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kNN-RVM Lazy Learning Approachfor Soft-sensing Modeling of Fed-

Batch processes

Jun Ji, Hai-qing Wang*, Kun Chen,and Dian-cai Yang

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Definiton of Terms:

Fed-batch processes- inherently difficult to modelowing to non-steady-state operation, small-samplecondition, instinct time-variation and batch-to-batchvariation caused by drifting

Soft-sensing approachesare investigated to establish

an online monitor and control of fed batch; needsdevelopment

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Types of Soft-sensors:

SOFT-SENSOR CHARACTERISTICS

AND LIMITATIONS

Analysis based methodsregression may fail in small-samplecondition and be sensitive to

measurement error

Artificial neural networks(ANN) based methods

difficulty in determiningnetwork complexity

Kernel based methods effective to deal with small-size samples but areinsufficient to trace the time-varying characteristics of theprocess

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SOFT-SENSOR CHARACTERISTICS ANDLIMITATIONS

Adaptive kernel learningalgorithm (AKL)

effectively updates the modelwith a two-stage recursivelearning mechanism butinaccurate prediction may stillarise in certain local regionwhere instinct time-variationis occurred

Relevance VectorMachine(RVM) need very few kernelfunctions but may not beapplicable to large data sets

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Relevance Vector Machine(RVM)

given the dataset of input-targetpairs N

standard formulation (batchculture) is followed and p(t|x) isassumed to be Gaussian

Does not use enough kernelfunctions

Does not follow standardformulation for fed batch whichhave numerous data sets

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Adaptive Kernel Learning

Algorithm (AKL)

Erroneous in time variation steps

Does not consider time changesand takes data sets at one time

Like RVM, does not use enough

kernel sensors

Have slow response time whichlimits its capabilities to detectsignificant changes occuring in

the culture

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kNN-RVM Lazy Learning Approach

Distance Vector

Indices

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Characteristics

Able to use enough kernel values

Has quick response time that enables it to detectsmall significant changes in the culture

Able to deal with numerous data sets

Considers time changes and is able to see trendsthrough time

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Performance of Fermentation Machines usingRVM, AKL and kNN as soft-sensors

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Today

Latest fermentation machines use kNN as soft-sensors

RVM and AKL are still used however without thesame precision as that kNN could offer

RVM and AKL are still used for cultures which do not

require highly sensitive measurements to maintaintheir growth

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REFERENCES

http://www.sciencedirect.com/science/article/pii/S0098135409000076

http://www.scientific.net/AMM.20-23.1185

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=59

30437 http://dynamics.org/~altenber/UH_ICS/EC_REFS/GP_REFS/GEC

CO/2004/31031078.pdf