Environmental Impact Monitoring (EIM) -- Software Tools for Mitigation

John C Sloan PhDJohn C. Sloan, PhDTaghi M. Khoshgoftaar, Full Professor

College of Engineering & Computer ScienceCollege of Engineering & Computer ScienceFlorida Atlantic University

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Vision

Tangible vision* & measurable objectives >

Suppose we were to operate: *

(i) a fleet of over one hundred 2MW ocean turbines(i) a fleet of over one hundred 2MW ocean turbines,(ii) with only scheduled annual maintenance,(iii) while attaining a composite uptime of eighty-five percent(iii) while attaining a composite uptime of eighty five percent,(iv) and (iv) and measurablymeasurably minimizing impact on the environment.minimizing impact on the environment.

Is it s stainable?Is it s stainable?.. Is it sustainable?.. Is it sustainable?

This talk concerns Objective (iv).

2* Disclaimer: This ‘vision’ is purely hypothetical and is meant to stimulate discussion.

j ( )

d cPhysical setup (prototype – not to scale – not yet deployed)

f d cf

a9m

e

9m ae 94m

ff??m

3m ba – direction of gulf current d – control & deployment buoy

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a direction of gulf currentb – anchor and mooringsc – telemetry buoy

d control & deployment buoye – turbinef – EIM recorders & buoy

Long term impacts: Active noise

Structure types and their point-source stressors:

Long-term impacts: • decreased biodiversity?• changes in species

composition? Marine Mechanical

Active noisefrom engines

??

p• indirect toxicity effects

like oxygen depletion?

Offshore

MarineSurfaceVessels

Mechanicaleffects

Offshore DrillingPlatform

OceanTurbineFarm

Hydrocarbonfouling

Immediate impacts: on weakly swimmingorganisms (i.e.,g

either EMR or

Localspeciesimbalance?(i e FAD)

???

tortoises, jellyfish)?.. indirect effects from cavitation?structure

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either EMR orPassive noise from gearbox

(i.e., FAD)

stressor

Ecology: Engineering:

Initial impact / mitigation workflow:

ConsoleImpact analyses 1

Ecology: Engineering:

Monitor/ControlEIM 3

RelaysSensorsAssays

PlantEnvironment 2

Initial regulatory approval of proposed plant

Continuous measurement of point source stressors1

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Continuous measurement of point-source stressors

Continuous mitigation of point-source stressors2

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Ecology: Engineering:

Operational mitigation workflow:

ConsoleImpact analyses 1

Ecology: Engineering:

Monitor/ControlEIM 3

RelaysSensorsAssays

PlantEnvironment 2

Initial regulatory approval of proposed plant

Continuous measurement of point source stressors1

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Continuous measurement of point-source stressors

Continuous mitigation of point-source stressors2

3

MCM/PHM* applied to EIM

Archive and Share (AS)

Architecture

Adv Gen (AG)Conduct mitigation

Archive and Share (AS)

* MCM/PHM is “machine

Prognosis (Px)

Adv. Gen (AG) MCM/PHM is machine condition monitoring & prognosis and (machine) health monitoring”

Diagnosis (Dx)

Prognosis (Px)Estimate impacts

State Detection (SD)

g ( )

Data Manipulation (DM)

Establish baselines

7Data Acquisition (DA) Perform species assays

Kinds of data needed from species assaysData Acquisition

AG A = Spacial-temporal: ta = timestamp B = Topside:

C = Wetside: oc = oxygenation

Px ia = time intervalxa = longitudeya = latitude

lb = ambient lightrb = rainfallwb = wind

sc = salinitypc = Phvc = current velocity

Dxyza = depth...

wb wind...

vc current velocitydc = current headinghc = temperaturet t biditSD tc = turbidity...DA Bottleneck: Assays are

costly and need standardization

DA

DM

Y b tOf ( A B C D )

D = Species: id = identifiercd = count

costly and need standardization

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DA YDA = subsetOf ( A x B x C x D ) cd = count..... a relation over a set of relations

How many members of each species

Data Manipulation

AG

y pwill be present at each assayed location, “all things being equal”?

DM Bottleneck: Assays are not comparable to one another

need to factor out variations

Px

-- need to factor out variations. Dx

Examples: seasonally-adjusted SD

p y jor depth-adjusted assay results,assays on overlapping species

DA

DM YDM = fDM ( YDA )

Y b tOf ( A B C D )Apply machine learning

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DA YDA = subsetOf ( A x B x C x D )and data pre-processing techniques to solve for fDM

What markers correspond to the f i f i t t?

State Detection

AGpresence of species of interest?

Why? To identify smaller,

Px faster, cheaper assay instruments and techniques!

SD Bottleneck: Equilibrium notconstant, long latency (i.e. manyreproductive cycles) can mis

DxExamples: T k T l

reproductive cycles), can mis-identify stressors in the meantime.

SD YSD = fSD ( YDM ) Tucker Trawl compared toShadowed Image

DA

DM YDM = fDA ( YDA )

Y b tOf ( A B C D )

Feature selection (i.e., PCA) to make fSD tractable

gParticle ProfilingEvaluation Recorder

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DA YDA = subsetOf ( A x B x C x D )and parsimonious. Recorder (SIPPER)

Dx Bottleneck: Desired simulation fidelity (i.e., using EcoSim) may not be feasible.

Diagnostics

AGTo what extent do certaint lt i

( e , us g coS ) ay o be eas b e

Caveat: EcoPath toolset geared t i l fi h i Ad t

Pxstressors result in someimpact (+ / -)?

to commercial fisheries – Adapt to point-source stressors?

Dx YDx = fDx ( YSD U F) F = Introduced stressors:*mf = mechanical impactnf = audible noise

SD YSD = fSD ( YDM )nf audible noiserf = EMRef = electrocutiontf t i it ( i t t)

DA

DM YDM = fDA ( YDA )

Y b tOf ( A B C D )Mass balanced trophic models (i e

tf = toxicity (non-existent)* all are manageable

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DA YDA = subsetOf ( A x B x C x D )Mass-balanced trophic models (i.e., EcoPath) may be used for fDx.

Px Bottleneck: Latency – see SD. C di i bl f i ?

Prediction

AGCan we predict sustainable farm size?

Px YPx = fPx ( YDx,t)

Dx How well do simulations predict subsequent behavior?

YDx = fDx ( YSD U F)

SD YSD = fSD ( YDM )

Simulation (i.e., EcoSim) and

DA

DM YDM = fDA ( YDA )

Y b tOf ( A B C D )

( , )distribution (i.e., EcoSpace) models may be used for fPx.Shouldn’t we also be modeling diffusion?

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DA YDA = subsetOf ( A x B x C x D )Shouldn t we also be modeling diffusion?

Advisories, sharing & dissemination

AG YAG = fAG ( YPx U YDx U YSD U YDM U YDA )

YPx = fPx ( YDx U Y’DA)Px Data management tools/strategies of other presenters may be reasonable choices for fAG Plus workflow tools (i e OMII-BPEL

YDx = fDx ( YSD U F)DxfAG. Plus workflow tools (i.e., OMII-BPEL, Pipeline Pilot) to automate workflows.

SD YSD = fSD ( YDM )

AG Bottleneck: Provenance and reproducibility of results.

DA

DM YDM = fDA ( YDA )

Y b tOf ( A B C D )

-- a sociological more than technological bottleneck?

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DA YDA = subsetOf ( A x B x C x D )

Gaps:

1. Identify and measure point source stressors2. Identify the kinds of data needed for EIM3. Ground truthing of automated assay instruments4. Automate species identification from these instruments5. Address comparability issues between assaysp y y6. Supplement mass-balance models with diffusion models7. Further address latencies between stressors and effects8 Better define the notion of equilibrium in state detection8. Better define the notion of equilibrium in state detection9. Tease out effects of climate change from local impacts10. Markers that indirectly measure species composition11 Ad t ti t E P th T l t f i t t11. Adaptations to EcoPath Toolset for point-source stressors12. Are there more suitable software tools?13. Workflow toolsets to expedite licensing & dissemination

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p g

Regulatory workflows:S Kopf Siting Methodologies for Hydrokinetics: Navigating the Regulatory

Bibliography:

S. Kopf, Siting Methodologies for Hydrokinetics: Navigating the Regulatory Framework, http://www1.eere.energy.gov/windandhydro/pdfs/siting_handbook_2009.pdf, Sections 3.9 – 3.16

Sh i & di i tiSharing & dissemination:Yale Law School Roundtable on Data and Code Sharing, "Reproducible Research”,Computing in Science & Engineering, vol.12, no.5, pp.8-13, Sept.-Oct. 2010

SIPPER:T. Luo, K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T Hopkins “Recognizing Plankton Images from the Shadow Image

p

and T. Hopkins. Recognizing Plankton Images from the Shadow Image Particle Profiling Evaluation Recorder”, IEEE Transactions on Systems Man and Cybernetics Part B – Cybernetics, vol. 34, pp. 1753-1762, 2004.

S Samson T Hopkins A Remsen L Langebrake T Sutton and JS. Samson, T. Hopkins, A. Remsen, L. Langebrake, T. Sutton, and J.Patten, “A system for high resolution zooplankton imaging,”IEEE Journal ofOcean. Engineering”, vol. 26, pp. 671–676, Oct. 2001.

E P th T l t

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EcoPath Toolset:V. Christensen, et. al. “Database-driven models of the world’s Large Marine Ecosystems”, Ecological Modelling, vol. 220, nr. 17, pp. 1984-1996, 2009.

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