis Labs in Ludwigshafen

Lab Automantion

@ BASF

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Automation of Process Steps in the Lab

Dissolving Shaking

WeighingLiquid handling

Heating

Capping/decapping

Cleaning

Sample transportation

Melting

Combustion

08.05.20199

Plasma based Technologies in the Atomic Spectroscopy Lab

OES

MS

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Fully Automated Acid Digestion coupled to ICP-OES

1. Generation(1996-2005)

1. Generation(1996-2005)

1. Generation(1996-2005)

2. Generation(since 1998)

3. Generation(since 2005)

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Under Developoment at BASF: Modular Systems

4 x 1 x

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Current Challenges in Automation

Software:

Bidirectional communication between automation and analytical instrument

Hardware:

Less sample series („lot size 1“)

more flexibilities of the automated systems

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What comes next?

2019 2025

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Mobil Robots For Connecting the Dots

Kuka

MIR

Fraunhofer

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The Future of Lab Automation

1995 2005 2018 2025

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Lab of the Future – Impact on Analytics

Automation

Digitalization

Process Development

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Digitalization in R&D

PreexistingKnowledge,

Application Profile

High-Performance Computing- Modeling & Simulation- Design of Experiments

Lab Experiments

AnalyticsChemical Analysis

AnalyticsProperties,

Application Testing

Data-Driven Modeling

Next Iteration

New Application Profile

Ok New Product

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Status quo in the Laboratory

R&D productivity challengeso Isolated applications

o Incompatible and unconnected instruments & software systems across research environment

o Manual transfer & data handling that challenges data integrity

o Silos of data, context and meaning

o Accessibility of data

o Implicit know-how

o Find vs. Reproduce data

Most data do not talk with each other08.05.201919

Lab Systems Need To Be Interlinked

Connectedo Plug and Playo Interoperableo Data Integrityo Reliable

Automatedo Sample & data flowo Sample processingo Tracking & Monitoringo Decisions

Online accessible o Data at your

fingertipso Collaborationo Everywhere

Intelligento Machine Learningo Artificial intelligence

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True integration requires two things

Communication Communication standards

Data Exchange Data standards

A Communication Protocol alone leaves us with proprietary data structures.

Data ends up in silos.

A Data (File) Format alone is not suitable for systems where components

need to communicate. Copying files is not communication.

Both are needed for end-to-end integration

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Without Standards digital transformation of the lab is impossible.

Drivers for the analytical lab of the future

How do we deliver this?

Efficient optimization of processes with online analytics

Intelligent automatization within labs

Standardization for data valorization

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