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Using ontologies to augment measurements with physico-chemical simulation for industrial application
EMMC Workshop on Interoperability
in Materials Modelling
St. John’s Innovation Centre
7-8 November, Cambridge, UK
Dr Amit Bhave, CEO & co-founder
CMCL Innovations
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Content
• Company overview – software products
• Motivation – Ontology Engineering
• Customer case study 1: Non-road application
• Customer case study 2: Eco-industrial park
• Summary
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CMCL Innovations: an overview
www.cmclinnovations.com
Simulation and design software supplier to industry and academia
>10 years in innovative R&D and advanced engineering services
SME with an organically growing, experienced team
Computational Modelling Cambridge Ltd.
Mission
Delivering digital engineering solutions to industry and academia
Business model
Software | Consulting | Training
Market segments
Powertrains & fuels | Energy & chemicals
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CMCL Innovations: software products
“Combining physico-chemical simulation…
SRM Engine Suite
kinetics
MoDS
Robust simulation of IC engines
Reactor models, fuel models, emissions pathways
Parameter estimation, sensitivity analysis and uncertainty propagation
Surrogates, data-driven model generation and Design of Experiments (DoE)
…with advanced statistical algorithms”
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Motivation
• Data heterogeneity– Customers: Extensive non-standardised data– Gaps, errors and uncertainties related to the data– Inconsistent data formats
• Differences in vocabulary: – Data channel names – Models vs. numerical methods– Variables (e.g. inputs, outputs) vs. parameters (model, numerical)
• The size of the evolving data, the heterogeneities and the inconsistencies inflict a cost on robust and systematic model development and application
• Ontology, a key part of the overall solution: formal, explicit specification of a shared conceptualisation
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Customer case study - 1
Engine operating window
4.4l Turbocharged Aftercooled (TA) Tier 4 capable IC engine
• Background & objective– CAT 4.4 IC engine used in multiple machines/applications: agriculture,
construction, cranes, loaders, etc. – Engines designed to meet stringent emissions regulations
– To develop and apply an integrated workflow combining physico-chemical and statistical models to augment the measurements data
– Data heterogeneity and evolution are the main drivers for standardisation
C4.4 ACERT Industrial EngineSource: www.cat.com
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Methodology
Store – timeless, accessible data
Manipulate – control, manage your data
Use – visualise and understand
Build – Build models on top of the data
Optimise – Model parameter estimation
Validate – Compare with experiments
Run –predictive/parametric studies
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engineML
• Consistent format
• point data (e.g. rpm, CO, ul) • time resolved data (p-CA)• Apparatus (production engine, research engine)• errors• data type (consistent units)• raw or processed
• XML code selected
machine and human readable tree structure validated against schema
• Easily accessible database
read by model code data stored consistently old data never forgotten
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Particulate matter (PM) emissions
• Soot emissions:
[ ] expB C
f
Dsoot g A mps phi
T
SAE 2010-01-0152
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Friction
• Statistical/data-driven analysis to produce a hierarchy of models
• Compare the performance of all models as a function of:• Choice of the objective function• Training, verification and
validation methodologies
• Final recommendation – model ranking based on technical criteria such as error measures and CPU expense.
Fast-response data-driven friction model from MoDS
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In-filling the operating window
Soot blind tests at part load rated speed
CO blind tests at full load rated speed
NOx blind tests at full load at 1800RPM
uHCs blind tests at peak torque
NOx emissions
Soot
+
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Customer case study - 2
CMCL’s role: Supplier of the MoDS software
System development for eco-industrial parks using ontological innovation
Li Zhoua, Ming Panb, Janusz J. Sikorskib, Sushant Garuda, Martin J Kleinelanghorstc, I. A. Karimia, Markus Kraftb, c
aDepartment of Chemical and Biomolecular Engineering, National University of Singapore, SingaporebDepartment of Chemical Engineering and Biotechnology, University of Cambridge, United Kingdom
cSchool of Chemical and Biomedical Engineering, Nanyang Technological University
Zhou et al., Applied Energy, 204 (2017), 1284-1298
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Eco-industrial park (EIP)
Jurong Industrial Park (Singapore)
A cluster of businesses that collaborate with each other and the localcommunity to efficiently share resources, and to reduce waste and pollution.
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OntoCAPE
A general-purpose ontology for applications in the domain of Computer-AidedProcess Engineering (CAPE) was adopted and extended
Extendable(can be extended to other domains: Electrical engineering, Transportation)
Specific application(chemical processes)
W. Marquardt, J. Morbach, A. Wiesner, and A. Yang. OntoCAPE: A Re-Usable Ontology for Chemical Process Engineering. Springer Science & Business Media, 2010.
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Ontological repository of the EIP
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J-Park Simulator
A cyber - infrastructure for Jurong Island industrial park
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Biodiesel plant representation in OWL
①
②
③
④
⑤
⑨
⑥
...
...
① - IRI for biodiesel plant; ② - IRI for the surrogate model, through which its executable model (⑤) and name
information (⑥) can be extracted.③ - IRI for the mathematical programming model, through which, the GAMS code
(⑨) can be extracted.④ - IRI of the subsystems (unit operations).
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Biodiesel plant as a composite modelAssociating a technical component with its semantic information and executable mathematical models
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Support for decision making
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Summary
Dr Amit BhaveCEO & co-founder
[email protected]+44 1223 370030
Systematic model development and application using Ontology Engineering
THANK YOU
Case Study 1 Case Study 2
www.cmclinnovations.comwww.twitter.com/cmclinnovations