the conjunction of process and spectral data for enhanced fault detection

Elaine MartinCentre for Process Analytics and Control Technology

University of Newcastle, England

www.ncl.ac.uk/cpact/

The Conjunction of Process and Spectral Data for Enhanced Fault Detection

Centre for Process Analytics and Control Technology (CPACT)University of Newcastle, UK

Motivation

It is conjectured that there may be factors relating specifically to a process that cannot be identified from the spectroscopic measurements that could be described by the process data or vice versa.

Consequently one way to enhancing prediction accuracy and process performance and fault detection is through the integration of process and spectral data.

The aim of the subsequent studies was to investigate the combined power of spectral and process data.


Overview

Process Modelling

Fermentation Process• Spectral Data• Spectral and Process Data

Process Monitoring and Fault Detection

Polymer-resin Manufacturing• Process Data• Process and Spectral Data


Challenges in the Monitoring of Fermentation Processes

Fermentation is a process in which micro-organisms convert chemical species to products of higher value.

On-line information relating to the progression of the process is not easily attained.

Near Infrared and Mid Infrared spectroscopy have been applied for the monitoring of fermentation processes.

The successful implementation of these spectroscopic approaches necessitates the application of appropriate multivariate data analysis techniques, such as partial least squares (PLS).


Experimental Data Set

The industrial pilot-plant scale Streptomyces fermentation process involves two stages:

Seed stage Final stage

The seed stage materialises in the generation of biomass. The starting ingredients include carbohydrate, soya protein,

vegetable oil and trace elements in water.

The biomass is transferred to the final stage for the production of the desired product.

The final stage is a fed batch process lasting approximately 140hrs.

NIR measurements were collected for the final stage of the process.


Spectra Data Acquisition

The NIR spectral data were recorded using a Zeiss Corona 45


Description of the Data Set

Final stage data from 7 standard batches and 7 Design of Experiment batches form the basis of the subsequent analysis.

Data collected included on-line process data, off-line data, biochemical and NIR measurements.


Methodological Summary

Pre-processing of the spectral data set First derivates Splining

Segmented wavelength region selection

Global modelling – Linear PLS, Neural Network PLS, Quadratic PLS

Local modelling - Linear PLS, Neural Network PLS, Quadratic PLS

Bagging of the models Linear partial least squares Averaging


Data Pre-processing

The NIR data (Zeiss Corona NIR) were recorded every 15 minutes and the first derivatives were taken.

Since only ten values of titre were recorded, a spline was fitted to the data.

The splined titre values were aligned to the 550 spectral values for each batch.

The range utilised for both the spectral and quality data was 43.75 to 125 log hours.


Data Pre-processing

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Real and splined values for batch 88

Batch 88

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NIR Data and First Derivatives

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Stack Batches 2088 to 2100

NIR Data First Derivative


Spectral Window Selection Algorithm

N

Select training and validation batches

Mean centre and take derivatives of the spectral

data

Generate random centres and widths

Build model ‘input’ matrix eliminating

common data. Generate PLS model Calculate RMS errors

Generate random changes to centres

and widths

Apply the randomchanges to the

current centres and widths

Build new input matrix,

generate model and calculate RMS errors

Has the RMS on training

data decreased?

Has number of iterations been

exceeded and there are more models to

build ?

Present the final bagged model

N

Y

Y


Spectral Window Selection Algorithm

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Wavelength

Centre

Width

Generate random increment in centre and width

Centre

Width

Update the centre and width

Take another step with theCentre and Width increment

Step too far. The prediction error has increased. Go back to where we were.

Generate a new increment in centre and width and continue search

Has the prediction error decreased?Yes, then a step in the right direction


Benefits of the SWS Algorithm

SWS offers the opportunity to consider not only the extremes of a single wavelength and the full set but also restricts selection to multiple sub-sets of the full set.

Finds the ‘best’ possible models for the product concentration and the biochemical components.

Finds the ‘best’ wavelength range from which these models can be built.


Bagging

SWS does not provide a unique model.

To obtain a more robust model, bagging is implemented.

‘Resample and Combine’ method or ‘bagging’ is an algorithm that helps improve the robustness of models by combining predictions

from different models.


Bagging of Models

30 models were generated by changing the initial random seed of the wavelength selection algorithm.

Bagging was applied to the 30 models:

The average value was calculated from the output of the 30 models.

A PLS model was fitted between the real and fitted values to give a weighted average.

X Y


Global and Local Modelling

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0.7Full data set of batch 133

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0.56Second half from the data set of batch 133

Apply Global Modelling

Apply Local Modelling


Total Sugar for 2088

-1 34 69 104 139

LH

To

tal S

ug

ar

Free glucose for 2088

-1 34 69 104 139

LH

Fre

e g

luco

se

Soluble Phospahe for 2088

-1 34 69 104 139

LH

So

l P

ho

sp

hate

2 critical points at 70 and 100 hours were identified from plots

of the biochemical data

Local Modelling


Local ModellingcTitre against Log Hours

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42 52 62 72 82 92 102 112 122 132 142

LH

cT

itre

MS2088 MS2090 MS2092 MS2094 MS2096 MS2098 MS2100

First Time Interval Second Time Interval Third Time Interval


Local Modelling Approach

Three time regions for both the spectra and the quality variable values (titre) were selected.

Samples up to 70 log hours, i.e 175-280 sample points.

From 70 log hours to 100 log hours, i.e 280-400 sample points.

From 100 log hours up to the end of the chosen window, i.e. 400-500 sample points.

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Titre Values 2088 to 2100 for Range[175 280]

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g(1/

R)

Stack Batches 2088 to 2100 for samples 175 to 280


Local Modelling Approach

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Region 2

Region 3


Results : Time Interval 1

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1 Validation Data

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ERRORS EXPERIMENTAL

The RMS of the training set for models 1, 7 and 29 is large.

The RMS of the validation data set for models 1, 7 and 29 is small.

The RMS error for PLS Bagging is smaller than the error of each individual model

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ERRORS VALIDATION

RMS error after PLS Bagging


Linear PLS – Region 1 (Wavelength Selection)

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0.9 Training Data Set - Performing PLS or Averaging

Samples

Titr

e va

lues

Predicted PLS Real Predicted Averaging

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0.9 Validation Data Set - Performing PLS or Averaging

Samples T

itre

valu

es

Predicted PLS Real Predicted Averaging

Training Data Set Validation Data Set



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5

10

15

wavelengths

num

ber o

f app

eara

nces

Frequency of Appearances

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Wavelength/nm

log(1

/R)


The wavelengths between 30 and 40 are selected most frequently.


Neural Network PLS – Region 2 (Wavelength Selection)

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1.3Training Data Set - Performing PLS or Averaging

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Predicted PLSReal Predicted Averaging

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1.3Validation Data Set - Performing PLS or Averaging

SamplesT

itre

valu

es




Polynomial PLS – Region 3 (Wavelength Selection)

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1.4Validation Data Set - Performing PLS or Averaging

Samples

Titr

e va

lues


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1.5Training Data Set - Performing PLS or Averaging

Samples

Titr

e va

lues




Local Modelling : Training Data Set

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1.3Training Data Set - All the Wavelengths- First Batch

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Real Predicted

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es

Real Predicted

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1.1Training Data Set - Taking the Average

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Real Predicted

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Samples

Titre

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Real Predicted

Global Modelling Local Modelling

Global Modelling predictions

Local Modelling predictions

for time intervals 1, 2 and 3


Local Modelling : Validation Data Set

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1.6Validation Data Set - All the Wavelengths- First Batch

Samples

Titre valu

es

Real Predicted

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1.2Training Data Set - Taking the Average-Local Model

Samples

Titre values

Real Predicted

1rst Time Interval2nd Time Interval

3rd Time Interval


Genetic Algorithm Results

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15

wavelengths

num

ber

of

app

ea

ran

ces


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wavelengths

num

ber

of

appeara

nces


Genetic algorithms provide the possibility of selecting individual wavelengths but potentially does not predict future samples well.

SWS Genetic Algorithms


GA Results – Region 2

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Samples

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lues

Real Predicted

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1.3Validation Data Set - Taking the Average

Samples

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Real Predicted

SWS Averaging Ga’S Averaging

RMS of Validation - SWS: 0.048 GAs:0.069


Genetic Algorithm Results

Time Interval 1 Time Interval 2 Time Interval 3

PLS Bagging

Average Bagging

PLS Bagging

Average Bagging

PLS Bagging

Average Bagging

SWS with Linear PLS 0.018 0.034 0.025 0.034 0.039 0.060

GAs with Linear PLS 0.018 0.018 0.023 0.024 0.037 0.038

TRAINING

Time Interval 1 Time Interval 2 Time Interval 3

PLS Bagging

Average Bagging

PLS Bagging

Average Bagging

PLS Bagging

Average Bagging

SWS with Linear PLS 0.045 0.049 0.059 0.048 0.095 0.058

GAs with Linear PLS 0.045 0.043 0.067 0.069 0.177 0.139

VALIDATION RESULTS


Summary of Results

GAs produced slightly better predictions for the training data set resulting in overfitting.

In the validation model, SWS combination with bagging for local modelling gave better results than the GA in combination with bagging.

Local modelling gives better results than global modelling.

SWS with bagging gives better results compared with the purported ‘one-shot wonder’ models.


Design of Experiment Data

Integration of Process and Spectral Data


Conjunction of Process and Spectral Data

In the later stages of the fermentation, the error in the calibration models was observed to be greater with offsets being present.

During this time, significant changes in the fermentation broth concentrations occur.

The offset can potentially be modelled by utilising other process information such as off-gas measurements.


Data Set and Aim

The aim is to infer product concentration and the biochemical components from the spectral data.

Working on the off-line, biochemical and NIR data for the design of experiment batches.

Changing conditions in experimental design:

• Temperature (°C) • pH• Sugar feed (gh-1)• Oil feed (%)



MODEL

SpectralΣ

+

Biochemical Concentration

-

Calibration spectral residuals

MODEL

Process DataΣ

+

Calibration Spectral Residuals

-

Innovations

First Step: Calculation of the calibration spectral residuals.

Second Step: Modelling of the calibration spectral residuals from the process data and the generation of the innovations.

Σ

Biochemical Concentration Predictions by Spectra

Residuals Prediction by Process Data

Final Product Concentrations Final Step: Prediction of the

product concentration



CER

CO2 Total

pH

OUR

Temperature

5 variables were considered to be the most important for the prediction of product concentration

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Time Series Plot

5 pH

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Time Series Plot

2 CER

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Time Series Plot

3 CO2 Total

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Time Series Plot

9 OUR

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26.85

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26.95

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27.05

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27.2


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Predicted train values


Predictions Residuals

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Residuals for training data set

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Predicted valid values

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Final predictions of the product

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Real values, Predicted values and Final predicted values for valid

New residuals

• The off-set is reduced

• The residuals exhibit less structure and reflect noise


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Conclusions

A Spectral Window Selection (SWS) algorithm has been proposed to select a window of wave numbers.

Multiple models are ‘bagged’ to produce a more robust model.

SWS produces better results than when the complete wavelength region is included.

Process data was combined with spectral data to eliminate offsets.

The wavelength selection-bagging approach in combination with the process data is now under investigation.

The results to date are promising.

the conjunction of process and spectral data for enhanced fault detection

Documents

online process data

data setfinal stage

offline data

quality data

process analytics

process performance

integration of process

conjunction of process