Digital_Twins_v1.0.pdf

AUTHOR @ WORK
Over 24 years of experience in IT Industry
• Doing value innovations through development of value products in ICT & allied sector.
• Building Network Security Products and Private Clouds for enterprises & government clientele
• Creating Growth Strategies and Driving Growth
• A finalist nominee for UTV Bloomberg – India CTO/CIO of the year award 2011
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• Workforce of the Future
• Megatrends
• Digital Twins
Shivajee Rao Sharma
[Chief Technology Officer]
RPS Consulting Pvt. Ltd.
linkedin.com/in/shivajeesharma/
Author: Shivajee.R.Sharma – CTO RPS Consulting www.rpsconsulting.in

AUTHOR @ LEISURE (INVENTORS INDIA RESEARCH FOUNDATION)
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Shivajee Rao Sharma
[Chief Technology Officer]
RPS Consulting Pvt. Ltd.
linkedin.com/in/shivajeesharma/
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Introduction to Digital Twins
Application Areas
Digital Twin Architecture
Case Studies of Digital Twin Application in the Industry
AGENDA: THE DEVIL IS IN THE DETAILS!!!

DIGITAL TWINS
INTRODUCTION

FORCES SHAPING OUR WORLD
Society
Technology
Environment Economy
Politics
Technological Breakthrough
Demographics Shifts
Rapid Urbanization
Shifts in global economic power
Resource scarcity and environmental change

EVOLVING WORLDS - KEY DRIVERS SHAPING OUR WORLD

EVOLVING WORLDS - BUSINESS PRESSURES
Operational Efficiencies
• Adaptive and to evolve over time
• Directions for innovation and execution
• Specific to on-time delivery
Customer Relationships
• Business strategies and implementation
• Improve customer service with faster
resolution
• Customer satisfaction
Growth Potential
• Market focused technologies
• Boost production and increase efficiency
• Measurable results
Cost Competitiveness
• Competitive measures
• Sustain cost reduction

DIGITAL TWINS - INDUSTRIAL REVOLUTION

DIGITAL TWINS - INDUSTRY 4.0
Higher
operational
efficiencies
New connected
ecosystems
coalescing
around software
platforms
Greater
collaboration
between
humans and
machines
Pronounced
shift from
selling products
and/or services
to selling
measurable
outcomes

DIGITAL TWINS – WHAT IS IT ?
A digital twin is a digital representation of a physical object, process or service.
“The digital twin is
virtual and connected
replica of a process or
product, capturing real-
time data, the digital
twin understands
current state, simulates
future state and
optimizes performance.”
It enables in-silico testing,
improved control, problem
detection and asset
management.

A virtual and connected model of the essential production process is created in an incremental
approach for quick time-to-value.

This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems
to head off problems before they even occur, prevent downtime, develop new opportunities and
even plan for the future by using simulations.

Create a continuously evolving predictive model to optimize product and production
Continuous calibration and refining
Continuous improvements based on validated predictive performance data

Digital replica of
physical things
can significantly
improve the
following
processes:
Design Simulation Planning
Building
Operating
Maintaining
Optimizing Disposal

Digital replica of
physical things
can significantly
improve the
following
processes:

• Specs
• Drawings
• Documents
• BIM models
• Analyses
• Geotech
• OEM Specs
Engineering
• IoT feeds
• Sensors
• Drones
• Cameras
• LiDAR
• Point louds
Operations
• Asset Tags
• Work orders
• Maintenance
records
• Inspection
records
Information
3D/XR
Immersive
Visualization
4D
Timeline of
Change
AI/ML
Analytics
Visibility
Physical Assets
>
>
>

BUILDING TESTING EXPLORING ASSESSING
DIGITAL TWINS – MBSE
MBSE - Model Based Systems Engineering
Digital + Artificial Intelligence

Design
• Virtually create a solution
• Render it operational
DIGITAL TWINS – IN DESIGN PHASE

Simulate
• Simulate the model under
various parameters
• Visualize and optimize the
performance
DIGITAL TWINS – IN SIMULATE PHASE

Plan + Build
Best Architecture
Best Configuration
Best Material
Cost
DIGITAL TWINS – IN PLAN & BUILD PHASE

Smart Factories
A digitally-enabled manufacturing plant
may have digital twins from design through to fully manufactured products, and even to
post-deployment performance and maintenance in the field.

Smart Factories
Sensors - device that converts signals from one energy domain to electrical
domain.
The more devices that have sensors for sending
back to their respective digital twins, the greater
the ability to model and improve the entire
process
These network of sensors are called
Industrial IoT

DIGITAL TWINS – IN OPERATE & MAINTENANCE PHASE
Operate & Maintain
Supervisory Twins
• Supervisory
• Diagnostic and Control
• Predictive

Operate & Maintain
Value Chain - Level 3
• High Level Metrics
• Enterprise or Business KPI
• Multiple Factory or Single Factory

Operate & Maintain
• Operational Process Level
• Production Line performance
• Unit Performance

Operate & Maintain
• Asset level metrics
• Equipment performance
• Equipment Controls

DIGITAL TWINS – WITH AI
Optimize
AI and Data Analytics
•Highlight process improvements
•Optimize production run
•Direct resources in real-time or in the future
•Proactively anticipate and fix problems
•Report MTBF
•Predictive Maintenance
•Inform overall equipment efficiency

DIGITAL TWINS – IN PHARMA
From Industry 4.0 to Pharma 4.0 Operating Model
Digital Maturity
Data integrity by Design
Resources
Digitalization
Workforce 4.0 - Available and
Qualified
Organization and Processes
ICH Holistic Control Strategy
Lifecycle Management
Information Systems
Holistic Value Network Integration and
Traceability
Culture
Communication
Decision Making
PHARMA 4.0

DIGITAL TWINS – ICH PHARMA 4.0

Plant Process Product Patient
Plant Model (BIM)
• Building layout
• Building modules
• Equipment & Utility
modules
• Module properties
• HVAC and Piping
• Material & Energy Flow
• 3d Visualization
• Virtual walkthroughs
Process Model (PIM)
• Process steps
• Process parameters
• Process equipment
• Process control systems
• Process simulation
• Simulation of material,
energy and information
flow
Product Model
• Product structure
• Product Properties
(CQAs, CPPs)
• Product Quality by
Design (QbD)
• Product design for
manufacturing,
assembly and packaging
• Product design for
individuals (personalized
drugs)
Patient Model
• Personal ID
• Personal properties
• Personal genome
• Personal disease
characteristics
• Personal drug reactions
(e.g., allergies)
Requires
Requires
Requires
Plant fit for process Process fit for product Product fit for patient

Siemens – ICH Model

• Based off historical data
• Statistically derived
• Seeks to establish statistical
relationships and correlations
between inputs and outputs
• can tackle problems with multiple
space and time scales
• Requires large datasets
---
• Can be inaccurate if not enough
data is present
• Can give theoretical outcomes
Process Twins - Flowsheet Modeling
Statistical/Empirical Models
• Based off physical phenomenon
• Derived with underlying physical,
chemical or biological phenomenon
understanding represented as
equations
• Seeks to establish a mechanistic
relationship between inputs and
outputs
• Requires small datasets
---
• Can be inaccurate if mechanisms
are unknown or assumptions are
not applicable
• Can be computationally expensive
• Difficult to accurately incorporate
information from multiple space
and time scales
Mechanistic Models

Process Twins - Flowsheet Modeling
Empirical Models (BB) Hybrid Models
Mechanistic Models (WB)

Process Twins – Hybrid Models
Biological
phenomenon
Modelling
principles
+
INPUT OUTPUT
Deductive model
Mechanistic model
(simulations)
New hypothesis for a casual mechanism observed in
an experimental scenario that is recreated through
simulation (mechanistic modelling) out of a series of
constructive principles
Modelling principles derived
from the data
Machine learning
(classification/regression)
Inductive process to extract unseen patterns
existing in the data. Machine learning models
filter out irrelevant or redundant information
to discover relevant co-occurrences and/or
dependencies
Additional data derived from
mechanistic hypothesis
Predictive model
Historical data of patient
cases, treatments and
final outcomes

Process Twins - Upstream
Categories Methods Platforms Comments
Bioreactor fluid
dynamics, system
heterogeneity
CFD simulation
CFD +PBM simulation
CFD +kinetics model
Ansys Fluent,
COMSOL Multiphysics
Support to understand operations such as agitation, aeration, nutrients
feeding.
Guide process scale-up. Computationally expensive. Can reduce the
computational time by using a compartment model, hard to be
validated.
Cell growth, nutrients,
and metabolism. Product
quality (protein
glycosylation)
Kinetic model
MATLAB, gPROMS,
Visual Basic for
Applications
Capture and predict the dynamic profile of the cell culture.
Correlate critical process parameters (CPPs) and critical quality
attributes (CQAs). Require a large amount of data for parameter
estimations.
Stoichiometric methods MATLAB, OptFlux etc.
Deal with a large amount of mechanistic reaction, genome-scale
simulation. Need to integrate with the kinetic model to capture the
dynamic profiles
Multivariate tools MATLAB
Require a large amount of data. Represent input–output correlations. Do
not capture the mechanistic correlations.
Media formulation
Multivariate analysis
MFA
MATLAB Identify nutrient correlations, improve productivity and cell viability
Product impurities
Regression model and
Multivariate analysis
MATLAB
Capture predict titer, aggregation, low molecular weight components,
and glycan groups

Process Twins - Downstream
Bind-elute/flow-through
chromatography
Mechanistic: Plate
model, mass balance
model, general rate
model with their
simplifications models
MATLAB, CADET,
ChromX Capture moving and stationary phases, obtain breakthrough curves,
gradient elution curves. Predict the product concentration and impurities
(charge variant, aggregates, host cell proteins)
Transport dispersive
model—ANN model
MATLAB
Filtration/ultrafiltration
Mechanistic: Film
theory, Osmotic
Pressure Model,
boundary layer, mass
transfer coefficient)
Aspen Custom
Modeler
Capture volumetric flow, flux, and pressure across the filtration
membrane. Can be used for model predictive control.
Hybrid model (ANN-
mechanistic film theory)
MATLAB
Downstream integration
(precipitation)
Empirical model
(quantitative structure-
activity relationship)
+Mechanistic model
NA
Physico-chemical process model supported by design of experiment
(DoE). Capture CPP and CQA
Downstream integration
and optimization
Mechanistic model—
Artificial Neural
Network-Optimization
algorithm
MATLAB
Optimize overall process yield and solvent use by adjusting operation
parameters such as duration. However, only high molecular weight
contamination was considered.

Process Twins – Integrated
Residence time
distribution
Probability distribution
function for each unit
operation
Python
Correlate input material operating conditions, design parameters with
outlet profile. Easy to update.
Activity tracking and
decision making
Discrete Event
Simulation
Extend Sim, Simul8
Discrete/dynamic system, track activity, scheduling, and resource
utilization
Material tracking and
decision making
Mechanistic/Empirical
model
SuperPro Designer,
Biosolve
Track material balance and optimize cost-effectiveness. Process
debottlenecking, capacity planning
Process risk assessment
Implement process
model with Monte Carlo
analysis
MATLAB
Evaluate parameter sensitivity, impurity purification, and product
quality. Hard to apply to computationally expensive model
Overall process
optimization
Integrate flowsheet
model with optimization
solvers
SuperPro
Designer-VB-Matlab
Optimize environment impact and cost-effectiveness by adjusting 4
operating parameters

DIGITAL TWINS
USE CASES

DIGITAL TWINS – USE CASES
AWS and Novartis: Re-inventing pharma manufacturing

HOW DO YOU STAY RELEVANT IN THESE CHANGING TIMES
:-)
* Left-Right: Pre icon days, SoftBank 1997, 2001, 2004, 2008; Apple 2008, 2018, WhatsApp 2019, 2020

WHAT DOES IT TAKE TO STAY RELEVANT?
“Today’s disruption requires both a broader perspective and a
narrower focus.”
How do you resolve this apparent duality? Where do you start?

Digital_Twins_v1.0.pdf

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