Table of Contents

Cover

Title Page

Preface

Chapter 1: Introduction

1.1 Life Sciences – A Definition
1.2 Automation – A Definition
1.3 History of Automation
1.4 Impact of Automation
References

Chapter 2: Automation in Life Sciences – A Critical Review

2.1 Overview
2.2 Definitions and Basics
2.3 Automation in Bioscreening
2.4 Automation in Chemical Sciences
2.5 Automation in Analytical Measurement Applications
2.6 Requirements for Automating Analytical Processes
References

Chapter 3: Automation Concepts for Life Sciences

3.1 Classification of Automation Systems
3.2 Classification Concept for Life Science Processes
3.3 Robot Based Automation Systems
3.4 Degree of Automation
3.5 Statistical Evaluations
References

Chapter 4: Automation Systems with Central System Integrator

4.1 Centralized Closed Automation System
4.2 Centralized Open Automation System
4.3 Decentralized Closed Automation System
4.4 Decentralized Open Automation System
References

Chapter 5: Automation Systems with Flexible Robots

5.1 Centralized Closed Automation System
5.2 Centralized Open Automation System
5.3 Decentralized Automation System
5.4 Automation Systems with Integrated Robotics
References

Chapter 6: Automated Data Evaluation in Life Sciences

6.1 Specific Tasks in Data Evaluation in Analytical Measurements
6.2 Automation Goals
6.3 System Design
6.4 System Realization
6.5 Process Description
6.6 Application Examples
References

Chapter 7: Management of Automated Processes

7.1 Laboratory Information Systems
7.2 Laboratory Execution Systems
7.3 Process and Workflow Management Systems
7.4 Business Process Management Systems
References

Index

End User License Agreement

List of Illustrations

Chapter 2: Automation in Life Sciences – A Critical Review

Figure 2.1 Development of throughput, costs, and resources invested in HTS projects of Novartis from 1998 to 2007; dark gray: single compounds per well (entities); light gray: costs per single compound; dotted line: full-time employees (FTEs) working on these HTS projects.
Figure 2.2 Concept of pre-, intra-, and post-sensoric selectivity.
Figure 2.3 General process workflow in bioscreening and analytical measurement.
Figure 2.4 General subprocesses in analytical measurement.

Chapter 3: Automation Concepts for Life Sciences

Figure 3.1 Schematic visualization of a centralized closed automation system. (SP: sample preparation for one application, A: analytical measurement for one application, CE: connection element).
Figure 3.2 Schematic visualization of a centralized open automation system. (SP 1,2,…,X: sample preparation for different applications, A 1,2,…,X: analytical measurement for different applications, CE: connection element).
Figure 3.3 Schematic visualization of a decentralized closed automation system. (SP: sample preparation for one application, A: analytics for one application, CE: connection element).
Figure 3.4 Schematic visualization of a decentralized open automation system. (SP 1,2,…,X: sample preparation for different applications, A 1,2,…,X: analytics for different applications, CE: connection element).
Figure 3.5 Automation concepts in life sciences (SP 1,2,…,X: sample preparation, A 1,2,…,X: analytical measurement, CSI: central system integrator, FR: flexible robot, CE: connection element, IR: integrated robotics).
Figure 3.6 Overview about the degrees of automation.

Chapter 4: Automation Systems with Central System Integrator

Figure 4.1 Schematic visualization of a centralized closed automation system with a central system integrator. (SP: sample preparation, A: analytical measurement, CSI: central system integrator)
Figure 4.2 Thalidomide and its enantiomers – (a) (R)-(+)-thalidomide, (b) (S)-(−)-thalidomide.
Figure 4.3 Principle of the parallel kinetic resolution.
Figure 4.4 Derivatization of l- and d-proline with two different forms of the Marfey's reagent.
Figure 4.5 Process workflow for the determination of chiral amino acids using TOF-MS.
Figure 4.6 Fully automated system for the determination of chiral compounds ((a) front view, (b) top view, 1: liquid handler Biomek 2000, 2: storage system for labware, reagents, and samples, 3: multi-parallel reaction system HPMR 50-96, 4: thermomixer, 5: ORCA laboratory robot (central system integrator), 6: HPLC system with autosampler, 7: ESI-TOF-MS, 8: computer for control and data evaluation, 9: regrip station).
Figure 4.7 Reusable cover frames for microplates (a) CAD design, (b) CAD explosion view, (c) realized cover, and (d) cover after use with HPLC system.
Figure 4.8 Microplate layout with 15 calibration standards (dark gray), one blank (white), and maximum 80 samples (light gray).
Figure 4.9 Process flow chart for the automated determination of chiral amino acids.
Figure 4.10 Example for an automated method in “SAMI Method Editor.”
Figure 4.11 Example for an automated liquid handling method in “BioWorks Method Editor.”
Figure 4.12 Example for an automated pipetting procedure.
Figure 4.13 Time requirements for an automated screening of chiral compounds.
Figure 4.14 Mass spectrum of the derivatives of proline.
Figure 4.15 Repeatability results for the amino acid proline.
Figure 4.16 Schematic visualization of a centralized open automation system with a central system integrator (SP 1,2,…,X: sample preparation, A 1,2,…,X: analytical measurement, CSI: central system integrator).
Figure 4.17 Selected components of dental composite materials, (a) base monomers, (b) co-monomers, and (c) organic matrix.
Figure 4.18 Process workflow for the determination of mercury in wood samples using ICP-MS or ICP-OES.
Figure 4.19 Process workflow for the determination of dental materials using HPLC-MS and GC-MS.
Figure 4.20 Complete automated system for sample preparation of different sample types; (a) front view, (b) top view, 1: liquid handler Biomek 2000 (Beckman Coulter, Krefeld) with housing and exhaust ventilation, 2: central system integrator – 2 ORCA laboratory robots on orthogonal robot rails (Beckman Coulter, Krefeld), 3: storage system for labware, reagents, and samples, 4: thermomixer MKR23 (HLC BioTech, Bovenden), thermomixer Comfort (Eppendorf, Hamburg), antistat (CEM, Kamp-Lintfort), 5: SCARA robot TS60 (Stäubli, Bayreuth) with balance and crimp station, 6: GC-MS system, 7: ICP-MS system with optional coupling to HPLC, 8: single vial liquid handler.
Figure 4.21 Racks in microplate format for the handling of different sample and chemical containers; (A) CAD construction, (B) realized racks with containers; (a, b) rack for two narrow bore bottles (LDPE, vol. 125 ml); (c, d) rack for six microwave digestion vessels CEM Xpress (PFA, vol. 25 ml); and (e, f) rack for 24 sample containers (PP, vol. 14 ml).
Figure 4.22 Racks in microplate format for the handling of different sample and chemical containers; (A) CAD construction, (B) realized racks with containers; (a, b) rack for two beakers (PFA, vol. 100 ml); (c, d) rack for six centrifuge tubes (PP, vol. 50 ml); and (e, f) rack for six centrifuge tubes with cover (PP, vol. 50 ml).
Figure 4.23 CAD construction of single vial liquid handler – 1 and 2: horizontal and vertical linear rails, 3: pneumatic carriage, 4: needle, 5: wash station, 6: ALPs for positioning racks in microplate format, 7: status lights (green: ready, rot: in operation), 8: ORCA laboratory robot (Beckman coulter, Krefeld), 9: linear rail for laboratory robot.
Figure 4.24 Shuttle for the transport of microplates between two automation platforms.
Figure 4.25 Process flow chart for the automated sample preparation for the determination of mercury in wood.
Figure 4.26 Racks in microplate format for handling of GC vials (vol. 2 ml) (a) CAD design and (b) realized rack with vials.
Figure 4.27 Process flow chart for the automated sample preparation for the investigation of dental materials.
Figure 4.28 Automated method for sample preparation of wood samples: “SAMI Method Editor” – method for addition reagents and pre-digestion (24 vessels).
Figure 4.29 Automated method for sample preparation of wood samples: “SAMI Method Editor” – method for dilution (1 : 12.5, v/v) of the digestion solution (24 vessels).
Figure 4.30 Automated method for sample preparation of wood samples: “BioWorks Method Editor” – method for dilution of the digestion solution (24 vessels).
Figure 4.31 Comparison of recovery rates and repeatabilities achieved using the manual standard, the manual miniaturized, and the automated miniaturized methods [64, 229, 231, 239].
Figure 4.32 Schematic visualization of a decentralized closed automation system with a central system integrator (SP: sample preparation, A: analytical measurement, CSI: central system integrator, CE: connection element).
Figure 4.33 Process workflow for the determination of calcium and phosphorus in bones using ICP-MS.
Figure 4.34 Process flow chart for the automated sample preparation and analysis for the determination of calcium and phosphorus in bones (Transport I: transport using the central system integrator, Transport II: transport using the mobile robot).
Figure 4.35 Automated method for sample preparation of bone samples: “SAMI Method Editor” – Method for dilution (1 : 1000, v/v) of the digestion solution (24 vessels on 4 racks).
Figure 4.36 Automated method for sample preparation of bone samples: “BioWorks Method Editor” – Method for dilution of the digestion solution (last dilution step, 24 vessels).
Figure 4.37 Communication model for mobile robotics.
Figure 4.38 Network architecture for communication with mobile robots.
Figure 4.39 Calibration curves for the determination of phosphorus and calcium by ICP-MS (a), mass spectrum of a sample solution from pig bones (b).
Figure 4.40 Comparison of the coefficients of variation of the repeatability achieved with manual and automated methods.
Figure 4.41 Mean values of the recovery rate for manual and automated methods for determination of Ca and P in NIST reference material SRM 1486.
Figure 4.42 Schematic visualization of a decentralized open automation system with a central system integrator (SP 1,2,…,X: sample preparation, A 1,2,…,X: analytical measurement, CSI: central system integrator, CE: connection element).
Figure 4.43 Process flow chart for flexible parallel execution of three applications in a decentralized open automation system.
Figure 4.44 Process flow chart for the automated execution of sample preparation for the analytical determination of mercury in wood using mobile robots for transportation tasks (Transport I: central system integrator, Transport II: mobile robot).
Figure 4.45 Process flow chart for the automated execution of the sample preparation for the analytical investigation of dental materials using mobile robots for transportation tasks (Transport I: central system integrator, Transport II: mobile robot).
Figure 4.46 Architecture of the automated laboratory system (PCS: process control system, ICS: instrument control system).

Chapter 5: Automation Systems with Flexible Robots

Figure 5.1 Schematic visualization of a centralized closed automation system with a flexible robot (SP: sample preparation, A: analytical measurement, FR: flexible robot).
Figure 5.2 Centralized closed automation system with flexible robot – (a) overall view, (b) top view, 1: dual-arm robot SDA10F (Yaskawa, Kitakyūshū), 2: LC-MS system (Agilent Technologies, Waldbronn), 3: GC-MS-MS system (Agilent Technologies, Waldbronn), 4: workbench with chemical resistant coating, 5: light curtain (a: transmitter, b: deflection mirror, c: receiver), 6: storage system with two levels, 7: ultrasonic bath RK31 (Bandelin Electronic, Berlin), 8: thermomixer Comfort (Eppendorf, Hamburg), 9: ALPs for samples, reagents, solvents, and labware, 10: shaker Teleshake (Variomag, Daytona Beach), 11: holder for HPLC auto sampler tray, 12: waste container, 13: holder for pipettes
Figure 5.3 Pipetting with the dual-arm robot SDA10 – (a) manual pipette with adapter, (b) pipettes on the holder of the automation system, (c) pipetting process using both robot arms
Figure 5.4 Gripper with different fingers – (a) finger for grabbing microplates and single vials at left robot arm, (b) finger for grabbing single vials, lids, manual pipettes, and glass pipettes at right robot arm
Figure 5.5 Insertion of samples into the HPLC auto sampler with the dual-arm robot SDA10 – (a) CAD layout of microplate tray with adapter and gripper, (b) realized tray with adapter, microplate, and cover, (c) insertion process
Figure 5.6 Racks in microplate format for the handling of sample vessels with different volumes and glass pipettes – above: CAD design, below: realized racks with containers; (a,b) rack for 12 glass vials with screw-on lid (vol. 4 ml); (c,d) rack for 12 glass vials with screw-on lid (vol. 22 ml); (e,f) rack for 12 glass pipettes with dispensing bulb
Figure 5.7 Measuring system HPLC-TOF-MS (1: solvent storage system, 2: vacuum degasser, 3: binary pump, 4: high-performance auto sampler, 5: column oven, 6: optical diode array detector, 7: time-of-flight mass spectrometer, 8: computer with control software)
Figure 5.8 Process flow chart for the automated analytical determination of chiral amino acids.
Figure 5.9 General structure of a motion frame.
Figure 5.10 Example for an automated method in “SAMI Method Editor” (sample preparation of chiral amino acids).
Figure 5.11 Communication between the process control software and the measuring instrument LC-MS.
Figure 5.12 General structure of the software interface for the integration of the LC-MS system.
Figure 5.13 Schematic visualization of a centralized open automation system with a flexible robot (SP 1,2,…,X: sample preparation, A 1,2,…,X: analytical measurement, FR: flexible robot).
Figure 5.14 Process workflow for the determination of cholesterol in biliary endoprostheses using GC-MS and GC-FID.
Figure 5.15 Labware holder and racks in microplate format for the handling of sample vials and lids – (A) CAD layout, (B) racks with vials and lids; (a) holder for GC vials in the ultrasonic bath, (b) ultrasonic bath with installed holder and GC vials; (c,d) rack for GC vials; (e,f) rack for lids with several diameters.
Figure 5.16 Automated filtration – racks in microplate format for the handling of disposable syringes, cannulas, and filters ((A) CAD layout; (B) realized racks with labware); (a,b) rack for syringes; (c,d) rack for cannulas; (e,f) rack for filters.
Figure 5.17 Operation of laboratory devices by the dual arm robot ((a) turn on/turn off of the shaker using a push button, (b) adjusting the sonication time using a turning knob).
Figure 5.18 Automated filtration process (a: picking of the syringe cannulas, b: aspiration of the samples to be filtered, c: removal of the used cannula, d: picking of the filter).
Figure 5.19 Process flow chart for the automated determination of cholesterol in incrustations of biliary endoprosthesis.
Figure 5.20 Process flow chart for the communication between the interface and the software “SAMI Workstation Ex.”
Figure 5.21 GC-MS calibration for cholesterol between 0.5 and 3 mg/l.
Figure 5.22 EI mass spectra: (a) cholesterol and (b) α-cholestan (internal standard).
Figure 5.23 Schematic visualization of a decentralized closed automation system with a flexible robot (SP: sample preparation, A: analytical measurement, FR: flexible robot, CE: connection element).
Figure 5.24 Schematic visualization of a decentralized open automation system with a flexible robot (SP 1,2,…,X: sample preparation, A 1,2,…,X: analytical measurement, FR: flexible robot, CE: connection element).
Figure 5.25 Process flow chart for the flexible processing of an application (investigation of incrustations of biliary stents) with different measurement methods in a decentralized closed automation system.
Figure 5.26 Process flow chart for the flexible execution of different applications in a decentralized open automation system.
Figure 5.27 Schematic visualization of a decentralized/closed (a) and a decentralized/open automation system (b) using an integrated robot (SP 1,2,…,X: sample preparation, A 1,2,…,X: analytical measurement, CSI: central system integrator, FR: flexible robot, IR: integrated robot).
Figure 5.28 Process flow chart for the determination of cyclophosphamide in cells and cell culture medium.

Chapter 6: Automated Data Evaluation in Life Sciences

Figure 6.1 System concept of automated data evaluation in analytical chemistry (ICP-MS: inductively coupled plasma mass spectrometry, LC-MS: liquid chromatography mass spectrometry, GC-MS: gas chromatography mass spectrometry).
Figure 6.2 Principle of data import from various measurement instruments (ICP-MS: inductively coupled plasma mass spectrometry, ICP-OES: inductively coupled plasma optical emission spectroscopy, GC-MS: gas chromatography mass spectrometry, LC-MS: liquid chromatography mass spectrometry, chiral MS: chiral mass spectrometry).
Figure 6.3 General process workflow of data evaluation using the “Analytical Data Evaluation” software (ADE).
Figure 6.4 Modular structure of the automated data analysis system in analytical chemistry (PMS: process management system, LES: laboratory execution system, PCS: process control system, BPMS: business process management system, IMS: information management system, LIMS: laboratory information management system, ELN: electronic laboratory notebook, SWMS: scientific workflow management system).
Figure 6.5 Web interface of the ADE software for (a) user registration and (b) user login.
Figure 6.6 User interface for integration and adjustment of measurement instrument software: (a) overview of integrated software versions and (b) software details
Figure 6.7 User interface for integration and allocation of measurement devices to software versions and measurements: (a) overview of integrated devices and related software versions and (b) device details.
Figure 6.8 User interface for measurement management: (a) overview about measurements, measurement devices, and related software versions and (b) measurement details.
Figure 6.9 User interface with measurement details: information related to samples.
Figure 6.10 User interface with measurement details: information related to the sample preparation.
Figure 6.11 User interface with measurement details: information related to the naming convention in the sample name.
Figure 6.12 User interface with measurement details: information related to the analytes.
Figure 6.13 User interface for project management: (a) overview of projects and related users and (b) project details.
Figure 6.14 Results page with projects navigation field (left) and the measurement results with the concentration of selected elements.
Figure 6.15 Chart with results of the determination of the measurement precision in the determination of mercury in wood materials.
Figure 6.16 Process workflow for the automated data evaluation using (a) the measurement instruments software, (b) the software module “Data Upload” on the instruments workstation, and (c) the web application “Analytical Data Evaluation” (ADE), (gray fields: executed by software, white fields: manual user interaction). (Redrawn from [35].)
Figure 6.17 User interface of the ADE software with the results of the determination of the repeatability in elemental analysis of wood material (determination of mercury and heavy metals).
Figure 6.18 User interface of the ADE software with the results of the determination of the repeatability in structural analysis of dental materials (determination of methacrylate-based compounds).
Figure 6.19 User interface of the ADE software with results of the determination of the within-laboratory reproducibility in structural analysis of biliary endoprosthesis (determination of cholesterol).

Chapter 7: Management of Automated Processes

Figure 7.1 Simplified architecture of the hierarchical workflow management system.
Figure 7.2 User interface for workflow planning with the process of the sample preparation for the determination of phosphor and calcium in bones.
Figure 7.3 Representative selection of typical combinable subprocesses in life science automation with frequently varying degrees of automation (roughly categorized in five levels) in a BPMN 2.0-controlled integration and automation platform

List of Tables

Chapter 2: Automation in Life Sciences – A Critical Review

Table 2.1 Examples for laboratory devices and their LUO
Table 2.2 Selected technologies and methods in physico-chemical analysis
Table 2.3 Comparison between classical screening and high throughput screening [38]
Table 2.4 Overview about drugs developed using HTS methods [33]
Table 2.5 Microplate formats [36, 37]
Table 2.6 Typical configuration of robot main axes and resulting work space
Table 2.7 Typical optical measuring methods in process analytical technology and their applications [145]
Table 2.8 Examples for sample vessels in analytical measurement
Table 2.9 SIGNIFICANCE – the 12 principles of green analytical chemistry [164]

Chapter 3: Automation Concepts for Life Sciences

Table 3.1 Automation structures according to Lauber and Göhner [1] (C: centralized, D: decentralized, left character: structure of the technical processes, center: local structure of the automation devices, right character: functional automation structure)
Table 3.2 Automation structures adapted to life science processes: (C: centralized, D: decentralized, O: open, Cl: closed, left character: local distribution of the automation devices; right character: flexibility of the automation structure)
Table 3.3 Trends in laboratory automation [24]
Table 3.4 Validation parameter

Chapter 4: Automation Systems with Central System Integrator

Table 4.1 Definitions for the description of the chiral composition
Table 4.2 Processing time of different methods for the determination of chiral compounds [60, 61, 64]
Table 4.3 Basic components of dental composite materials [212]
Table 4.4 Results of the repeatability and recovery rate using microwave vessels with various volumes [231]
Table 4.5 Overview of material savings using microwave vessels with different volumes and the methods presented [231]
Table 4.6 Processing time for determination of mercury in wood with the manual standard procedure, the miniaturized, and the automated methods [228, 229, 231–233, 240]
Table 4.7 Polyatomic interferences for P and Ca in the ICP-MS analysis [267, 268]
Table 4.8 Results of the repeatability rate using microwave vessels with various volumes
Table 4.9 Results of recovery rate using microwave vessels with various volumes and the NIST reference material SRM 1486
Table 4.10 Processing time for determination of Ca and P in bones with the manual standard procedure, the miniaturized, and the automated methods [230, 239, 273]

Chapter 5: Automation Systems with Flexible Robots

Table 5.1 Work range and velocities of the main and hand axes of the SDA10 [2, 3]
Table 5.2 Time requirement for preparation and analysis of 96 samples using manual method, automated method with system integrator, and automated method with flexible robot (no overlapping sample preparation, *samples and reagents are prepared)
Table 5.3 Time requirement for the manual and automated sample preparation with flexible robot (no overlapping sample preparation, *no rinsing steps)
Table 5.4 Work range and velocities of the main and hand axis of the SDA5 [46]

Chapter 6: Automated Data Evaluation in Life Sciences

Table 6.1 Example of a heterogeneous software environment (measurement instruments and related software) in the analytical laboratory of CELISCA (Center for Life Science Automation, Rostock, Germany) [7]
Table 6.2 Types of calculation tasks and the related abbreviations in the sample name tag [35]
Table 6.3 Comparison of processing time required for manual and automated data evaluation [35]

Chapter 7: Management of Automated Processes

Table 7.1 Selection of typical information systems in life science automation [64]

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Preface

Automation systems with applied robotics have already been established in industrial applications for many years. In the field of life sciences, a comparable high level of automation can be found in the areas of bioscreening as well as high-throughput screening. Strong deficits still exist in the development of flexible and universal fully automated systems in the field of analytical measurements. Reasons are the heterogenous processes with complex structures, which include sample preparation and transport, analytical measurements using complex sensor systems as well as suitable data analysis and evaluation. Furthermore, the use of non-standard sample vessels with various shapes and volumes results in an increased complexity. The state of the art includes automated workstations, semi-automated systems, or proprietary fully automated systems, which have been developed for specific applications. In general, a flexible use of automation systems for different applications is a challenging scientific task.

The development of appropriate automation systems in the field of analytical measurements using analytical instruments and complex sensor systems initially requires a systematic analysis of the processes to be automated with the aim to develop suitable structures and allocate them to these processes. In industrial applications, eight different structures can be distinguished according to their centralized or decentralized process structure, local, and functional structure. In analytical measurement technology, there are limitations regarding a general applicability of these structures, thus, an adequate adaption is required. Analytical processes are always characterized by a decentralized process structure. This enables a distinction according to their local and applicative structure. Depending on the robot technology used, two basic automation concepts can be applied to processes in analytical measurements: central system integrators and flexible robots. For a maximum versatility of the processes to be automated an extension to a third concept – integrated robotics – is possible.

Due to their high flexibility, robots can be used as transport systems. This enables a connection of the individual subprocesses and workstations, whereby the robot has the function of a central system integrator. A higher flexibility of an automation system can be achieved when, besides transportation tasks, the robot additionally performs active manipulation tasks, whereby the robot has the function of a flexible robot. A further increase in flexibility can be achieved using mobile robots, which perform both, transportation tasks between various subsystems and manipulation tasks. For an efficient workload of such robots, some of these tasks can be performed even during the transport.

This book will provide a substantial contribution to the development and systematization of appropriate automation systems in the life sciences, in particular, in the field of analytical measurement technique. The first chapter gives a widespread overview about the history and the impact of automation systems in the field of life sciences. The second chapter involves a critical review of existing automation systems in bioscreening, chemical sciences, and analytical measurement applications. The chapter begins with general definitions and basics and concludes with the requirements for automating analytical measurement processes. The third chapter is particularly dedicated to the theoretical view on automation structures and presents general automation concepts for analytical measurement processes. The theoretical considerations are completed with delineations regarding the degree of automation and statistical evaluations. The fourth and fifth chapters present realized automation concepts with a central system integrator and a flexible robot. Therefore, special applications from various areas are introduced. This includes applications in environmental measuring technology, medicine, drug development, and drug discovery as well as quality assurance. The goal is to achieve a high degree of automation with maximum sample throughput, short processing, and measurement times with a special focus on the applicative flexibility of the automated systems. The systems are described in detail and the evaluation is done on both, the process performance and the measurement results achieved. The sixth chapter is related to the software development for automated data evaluation. The challenge was developing a flexible solution, which enables the integration of several analytical measurement instruments from different manufacturers to ensure a fully automated process, including the sample preparation, the measurement, and the final data evaluation. The last chapter is dedicated to the high-level management of automated processes and discusses several management systems used in the field of laboratory automation.

The authors would like to express their personal thanks to Prof. Dr.-Ing. Norbert Stoll for his support and valuable discussions. Our special thanks go to the company Yaskawa, especially Dr.-Ing. Michael Klos and B.Eng. Wolfgang Schuberthan for providing the dual-arm robot SDA10F and for the support in generating the robot jobs. We would like to acknowledge our thanks to the Federal Ministry for Education and Research (BMBF) for partially supporting several projects. For the realization of the automation systems in detail, we thank the members of the following research groups at CELISCA (Center for Life Science Automation) at the University of Rostock: research group “Life Science Automation – Systems” under the guidance of Dr.-Ing. Steffen Junginger, research group “Life Science Automation – Mobile Robotics” under the guidance of Priv.-Doz. Dr.-Ing. habil. Hui Liu, research group “Life Science Automation – Process IT” under the guidance of Dr.-Ing. Sebastian Neubert, and research group “Life Science Automation – Processes” under the guidance of Priv.-Doz. Dr.-Ing. habil. Heidi Fleischer. Finally, we wish to thank all the students for their contributions within the scope of their bachelor and master theses.

We wish all users of this book an interesting and informative read.

December 2016
Rostock, Germany

Heidi Fleischer
Kerstin Thurow

Automation Solutions for Analytical Measurements

Concepts and Applications

Preface