using ontologies to augment measurements with physico … · 2017. 12. 25. · using ontologies to...

cmclinnovations.com | [email protected] 1

Copyright 2017 © CMCL Innovations

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

http://www.cmclinnovations.com/

http://www.twitter.com/cmclinnovations



Content

• Company overview – software products

• Motivation – Ontology Engineering

• Customer case study 1: Non-road application

• Customer case study 2: Eco-industrial park

• Summary





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





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”





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





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



www.cat.com



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





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





General data





Case data





Particulate matter (PM) emissions

• Soot emissions:

[ ] expB C

f

Dsoot g A mps phi

T

SAE 2010-01-0152





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





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

+





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





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.





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.





Ontological repository of the EIP





J-Park Simulator

A cyber - infrastructure for Jurong Island industrial park





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).





Biodiesel plant as a composite modelAssociating a technical component with its semantic information and executable mathematical models





Support for decision making





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



mailto:[email protected]



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