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\begin{document}

\title{Methodology for fast development of digital solutions in integrated
continuous downstream processing}



\author[1]{Bernt Nilsson}%
\author[1]{Niklas Andersson}%
\author[1]{Joaquín Gomis Fons}%
\author[1]{Madelene Isaksson}%
\author[1]{Simon Tallvod}%
\author[1]{Daniel Espinoza}%
\author[1]{Linnea Sjökvist}%
\author[1]{Gusten Zandler Andersson}%
\affil[1]{Lunds Universitet}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
The methodology for production of biologics is going through a paradigm
shift from batch-wise operation to continuous production. Lot of efforts
are focused on integration, intensification and continuous operation for
decreased foot-print, material, equipment and increased productivity and
product quality. These integrated continuous processes with on-line
analytics becomes complex processes, which requires automation,
monitoring and control of the operation, even unmanned or remote, which
means bioprocesses with high level of automation or even autonomous
capabilities. The development of these digital solutions becomes an
important part of the process development and needs to be assessed early
in the development chain. This work discusses a platform that allow fast
development, advanced studies and validation of digital solutions for
integrated continuous downstream processes. It uses an open, flexible
and extendable real-time supervisory controller, called Orbit, developed
in Python. Orbit makes it possible to communicate with a set of
different physical setups and on the same time perform real-time
execution. Integrated continuous processing often imply parallel
operation of several setups and network of Orbit controllers makes it
possible to synchronize complex process system. Data handling, storage
and analysis are important properties for handling heterogeneous and
asynchronous data generated in complex downstream systems. Digital twin
applications, such as advanced model-based and plant-wide monitoring and
control, are exemplified using computational extensions in Orbit,
exploiting data and models. Examples of novel digital solutions in
integrated downstream processes are automatic operation parameter
optimization, Kalman filter monitoring and model-based batch-to-batch
control.%
\end{abstract}%



\sloppy


Methodology for fast development of digital solutions in integrated
continuous downstream processing

Niklas Andersson, Joaqu\selectlanguage{ngerman}ín Gomis Fons, Madelène Isaksson, Simon Tallvod,
Daniel Espinoza, Linnea Sjökvist, Gusten Zandler Andersson and Bernt
Nilsson\textsuperscript{\#}

Dept. of Chemical Engineering, Lund University, Lund, Sweden

\textsuperscript{\#} Corresponding author, email address:
bernt.nilsson@chemeng.lth.se

Methodology for fast development of digital solutions in integrated
continuous downstream processing

Abstract

The methodology for production of biologics is going through a paradigm
shift from batch-wise operation to continuous production. Lot of efforts
are focused on integration, intensification and continuous operation for
decreased foot-print, material, equipment and increased productivity and
product quality. These integrated continuous processes with on-line
analytics becomes complex processes, which requires automation,
monitoring and control of the operation, even unmanned or remote, which
means bioprocesses with high level of automation or even autonomous
capabilities. The development of these digital solutions becomes an
important part of the process development and needs to be assessed early
in the development chain. This work discusses a platform that allow fast
development, advanced studies and validation of digital solutions for
integrated continuous downstream processes. It uses an open, flexible
and extendable real-time supervisory controller, called Orbit, developed
in Python. Orbit makes it possible to communicate with a set of
different physical setups and on the same time perform real-time
execution. Integrated continuous processing often imply parallel
operation of several setups and network of Orbit controllers makes it
possible to synchronize complex process system. Data handling, storage
and analysis are important properties for handling heterogeneous and
asynchronous data generated in complex downstream systems. Digital twin
applications, such as advanced model-based and plant-wide monitoring and
control, are exemplified using computational extensions in Orbit,
exploiting data and models. Examples of novel digital solutions in
integrated downstream processes are automatic operation parameter
optimization, Kalman filter monitoring and model-based batch-to-batch
control.

Introduction

The methodology for production of biologics is going through a paradigm
shift from batch-wise operation to continuous production. A lot of
efforts are focused on integration, intensification and continuous
operation for decreased foot-print, material, equipment and increased
productivity and product quality (Ng et al 2012, Godawat et al 2012,
Baur et al 2016a, 2016b, 2018, Sellberg et al 2017, Gomis-Fons et al
2020B and 2021, Moreno-González et al 2021). On-line analytics are
needed in these continuous processes to reach their potentials with the
final goal of real-time release testing, RTRT (Farid 2007, Shukla and
Thömmes 2010, Godawat et al 2015, Walthers et al 2015, Konstantinov and
Cooney 2015, Zydney 2015, Shukla et al 2017, Kesik-Brodacka 2018, Vogg
et al 2018). These integrated continuous processes with on-line
analytics become complex processes, which requires automation,
monitoring and control of the operation, even unmanned or remote, which
means bioprocesses with high level of automation or even autonomous
capabilities. The development of these digital solutions becomes an
important part of the process development and needs to be accounted for
early in the development chain. Lab-scale automated continuous
bioprocesses have been reported (Steinebach et al 2017, Feidl et al
2020, Gomis-Fons et al 2020B, Rathore et al 2021, Schwartz et al 2022,
Tiwari et al 2022). The software used in these experimental platforms
were very different from locally developed PLC to commercial process
automation SCADA systems. This work discusses a platform that allows
development, advanced studies and validation of digital solutions for
integrated continuous downstream processes. It uses an open flexible and
extendable real-time supervisory controller developed in Python. Python
is a well-known programming language with a large user community that
supports the language with a large set of external packages for
different kind of applications, for instance in data handling, machine
learning, simulation and optimization.

The paper presents the properties of the supervisory controller, called
Orbit, and networks of controllers implemented in Python, and how it
impacts the development of downstream processes (Andersson et al 2017,
2022). Orbit makes it possible to communicate with a set of different
physical setups and on the same time perform real-time execution.
Integrated continuous processing often imply parallel operation of
several setups and network of Orbit controllers makes it possible to
synchronize complex process system. Data handling and data analysis is
an important property for handling heterogeneous and asynchronous data
generated in complex downstream systems. Advanced model-based and
plant-wide monitoring and control are used as examples of novel digital
solutions in integrated downstream processes.

Materials and methods

Integrated continuous downstream system

A process for continuous production of an antibody was developed and
studied in lab-scale, Scheffel et al 2022, and scaled-up and operated in
pilot-scale, Schwartz et al 2022. The upstream process consisted of a
high cell-density perfusion bioreactor with a membrane system for
production of clear cell-free supernatant. Between the upstream process
and the downstream process there was a harvest tank making it possible
to disconnect the downstream process for up to 20 hours. Under normal
conditions, the feed flow rate to the downstream process was set for the
control of the liquid amount in the harvest tank, meaning that the
production rate in the complete process was controlled by the perfusion
rate selected in the upstream process.

The downstream process consisted by four units; continuous periodic
counter-current chromatography system (PCC) for antibody capture, viral
inactivation unit (VI) based on two parallel batch reactors, one cation
exchange chromatography step (CEX) in bind-and-elute mode mainly for
aggregate removal and finally one anion-exchange chromatography step
(AEX) in flow-through mode for final polishing. The PCC and the loading
of VI was configured on one ÄKTA setup and the VI control together with
the IEX polishing train on a second ÄKTA setup. Each setup was
controlled by an installation of the research software Orbit, through
the Unicorn control system for the ÄKTA system. The lab-scale process
was operated continuously for 9 days and the pilot-scale process for 19
days, for details see Scheffel et al 2022 and Schwartz et al 2022.

Support system automation

An ÄKTA explorer 100 was modified into a buffer preparation unit, BPU,
by introducing additional valves and adapting the standard flow path.
Two pumps were needed to perform the buffer preparation and delivery.
The configuration enabled connection of 9 different stock solutions. In
addition, 14 buffer mixing flasks were installed using the column
valves, the outlet valve and the sample valve. In each of the buffer
mixing flasks, a specific buffer could be prepared. Since the same
buffer was prepared in the same mixing flask the whole time, no cleaning
of the flasks was needed. At each of the client systems, the tube from
the BPU was connected to a client inlet valve, which was used to
distribute the buffers to the corresponding buffer flasks in the client.
This setup allowed for the BPU to deliver buffer directly to the right
buffer flask in the client system. The client inlet valve worked
independently of the other tasks that the client system performed,
meaning that the downstream process did not need to stop every time a
buffer in the client was re-filled. For more detail see Isaksson et al
2023.

Quality analysis systems consists of a sample preparation system, a
modified ÄKTA explorer 100, and a HPLC analysis system, Agilent 1260.
The prep and analysis systems were connected via a tube between the prep
system's outlet valve and the analysis system's injection valve. The
client systems were connected to the prep system at several points
allowing for sampling the flow path of for sampling a container with a
pool, before further processing. The retrieved sample is prepared for
analysis, which can mean dilution, buffer exchange, chemical
modification etc., before sending to analysis. The analysis system
consisted of an injection valve with an injection loop, a column valve
with several analysis columns, a diode array detector and a refractive
index detector. When the prep system was sending a sample for analysis,
the flow path on the analysis system went through the injection loop and
directly to waste. Based the kind of sample loaded the corresponding
analysis protocol were performed. For more detail see Tallvod et al
2023.

Automation and operation of continuous downstream system

Orbit was originally created in 2015 and the current version is 3. Orbit
was created to make complex, custom control of processes and to
integrate several instruments together. The fundamental part of the
orbit design is the orbit kernel that consist of one part where the
system configuration and communication is defined and a second part
where the real-time control is defined. A configuration of a system is
designed with unit objects which contains methods to control the object.
There are unit libraries where units for different type of system is
defined. Every unit that communicates with an instrument has a defined
protocol for the available methods. Tubes can also be defined to keep
track of flow paths, dead volumes. The real-time kernel handles the
time, current phases, sampling and communication to the instrument and
between Orbit controllers. The user writes a script to define the phases
of the process that could be run sequential or in parallel. The phases
are defined by the methods in a method library to control the process.
The methods can be commands executed at defined times or more complex
event-based tasks like control. There is an existing library from
different application, such as sequential polishing steps, PCC, MCSGP,
conditioning, etc. A new phase starts when all the instructions in the
previous phase are executed. For automation of integrated continuous
downstream process discussed above two setups were automated using two
Orbit supervisory controllers; one for the PCC operation on an ÄKTA pure
and one for the sequential control of the polishing ion exchange train
also on an ÄKTA pure.

Functionality of the orbit kernel can easily be extended. There are
built-in extensions like monitoring, simulation and plotting and a user
can easily create their own extensions to add for example control,
optimization or predictions based on Kalman filters, see below.

In a process with several orbits there need to be communication between
Orbit controllers. Every Orbit instance has a server enabled where it
can be connected to by other orbit instances or for manual control. For
larger processes this creates a network of Orbit controllers. The
protocol for the communication is to send dictionaries with specified
tasks and arguments to complete the task. One type of Orbit instances is
called orbitX, where a service is offered for other Orbit controllers
that can connect as clients. Examples of this is the quality analysis
system and buffer management system discussed above. When for example
ordering a buffer a task is sent with information about the recipe and
the volume of the ordered buffer. In orbitX instances that handles
requests from several clients there need to be a queue controller
defined. The simplest version of a queue is to have the
first-in-first-out principle. This can also be extended with a priority.
For time critical requests where a buffer or analysis needs to be
performed within a deadline, this can also be added and considered that
the priority is high enough it is allowed to jump the queue.

When working with real-time processes a lot of data is created. A
database has been implemented to handles Orbit data. The data to be
stored is both the system configuration, but also run logs, signal
information and data. This is automatically uploaded directly to the
database during a run. In a process there typically exist sampling
points from where a sample is taken for further analysis. In our QAS
system this analysis is ordered and information about where the sample
is taken is stored together with the result of the analysis.

Modelling and model-based monitoring and control

The Orbit supervisory controller requires a digital representation of
the physical setup, with it is communicating. The representation is
composed of a list of objects and a list of tubing, connecting the
object ports. All objects and tubing have a mathematical representation
in Orbit, allowing a simulation extension to perform a simulation of the
complete setup models, i.e. automatic generation of a digital twin of
the chromatographic system. A set of experiment can be executed to
generate data for parameter estimation and model calibration, see
Tallvod et al 2022A, based on methodologies presented in Borg et al 2014
and Saleh et al 2020. One example of real-time usage of a model is in
on-line monitoring. A dynamic extended Kalman filter is implemented as a
computational extension in Orbit for monitoring of the composition
inside the column. A size exclusion application for the separation of
two components with large size difference is presented for illustration.
Both components can be measured using UV. A sample is introduced at 70s
and passes through the column after 300s, with peak max at about 140s
and 210s. The size exclusion model is calibrated using residence time
experiments and the model were discretized using 55 mesh points. This
means that the Kalman filter estimates 110 states, inside the column.

A batch-to-batch controller was implemented for the retention peak
control of two components using the elution gradient, see for more
details in Espinoza et al 2022B. Two proteins are separated with an
ion-exchange chromatography column by a salt gradient, with a start
value and an end value. These two values are the output from the
controller. After the batch-wise separation, the retention time for the
peak maximum is detected, which is the input to the controller. Based on
the difference between the measured and desired retention time the
controller computes the gradient profile. The controller parameters are
found by using a process gain matrix, derived from the system model.

Results and discussion

Operation of integrated downstream processes

In a previous work a continuous antibody production process was operated
continuously for 9 days in lab-scale and in pilot-scale for 19 days, for
details see Scheffel et al 2022 and Schwartz et al 2022. The perfusion
rate targeted 1.5 reactor volume per day which means that the upstream
process together with the harvest tank had an approximated residence
time of about 24 hours. The downstream process had a residence time of
about 5 hours, with a column switching in the PCC every 40 min, a
product pool passed the polishing ion exchanger train every second
hours. The experimental data from the two different runs in lab-scale
and pilot-scale, clearly indicated that the process dynamics in the
process was slow, dominated by the time-scale in the upstream process.
The time scale of changes in upstream outflow was about 24 hours. The
downstream process consisting of four steps was configured on two ÄKTA
setups for parallel operation of PCC, viral inactivation and polishing,
see Figure 1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\emph{Figure 1: Process overview of an integrated continuous antibody
production with perfusion bioreactor system, continuous capture system
and a system for polishing, inspired by Schwartz et al 2022.}

The integrated continuous process platform was well suited to be a
testbed for studies of novel processing step, new analytical tools, data
management, automation, real-time monitoring and control, and other
digital solutions. Based on experiences a general platform, see
Andersson et al 2022, for development studies and validation of digital
solutions needs following features:

\begin{itemize}
\tightlist
\item
  Local supervisory controller for handling general setups with an open
  and flexible architecture for introduction of novel automation,
  feedback control and computational tools
\item
  Network of supervisory controller with a general communication
  protocol for the configuration and operation of complex processing
  systems
\item
  Automated support systems to run the continuous processing system
  almost autonomous, such as buffer management and quality analysis
\item
  Common real-time database together with general tools for data
  handling and analysis that can handle heterogeneous, asynchronous and
  distributed data
\item
  Embedded tools that can handle model based simulation, monitoring and
  control for digital twin applications
\end{itemize}

A lab-scale platform that contained all these features made it possible
to implement and validate novel digital solutions with minimal
resources. The lab-scale size reduced the amount of material needed to
do long-time runs. Automation of the processing together with the
support system means that operation can be unmanned or remote. The
previous work was only manned during office hours, unmanned during
nights and over weekends. This resulted in that efforts could be focused
on implementing digital solutions at different levels of the complex
system, some will be discussed below.

Local supervisory controller

The instruments are controlled with a local supervisory controller. An
overview of the architecture can be seen in Figure 2, where it's divided
into an orbit kernel, user code and application library. The user
configures the system and designs a script defining the sequence of
phases that should be executed, where each phase consists of the methods
from the application library. The application library often shares many
methods but is unique for every specific setup. For each local orbit a
system configuration is defined and a method library for the current
setup. The controller also handles tasks such as sampling, database
communication and network communication with other orbits.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\emph{Figure 2: Orbit kernel architecture with the two layers; i)
communication and ii) real-time execution, together with the application
specification of its configuration and automation.}

Network of supervisory controllers

When processes consist of several instruments communication of the
instruments are vital. Orbit has support for communication between each
other. An orbit can connect to another orbit as a client or act as a
server to receive connections from other orbits. For a network of Orbit
running instruments it's important to be able to communicate when a pool
needs to be transferred. In that case there need to be a synchronization
when both of the systems are ready. A system in a chain needs to
synchronize with both the machine before and the machine after. The
cycle of an instrument can therefore be generalized to be ``ready to
receive'', ``receiving'', ``processing'', ``ready to deliver'' and
``delivering''. When an instrument is ``ready to receive'' and the
previous machine is ready to deliver the delivering/receiving phase can
start.

In the BMS and QAS OrbitX controllers, the OrbitX works as a service
where other Orbits can connect and make an order. In that case the Orbit
connects as a client and makes an order. A necessity is of course that
there is a physical connection. Figure 3 illustrates the process where
the integrated downstream process is communicating with each other. They
are also connected to the support systems BMS and QAS for ordering of
fresh buffer or analysis of the product. All communication happens in a
local network. Each Orbit controller is it's own module and with this
framework it's easy to extend the system with another process step or to
add another service. All of the orbit instances are also connected to
the database where all data are streamed to. The modularized approach
creates a flexible, almost plug-and-play, architecture. Modules, a
physical setup with a local Orbit supervisory controller, can be
exchanged in the structure with minimal effort as long as the plant-wide
synchronization protocol is kept constant.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\emph{Figure 3: Decentralized control based on a network of Orbit
controllers of an autonomous integrated downstream process with support
systems.}

Automated support systems

Automated operation of integrated continuous downstream process for a
long time means a lot of support work, like preparing buffers and sample
flow path for quality analysis and performing the actual analysis. This
will actually generate more work than operate the processing system. For
this reason, it is vital to automate these procedures in order to create
a sustainable long-term lab-scale operation, which are needed for
validation of digital solutions. Automated buffer management system and
quality analysis system are briefly presented below.

Automated continuous downstream processes operate 24-hours, 7 days a
week in a couple of months. During this time, it consumes a lot of
different buffers even in lab-scale. After the automation of the actual
processing line, it is important to automate the buffer preparation
after demand of the process. One such system was implemented and studied
in the lab-scale process and it is called buffer management system
(BMS), see Isaksson et al 2023. The BMS is composed of four parts; i)
monitoring of the buffer container levels at all clients to detect when
they pass below an order level, ii) queue system for orders of needed
buffers, iii) buffer preparation unit, BPU, and iv) delivery procedure
from BPU to the ordering client.

Based on the digital application configuration of the physical system,
Orbit keep track of the active flow path and therefore the size of the
flow rate generated by the pumps. Orbit also automatically generate
estimators of the levels of all containers in the flow path, like buffer
flasks, waste containers and viral inactivation reactors. These
estimators integrate the inflow and outflow in open loop. The level in
the containers need to be updated based of measurements of the real
container regularly, in order not to deviate too much. For the accuracy
needed for predicting the buffer flask level this is not an important
issue, but for more important processing units it can be. Associated to
all container level estimators it is possible to add logic to generate
events. One example is to trigger an order of more buffer when the
estimated level passed below a given value.

The buffer order generated by the level estimator is sent to the buffer
ordering queue system, an orbitX instance in Figure 3. The queue is a
first-in-first-out (FIFO), but it is possible to rank the buffers in an
importance order. The queue controller adds a new order into the queue
and removes an order that is delivered. When the buffer preparation unit
is ready it gets the first order in the queue from the queue controller.
When the unit has delivered the buffer it communicates to the queue
controller that the order has been performed, and the queue controller
removes the order from the queue. An 11-days long operation of a
lab-scale antibody platform is seen in Figure 4, where 55 order was
handled by the buffer order queue controller.

BPU is an ordinary setup with a supervisory Orbit controller that on
demand perform a procedure to prepare a specific buffer, following a
predefined recipe, based on a set of stock solutions. The BPU used in
this study can prepare up to 14 different buffers. The stock solutions
are mixed in an intermediate buffer flask. The mixing can be operated in
closed loop controlling pH and conductivity, but this feature had not
been performed in this study. When the buffer is mixed, it is pumped to
the client that ordered the buffer. The receiving client directs the
buffer delivery valve to the right buffer flask. This required a
communication between the buffer preparation unit and the ordering
client over the network, see Figure 3. Each sample point in the
downstream process has a collection loop on the preparation system.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\emph{Figure 4: Performance of the buffer management system during 11
days of operation of a continuous antibody production.}

The requirement on quality analysis can be handled almost in the same
way as the buffer management system. As discussed above the continuous
downstream process could assume the conditions in the harvest tank to be
almost constant or changing very slowly. A set of performance and
quality attributes were measured through chemical analysis of samples
taken from different positions in the process flow path once a day. The
sampling of the flow path were performed by the automation system but
the chemical analyses were performed off-line and the actual attribute
were calculated after the process were completed. The quality analyses
were not used for monitoring of the operation, which would had been
beneficial.

In lab-scale continuous downstream process there is no need to sample
the flow path more often than a couple of times per day for any upstream
based disturbance, based on the slow upstream dynamics compared to
downstream. Assume that an HPLC analysis of a quality attribute takes 30
min and it is performed twice a day, then the HPLC is idle 23 hours
every day. A general analysis system like HPLC can be configured to do a
set of different analysis. If an analysis takes, in average, 30 min to
perform it means that an on-line HPLC can handle almost 50 analysis per
day. In lab-scale development resources are limited and an on-line HPLC
needs to handle all on-line analysis in the processes. One such system
was implemented and studied in Tallvod et al 2023 and 2022B. It is
called quality analysis system, QAS. The physical configuration of the
QAS was composed of two setups, one for sample handling and preparation,
and a second dedicated analysis system, here a HPLC. The preparation
setup handled the sample from the process and performed needed
preparation, like dilution, conditioning buffer exchange and chemical
modifications. The second setup, the HPLC, were configured to perform a
set of different analysis. The automation part of the QAS is composed of
three parts; i) sampling of the processing flow path, ii) queue system
for orders of needed analyses iii) prepare the sample for analysis, and
iv) preform the HPLC analysis and send data to database, see Figure 5.

The client application procedure does the necessary operations to do the
actual sampling of the processing flow path. This sampling operation can
be performed on demand based on an external order or by the client
itself. The sampling can be a small amount directed to a sample loop or
a complete pool shortly stored in a container, which is then pumped into
a sample loop, before further processing in the process. The size of the
sample depends on the analysis and the number of differ of analysis
needed. Each sample point in the downstream process has a collection
loop on the preparation system. When the sampling of the process flow
path is completed, an order for analysis is sent to the QAS queue
controller.

The queue in the queue controller, in an orbitX controller, is a
first-in-first-out, but it is possible to rank the analysis in an
importance order. Particularly if a time-critical-analysis is need for
on-line control, which means that the analysis needs to be performed to
generate a result in a given timeframe. The queue controller adds new
orders into the queue and removes orders that are completed.

When the preparation/analysis unit is ready, it gets the first order in
the queue from the controller. When the unit has performed the analysis
it communicates that the analysis has been done and the queue controller
removes the order from the queue. Note that the order can contain
multiple analysis for different attributes. The generated data from
preparation and analysis is sent to the Orbit database for further data
processing. In some situations, it is convenient to do the data analysis
locally, which is straightforward to do as a computation extension in
the local Orbit controller.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\emph{Figure 5: Queue based control of the quality analysis systems.}

Orbit real-time database and data analysis

The processing platform with support systems discussed above generate a
lot of data in form of structural information, input and control
signals, detector signals, HPLC analysis, on-line software computations
etc. The on-line generated data is heterogeneous, i.e. different types,
asynchronous, i.e. in-line detectors vs advanced quality analysis, and
distributed, i.e. generated on multiple physical detectors and different
setups. A database was needed to handle real time process data and made
it practical to handle for both local and plant-wide data analysis.

General tools for automatic data analysis of real-time data, on-demand
at-line or off-line analysis, are important for process monitoring and
process control. One example is the peak analysis of the PCC operation
in the pilot-scale case study in Schwartz et al 2022. In Figure 6 the
first two switches in the PCC are shown together with the automatic peak
finder with associated peak measurement. The peak heights are plotted on
right where peak B is the interesting product peak. As seen the three
different PCC column do not behave exactly the same. They have slightly
different peak heights, about 3\% difference.

This kind of data analysis is based on the data stored in the database,
which come from different sources in the process. In this case it was
actually only from one detector on one setup, but in the general case it
allows plant-wide data analysis using distributed data sources. Data
analysis processing can be performed locally at the setup with the
detector or at the setup needing the analysis results. An alternative
suggested here is a plant-wide computational extension, in an orbitX
controller, exploiting data in the database to perform more or less
advanced data analysis for usage somewhere in the processing system or
just for plant-wide monitoring. More advanced data analysis for
data-driven modelling and machine learning are often performed off-line,
but in some cases the results need to be validated in real-time. The
architecture suggested here allow both off-line development and on-line
validation.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\emph{Figure 6: General peak data analysis used to monitor the PCC
performance, left) the first two switches of the PCC together with
automatic peak finding, right) PCC peak heights after about 40 hour
operation.}

Model based simulation, monitoring and control

The concept of digital twins in real-time applications means to use
digital representations and mathematical models for monitoring and
control of the processing system. The architecture of the proposed
system with Orbit supervisory controllers and network of Orbit
controllers allows the user to implement, study, design and validate
these real-time applications of digital solutions.

The generation and validation of mathematical models is as important as
it is time consuming, requiring a lot of engineering resources, see
Nilsson and Andersson 2017. An attractive idea is to automatically
generate the process model and to have automatic methods to calibrate
and validate the model to experimental data. One such idea was studied
and presented in Tallvod et al (2022A). In Orbit, every object in the
digital configuration of the physical system has a mathematical
representation. It means that a complete model of the setup is
automatically generated based on the described configuration. Many of
the objects can be described quite well, because the behavior is
dominated by measurable physical attributes, like volume, flow rate,
concentrations etc. Other objects need to be calibrated to experimental
data and therefore needs a design of experiments to generate high
quality data for parameter estimation. Off-line experiment plans are
straightforward to perform together with computational extensions for
parameter estimations of different models, see Tallvod et al (2022A).
During continuous operation it is much harder to do experiments for
parameter estimations and model calibration, but limited disturbances
are often allowed if the control system can handle the disturbances.

One example of real-time usage of a mathematical model is in Kalman
filter based monitoring. Extended Kalman filter tools are implemented as
computational extensions in Orbit, with the possibility to use the build
in automatic model generation. A simplified example is illustrated in
Figure 7, which is a size exclusion chromatographic separation of two
components with vary large difference in size. This case is actually
linear, which simplifies the Kalman filter design in the case, but it is
still a dynamic filter with covariance update algorithm. In Figure 8,
column concentration profiles are estimated using the Kalman filter at
four different time instances. The Kalman filter predicts the profile
based on the model and corrects their value using the measurement
signal. The example illustrates the power of these kinds of real-time
algorithms for soft sensing, parameter estimation and on-line
prediction.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\emph{Figure 7: Size exclusion chromatography of two components with
dynamic Kalman filter prediction of column profile using Orbit.
Concentration profiles in the column together with outlet UV signal
indicated at 100s, 140s, 170s and 210s.}

As discussed above the integrated continuous downstream process can
assume slow varying disturbances from the upstream process, like titer
and other quality attributes. Other disturbances are buffer quality,
which can change more drastically. For integrated downstream process
with slow varying upstream disturbance it can be beneficial to control
the separation using a batch-to-batch controller. The idea is to use
previous batch behavior to compensate in the next batch. A
batch-to-batch controller for retention time control using a salt
gradient are presented in Espinoza et al (2022A) and (2022B), and an
adaptive batch-to-batch controller for capture loading is presented in
L\selectlanguage{ngerman}öfgren et al (2021).

Note that the controller needs good information about the real system
and this information can be retrieved in two different ways. The first
way is by an automatic modelling with calibration as discussed above to
generate a simplified model for controller tuning. The other way,
presented in Espinoza et al (2022B) is by performing a set of on-line
experiments for on-line controller tuning.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\emph{Figure 8: Batch-to-batch control of the peak retention time by the
gradient elution of a two component separation by cation exchange
chromatography, feed forward of desired set point, multivariable
feedback controller with decoupling. A pH disturbance in the elution
buffer is introduced in cycle 3, causing the controller to adjust the
gradient to restore the retention times.}

Conclusions

This paper presents a methodology, based on a lab-scale platform, for
fast development of digital solutions in integrated continuous
downstream processes. The fundamental building block is the Orbit
supervisory controller that can control and communicate physical setups
and their associated local control software. Orbit is an open, flexible
and extendable software, which opens up the operation of the setup to
any computational tool. Many different kind of processing steps and
configurations has been implemented and operated using the methodology.
These setups can be connected and configured into complex processing
systems and a network of Orbit controllers makes it possible to operate
almost autonomous. Both the configuration and automation of multiple
processing steps and support systems are vital for sustainable and
autonomous operation for long-term studies of digital solutions.

During processing of the integrated continuous downstream process
platform data are streamed to the Orbit database. The on-line generated
data is heterogeneous, asynchronous and distributed. The data are design
and configuration information, real-time detector signals, asynchronous
chemical analysis data, real-time data analysis information and on-line
computational results. The Orbit software contains methods for automatic
generation of mechanistic models and model calibration. This makes it
possible to perform advanced data analysis, data-driven or mechanistic
modelling, optimization and machine learning. The results can directly
be implemented into the Orbit automation and control system for on-line
processing. The conclusion is that the physical and digital platform are
well suited for development, study and validation of real-time digital
twin application in downstream processing.

\subsection*{CRediT authorship contribution
statement}

{\label{credit-authorship-contribution-statement}}

\textbf{Niklas Andersson} : Conceptualization, Methodology, Software,
Writing (review \& editing), Supervision, Visualization, Project
administration.

\selectlanguage{ngerman}\textbf{Joaquín Gomis-Fons} : Conceptualization, Methodology, Software,
Validation, Visualization.

\textbf{Madèlene Isaksson:} Conceptualization, Methodology, Software,
Validation, Visualization.

\textbf{Simon Tallvod} : Conceptualization, Methodology, Software,
Validation, Visualization.

\textbf{Daniel Espinoza} : Conceptualization, Methodology, Software,
Validation, Visualization

\textbf{Linnea Sjökvist} : Methodology, Software, Validation,
Visualization

\textbf{Gusten Zandler Andersson} : Methodology, Software, Validation,
Visualization

\textbf{Bernt Nilsson} : Conceptualization, Methodology, Resources,
Writing (review \& editing), Supervision, Visualization, Project
administration, Funding acquisition.

Acknowledgement

This project has received funding Vinnova, (through the Competence
Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO)
under Grant 2016\selectlanguage{english}-05181, Grant 2019\selectlanguage{english}-05314 (AutoPilot project), Grant
2022-01477 (AutoADD project) and finally from the European Union's
Horizon 2020 research and innovation program under the Marie
Sk\selectlanguage{polish}ł\selectlanguage{english}odowska-Curie grant agreement No 812909.

References

Andersson, N., L\selectlanguage{ngerman}öfgren, A, Olofsson, M, Sellberg, A., and Nilsson, B.
(2017): ''Design and Control of Integrated Chromatography Columns
Sequences'', \emph{Biotechnol. Prog.} , 33:923-930

Andersson, N., Gomis-Fons, J., Nilsson, B. (2022): ''Smart platform for
development of small-scale integrated continuous downstream
processes'',\emph{AChE,} doi:10.1016/bs.ache.2022.03.004

Baur D, Angarita M, Müller\selectlanguage{english}-Spath T, Steinebach F, Morbidelli M.
Comparison of batch and continuous multi\selectlanguage{english}-column protein A capture
processes by optimal design. \emph{Biotechnology Journal} .
2016a;11(7):920-931.

Baur D, Angarita M, Muller-Spath T, Morbidelli M. Optimal model-based
design of the twin-column CaptureSMB process improves capacity
utilization and productivity in protein A affinity
capture.\emph{Biotechnol J} . Jan 2016b;11(1):135-45.

Baur D, Angelo JM, Chollangi S, Xu, X., Muller\selectlanguage{english}-Spath T, Zhang, N.,
Ghose, S., Li, Z., Morbidelli M. Model assisted comparison of Protein A
resins and multi-column chromatography for capture
processes.\emph{Journal of Biotechnology} . 2018/11/10/ 2018;285:64-73.

Borg N, Brodsky Y, Moscariello J, Vunnum, S., Vedantham, G., Westerberg,
K., Nilsson, B. Modeling and robust pooling design of a preparative
cation-exchange chromatography step for purification of monoclonal
antibody monomer from aggregates. \emph{Journal of Chromatography A} .
2014/09/12/ 2014;1359:170-181.

Espinoza, D., Andersson, N., Nilsson, B. (2022A): ''In-silico
formulation of iterative learning control for chromatographic
purification od biopharmaceuticals'', In \emph{Proceedings of ESCAPE32}
2022, Toulouse, France

Espinoza, D., Andersson, N., Nilsson, B. (2022B): ''Binary separation
control in preparative gradient chromatography using iterative learning
control'', \emph{J Chrom A} 1673, 463078

Farid SS. Process economics of industrial monoclonal antibody
manufacture. \emph{J Chrom B} . Mar. 2007;848(1):8-18.

Feidl F, Vogg S, Wolf M, Podobnik, M., Ruggeri, C., Ulmer, N., Walchli,
R., Souquet, J., Broly, H., Butte, A., Morbidelli, M. Process-wide
control and automation of an integrated continuous manufacturing
platform for antibodies. \emph{Biotechnology and Bioengineering} .
2020;117(5):1367-1380.

Godawat R, Brower K, Jain S, Konstantinov K, Riske F, Warikoo V.
Periodic counter-current chromatography -- design and operational
considerations for integrated and continuous purification of
proteins.\emph{Biotechnol J} . Dec 2012;7(12):1496-508.

Godawat R, Konstantinov K, Rohani M, Warikoo V. End-to-end integrated
fully continuous production of recombinant monoclonal antibodies.\emph{J
Biotechnol} . Nov. 2015;213(Supplement C):13-19.

Gomis-Fons, J., Andersson, N. and Nilsson, B., (2020A) ``Optimization
study on periodic counter-current chromatography integrated in a
monoclonal antibody downstream process''. \emph{J Chrom A} ., 1621
(2020) 461055

Gomis-Fons, J., Schwarz, H., Zhang, L., Andersson, N., Nilsson, B.,
Castan, A., Solbrand, A., Stevenson, J., Chotteau, V. (2020B)
``Model-based design and control of a small-scale integrated continuous
end-to-end mAb platform''. \emph{Biotechnol. Prog} ., DOI:
10.1002/btpr.2995

Gomis-Fons, J., Yamanee-Nolin, M., Andersson, N. and Nilsson, B., (2021)
``Optimal loading flow rate trajectory in monoclonal antibody capture
chromatography''. J Chrom A., 1635 (2020) 461760

Isaksson, M., Gomis Fons, J., Andersson, N., Nilsson, B., (2023): ``An
automated buffer management system for small-scale continuous downstream
bioprocessing'', \emph{J Chrom A} , revision

Kesik-Brodacka M. Progress in biopharmaceutical
development.\emph{Biotechnol Appl Biochem} . 2018;65(3):306-322.
doi:10.1002/bab.1617

Konstantinov KB, Cooney CL. White Paper on Continuous Bioprocessing May
20--21 2014 Continuous Manufacturing Symposium. \emph{J Pharm Sci} .
Mar. 2015;104(3):813-820.

Lofgren, A., Gomis-Fons, J., Andersson, N. Nilsson, B., Berghard, L.,
and Lagerqvist Hagglund, C. (2021) ``An integrated continuous downstream
process with real-time control: A case study with periodic
counter-current chromatography and continuous virus
inactivation''.\emph{Biotechnol Bioeng} ., doi: 10.1002/bit.27681

Moreno-Gonzalez, M., Keulen, D., Gomis-Fons, J., Gomez, G. L., Nilsson,
B., and Ottens, M., (2021) ``Continuous adsorption in food industry: The
recovery of sinapic acid from rapeseed meal extract''. \emph{J
SepPur.,}254 (2021) 117403

Ng CKS, Osuna-Sanchez H, Valery E, Sorensen E, Bracewell DG. Design of
high productivity antibody capture by protein A chromatography using an
integrated experimental and modeling approach. \emph{Journal of
Chromatography B} . 2012/06/15/ 2012;899(Supplement C):116-126.

Nilsson, B., and Andersson, N. (2017): Simulation of Process
Chromatography, In \emph{Preparative Chromatography for Separation of
Proteins} , First Ed., Editors Staby, Rathore and Ahjua, Wiley, Chap 3,
pp 81-110.

Rathore AS, Nikita S, Thakur G, Deore N. Challenges in process control
for continuous processing for production of monoclonal antibody
products. \emph{Current Opinion in Chemical Engineering} . 2021/03/01/
2021;31:100671.

Saleh D, Wang G, Muller B, Rischawy, F., Kluters, S., Studts, J.,
Hubbuch, J.. Straightforward method for calibration of mechanistic
cation exchange chromatography models for industrial
applications.\emph{Biotechnology Progress} . 2020;36(4):e2984.

Scheffel,J., Isaksson, M., Gomis-Fons, J., Schwarz, H., Andersson, N.,
Noren, B., Solbrand, A., Hober, S., Nilsson, B., Chottaeu, V., (2022):
''Design of an integrated continuous downstream process for
acid-sensitive monoclonal antibodies based on a calcium-dependent
Protein A ligand''. \emph{J Chrom A} 1664

Schwarz, H., Gomis-Fons, J., Isaksson, M., Scheffel, J., Andersson, N.,
Andersson, A., Castan, A., Solbrand, A., Chottaeu, V., Hober, S.,
Nilsson, B. (2022): ''Integrated continuous biomanufacturing at
pilot-scale for acid-sensitive monoclonal antibodies''. \emph{Biotechnol
Bioeng} , DOI: 10.1002/bit.28120

Sellberg, A., Holmqvist, A, Magnusson, F, Andersson, C, and B. Nilsson
(2017): ''Discretized multi-level trajectory: A proof-of-concept
demonstration'', J. of Chrom A, 1481, 73-81

Shukla AA, Thommes J. Recent advances in large-scale production of
monoclonal antibodies and related proteins. \emph{Trends in
Biotechnology} . 2010/05/01/ 2010;28(5):253-261.

Steinebach F, Ulmer N, Wolf M, Decker, L., Schneider, V., Walchi, R.,
Karst, D., Souquet, J., Morbidelli, M. Design and operation of a
continuous integrated monoclonal antibody production
process.\emph{Biotechnology Progress} . 2017;33(5):1303-1313.

Tallvod, S., Espinoza, D., Gomis-Fons, J., Andersson, N., Nilsson, B.
(2023): \emph{``Automated quality analysis in continuous downstream
processes for small-scale applications''} , report, Dept of Chemical
Engineering, Lund University

Tallvod, S., Andersson, N., Nilsson, B. (2022A): ''Automation of
Modeling and Calibration of Integrated Preparative Protein
Chromatography Systems'', \emph{Processes} 10, 945

Tallvod, S., Yamanee-Nolin, M., Gomis-Fons, J., Andersson, N., Nilsson,
B. (2022B): ''A novel process design for automated quality analysis in
an integrated biopharmaceutical platform'', In \emph{Proceedings of
ESCAPE32} 2022, Toulouse, France

Tiwari, A., Masampally, V.S., Agarwal, A., Ratore, A. (2022) Digital
twin of continuous chromatography process for mAb purification: Design
and model-based control. \emph{Biotechnology and Bioengineering} . 2022;
DOI: 10.1002/bit.28307.

Vogg S, Muller-Spath T, Morbidelli M. Current status and future
challenges in continuous biochromatography. \emph{Current Opinion in
Chemical Engineering} . 2018/12/01/ 2018;22:138-144.

Walther J, Godawat R, Hwang C, Abe Y, Sinclair A, Konstantinov K. The
business impact of an integrated continuous biomanufacturing platform
for recombinant protein production. \emph{J Biotechnol} . Nov.
2015;213:3-12.

Zydney AL. Perspectives on integrated continuous
bioprocessing---opportunities and challenges. \emph{Current Opinion in
Chemical Engineering} . 2015/11/01/ 2015;10(Supplement C):8-13.

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