Creating Adaptive Predictions for Tissue Critical Quality Parameters Using Advanced Analytics and Machine LearningPresenter: Cydney RechtinAuthors: C. Rechtin, C. Ranjan, A. Lewis, B. Zarko

Outline• Motivation• Application• Process for creating adaptive and real-time predictions• Value Examples

• Informed decision-making• Change detection• Tuning by learning

MotivationWHY DO PAPERMAKERS CARE?

• Machine direction quality profiles• Process visibility leads to

informed process control decisions:

• Smarter fiber utilization

• Optimized energy and chemical use

• Reduced process variability

• Increased % first quality tonnage

• Improved production rate

• Improved downstream converting

ADAPTIVE & REAL-TIME PREDICTION

• Adaptive: Changes with the process

• Real Time: Predicting the now

Application• Quality ‘soft sensors’ constantly

monitor paper critical quality• On-the-fly vs. reel-to-reel control

• Not limited by lab • Analogous to online

moisture/bw scanner control • No modeling SME required

• Self-tuning predictive models

Real Time Paper Machine Data

Relationship Discovery

Adaptive and Real Time Predictive Modeling

Soft Sensor Prediction

Process For Creating Adaptive and Real-Time Predictions

• Step 1: Collect Important Data

• Step 2: Survey Response and Predictor Data

Process For Creating Adaptive and Real-Time Predictions

• Step 3: Clean Data• Noise treatment, handling missing data,

normalization, etc.• Exclusive characteristics require a unique

set of data cleaning process• Step 4: Data Mining

• Time series cross-sectional (TSCS) data• Response-Predictor relationships are:

• High-dimensional

• Non-linear

• Non-constant

Process For Creating Adaptive and Real-Time Predictions

• Step 5: Predictive Modeling• Paper quality prediction challenges

• High-dimensionality• Spatial and temporal dynamics• Measurement errors• Observational Data

• Overcoming the challenges• Regularization in spatial and temporal

domain• Causal relationship extraction• Adaptive and evolving model

• Utilizing machine learning

Process For Creating Adaptive and Real-Time Predictions

• Step 6: Predictive Model Accuracy• Evaluated in model building phase

• Inherent error in prediction less than lab value High level: �𝜎𝜎𝑖𝑖2 ≤ 𝜎𝜎𝑖𝑖2

• Prediction close to actual value High level: �𝑌𝑌𝑖𝑖 − 𝑌𝑌𝑖𝑖 ≈ 0• Real-time model accuracy evaluated using advanced control chart

• Step 7: Live Connection

Process For Creating Adaptive and Real-Time Predictions

Informed Decision Making Value Creation Example 1

• Raw material optimization• Increased broke utilization—prediction provided confidence in meeting spec• Annualized savings for 5% increase in broke fiber: ~ $500,000

• Basis Weight Optimization• On-the-fly incremental basis

weight reductions• Drive strength to target

• Confidently make on-the-fly control decisions due to real-time strength prediction

• Strength prediction responds as soon as BW is reduced—no need to wait for the lab

• Annualized fiber savings for 2% decrease in BW: ~$500,000

Informed Decision Making Value Creation Example 2

Change Detection Value Creation Example 1

• Stable prediction indicates significant process change during new dry strength chemical trial

• Oscillations in MD Wet Tensile Prediction1. Monitor prediction influential

variables2. Similar oscillations seen in acid

pump (an influential variable at the time of prediction oscillations)

3. Mechanical failure identified in acid pump

4. Maintenance resolved issue and process stabilized

Change Detection Value Creation Example 2

Tuning by Machine Learning Simulation Example

• Real-time machine learning perfectly tunes predictive model for simulated (but real) data over time• Prediction almost identical to

actual even during process shifts

• Prediction confidence very high, thus confidence interval very small

• Note: This would never happen in “real life” as there are always new and changing variations in papermaking!

Thank you! Questions?Contact Info: Cydney Rechtincrechtin@Solenis.com+1-404-416-9861