Process optimisation using machine learning …...Real-Time Machine Learning for Process...
Transcript of Process optimisation using machine learning …...Real-Time Machine Learning for Process...
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Process optimisation using machine
learning techniques
Matthew McEwan
(Principal Engineer – Perceptive Engineering Ltd)
Assisted by Jason Fung & Mihai Matai-Rascu
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About this workshop….
•Introduction to Perceptive Engineering
•Challenges in Process Development/Optimisation and How Machine Learning Can Help
•About the Nelder-Mead Self Learning Optimisation Algorithm
•Introduction to the Experimental Rig
•Optimisation runs
•About Adaptive Model Predictive Control
•About Gaussian Optimisation
•Results and Discussion
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Perceptive Engineering
2-Minute Capability Pitch
•Solely focussed on software and solutions applying Advanced Process Control
techniques
NinePhD Software Programmers
DedicatedSupport Team
20Application Engineers
FourLocations
We work with
25MNC’s
Worldwide
Technology partners: Support
EightResearch Centres
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PharmaMV
Process Control & Monitoring
In routine manufacturing, PharmaMV sits on top of a SCADA pooling parametricand PAT data and using this to controlprocess parameters to CQA’s
MPC PATLab
DataProcess Sensors
Finished Product / Package /Consumer
Process
Mechanistic Models
Spectral Calibration
Data-driven Models ‘Data to
Knowledge’ Layer
PharmaMVSoftware
Cloud/Local Database
UPSTREAM &
RawMaterials
Process / Plant Dashboards
In the lab, PharmaMV can act as a SCADA/HMI to pull control and monitoring of discrete pieces of equipment into a single interface
allowing ALL data to be accessed from a single interface and used in modelling and process understanding
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PharmaMV
Philosophy of “Data”
DATA• Process• Lab/Offline• PAT/Spectral• Contextual information
INFORMATION• Data Alignment • Pre-processing• Pre-treatment• Key-Performance Indicators
KNOWLEDGE• DoE Execution• Rapid Development• Data-Driven and hybrid modelling
WISDOMRobust, real-time prediction, soft-sensors, monitoring, control and optimisation
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Teaching the machine
Today….
https://towardsdatascience.com/workflow-of-a-machine-learning-project-ec1dba419b94
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Motivations and Benefits
Process Development Approaches
•Traditional “One at a Time” approach
• Trial and error optimisation of the reaction
• Significant human input – depends of the know-how of the
chemist
•Quality by Design Approach
• Application of Design of Experiments
• Automation can be used to execute pre-defined
experimental conditions
• Extensive experimental effort required
•
*An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product The Journal of Organic Chemistry 2018 83 (23), 14286-14299
ML – Recursive Learning Approach• Automation and online analysis combined with a “curiosity”
algorithm• Outperforms a human to get to the optimum
• No human interaction required after initialisation
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Nelder-Mead Self-Optimisation
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Smart Data Generation. . . Nelder Mead Method
What and Why?
What is it? Why do you want it?
BenefitRun less trials to find optimal process parameters
ValueCost & time savings, less wasted batches
Iterative “DoE” which calculates next
experiment based on previous results
1
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Smart Data Generation. . . Nelder Mead Method
Simple Overview
*An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product The Journal of Organic Chemistry 2018 83 (23), 14286-14299
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How?
2
• Optimisation via customised Nelder-Mead type algorithm
• Customised?• Objective function style redefined for target-aiming type of problem
• 𝑓 𝑥 = 𝑇𝑎𝑟𝑔𝑒𝑡 − 𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 2
• Stopping parameters re-defined• i.e. stop when Target is within threshold
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How?
Example for a 2 variable problem
3
1. Initial Parameters
V1
V2
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices
V1
V2
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank
V1
V2
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank4. Reflect away from worst result to
generate new set of parameters
V1
V2
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank4. Reflect away from worst result to
generate new set of parameters5. Evaluate new point, if favourable
expand (1), if not contract (2,3)
V1
V2
1
2
3
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How?
Example for a 2 variable problem
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank4. Reflect away from worst result to
generate new set of parameters5. Evaluate new point, if favourable
expand (1), if not contract (2,3)6. If none of these points are better
than the current best then the simplex is shrunk toward the best. But . . .
3V1
V2
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank4. Reflect away from worst result to
generate new set of parameters5. Evaluate new point, if favourable
expand (1), if not contract (2,3)6. But. . V1
V2
1
2
3
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank4. Reflect away from worst result to
generate new set of parameters5. Evaluate new point, if favourable
expand (1), if not contract (2,3)6. But. . If we accept then the new
point becomes part of the simplex
V1
V2
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How?
Example for a 2 variable problem
3
1. Initial Parameters2. Construct initial simplex (n + 1)
vertices3. Evaluate and Rank4. Reflect away from worst result to
generate new set of parameters5. Evaluate new point, if favourable
expand (1), if not contract (2,3)6. Either way the steps repeat with
the new simplex7. Until the stopping parameter is
met
V1
V2
V1
V2
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Our Experimental Rig
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Dye sources (‘blue’, ‘red’ and ‘clear’ (disturbance))
Dye pumps
Static mixing chamber
‘Ocean Optics’ Halogen light source
‘Ocean Optics’ STS-VIS Miniature Spectrometer
‘Final Product’ vessel
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Rig Demonstration Run
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Adaptive Model Predictive Control
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Model Predictive Control –
Principle of Operation
•To obtain control moves, need:
• The current and recent past state of the process
• the model, and
• an optimisation algorithm
•To calculate the moves
• first, predict the future behaviour of the process (using a model)
• then work out the “best” way to manipulate the MVs in order to achieve the control objectives.
• “Best” is defined through a cost function that is minimised by the optimisationprocedure to yield the control moves.
• Minimisation of the cost function can directly consider the process constraints.
© Perceptive Engineering 2017
PredictionControl
CalculationsProcess
Model
Set-point
Process Outputs
Model
Outputs
Residuals
Control
Outputs
+-
PredictionControl
CalculationsProcess
Model
Set-point
Process Outputs
Model
Outputs
Residuals
Control
Outputs
+-
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Model Predictive Control
Simple Overview
© Perceptive Engineering 2017
Prediction Horizon, P
Control Horizon, M
Set Point (Target)
FuturePast
Control Move, u
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Previous Sensor Values
Previous Control Moves
Predict Future Behaviour
Calculate Control Moves
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‘Gaussian’ Multi-Objective Optimisation
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True Multi-objective Optimisation
Gaussian Search
Process specific parameters to
consider
StartInitial
Design (LHS)
Fit Surrogate
Models
Evaluate Large LHS
Set
Find Pareto Front
Calculate Hyper
volume
Choose Experiment
Carry out Experiment
Cheap! Done in-silico
Stop? End
N.
Y.
D
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True Multi-objective Optimisation
Gaussian Search
D
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Data Generation
Exploration vs Exploitation
Variable 1
Var
iab
le 2
Variable 1
Var
iab
le 2
Variable 1
Var
iab
le 2
DoE:Pure Exploration
Nelder-Mead:Pure Exploitation
Gaussian Search:Both via Surrogate Models
Surrogate PointsExperiment Points
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Nelder Mead and MPC
Does each algorithm Learn?
The Nelder Mead curiosity algorithm doesn’t learn in the
same way as other AI (Neural Networks for example):
• Constrained “trial and error” learning
• Minimising or maximising the objective function.
• Systematic approach leads to a (local) optimum
• No “predictive” capacity
•
MPC predicts future behaviour using it’s dynamic model.
• Traditionally the model is built offline from process
data
• A linear representation around a defined operating
point
• Online Adaption can be used to update the model
(regression based on new information).
• Narrow learning under human supervision.
APC Adaption Workflow
Nelder Mead Optimisation
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Optimisation as a controller?
Effect of Process Disturbances
Optimisation In Spec
Process Disturbance Introduced
Out of Spec Repeat Optimisation In Spec
Only way to manage is to repeat optimisation
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Self-Optimising Reactor Case-Study
Combining algorithmic approaches
•These two approaches are complimentary:
Model Predictive ControlSelf Optimisation
Advanced Controlbuild model on process data, keep the
process at that optimum, whilst compensating for raw material and
process disturbances.
Self OptimisationHit the optimum efficiently and generate
useful data in doing so.
WISDOM
Self Optimisation + Model Predictive Control
SMARTDATA
GENERATION
CONTROL
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Combined Advanced Process Control And Machine Learning
Example
Learning - Model Adaption MPC Active Setpoint 1 Setpoint 2
Throughput maximisation
Adaption + ML Optimisation In Specification – MPC ONSteady State
Objective function maximised through ML and
MPC model adapted
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Nelder-Mead Space Filling
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Gaussian Search Space Filling
Experimental Points
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Gaussian Search Space Filling
Surrogate Points
Experimental Points
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Smart Data Creation
How ‘Rich’ is the Data for Generating an MPC Model?
DoE
NM
GS
RMSEP
CQA1 CQA2
0.12
0.030
0.013
6.8
1.05
0.51
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Comparison
Does the Machine Learning algorithm do what we want?
Automated DoE
Adaptive MPC
Nelder-Mead Gaussian Search
Optimised Process
“Single Objective”
Pseudo-Multi-Objective Possible
“Multi-Objective”
Static Process Model
Anova and Linear Model at Best
(Further Modelling Step)
(Further Modelling Step)
Linear and/or Non-Linear for Each Objective
PAT Calibration Unlikely Unlikely
Rich enough Data for MPC
Sometimes
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Real-Time Machine Learning for Process Optimisation
Webinar Summary
ML has brought along with it a whole new set of terminology for existing techniques
The potential of these techniques is significant provided they are selected with care
Gartner Hype Cycle for Emerging Tech (2017)
Within ML are a lot of methodologies that bring genuine value
Most appropriate when dealing with large data sets
Like all technology, these have strengths and weaknesses
These methodologies require insightful application
Beyond the Hype:
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Thank you for listening!
To learn more:
www.perceptiveapc.com