maximising operational efficiency in process industries with artificial intelligence
TRANSCRIPT
Maximising operational efficiency in process industries with artificial intelligence
│ 8 September 2017
Artificial intelligence (AI) and process industries: a perfect match
- Stiff processes
- Big data
- The culture of experimentation
- “A little optimisation” means a lot of money
From data to value
Provide knowledge for decision support
Knowledge Decisions
Make operational decisionsautomatically
Data Execution
How AI and ML differ from models of physical processes traditionally used in process industries
Processes relying on traditional physical models
Processes relying on traditional physical models
Results of chemical analyses
Equipment telemetry
Process parameters
Processes relying on traditional physical models
Results of chemical analyses
Equipment telemetry
Process parameters
Processes relying on traditional physical models
Results of chemical analyses
Equipment telemetry
Process parameters
Traditional models of physical processes
embedded in process control
systems
Processes relying on traditional physical models
Results of chemical analyses
Equipment telemetry
Process parameters
Traditional models of physical processes
embedded in process control
systems
Expert judgement
Processes relying on traditional physical models
Results of chemical analyses
Equipment telemetry
Process parameters
Traditional models of physical processes
embedded in process control
systems
Expert judgement
L(z)
0 z
Processes relying on traditional physical models
Results of chemical analyses
Equipment telemetry
Process parameters
L(z)
0 z
Does AI replace traditional models?
No, AI doesn’t abolish traditional models.
It complements them and increases their accuracy.
What this AI is good for
Established,repetitive process
Uncertainty in inputs
Well-defined, measurable outcomes
to create value to start quickly to measure success
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Checklist for a process to start using AI
〉The process is important and costly
〉The more complex, the better
〉There’s a KPI that can be measured
〉Enough historical data at hand
〉Experimenting is possible
Use cases in process industries
│ Optimising ferroalloy use │ during steel production
Saving expensive ingredients
Here comes the optimisation
$$$$$$Optimisation potential
$$$Cost savings achieved
Smelting model. Three-steps modeling
Simple (e.g. linear) dependency on the most important features 𝑧:
𝑧 - Values of technical parameters𝑦%- Target (mass percent of chemical element k)𝑧&, 𝑦% - Historical dataset
𝑦% ≈ L(𝑧)
More sophisticated dependency on the whole set of features �⃗�:
𝑦% ≈ F �⃗� =L 𝑧 + M(�⃗�)
Probabilistic final model:1 2 3
Smelting model
Y D F
Prob
abili
ty
Amount of Mn
Permitted chemicalrange
L(z)
0 z 0
OptimisationThe domain of confident
meeting the specificationsThreshold of confidence for
meeting the steel specifications
Dop
ant 2
, kg
Dopant 1, kg
In a certain way it corresponds to the range of the restrictions.
│5% of ferroalloy│costs reduction
│>$4m a year in projected savings
Magnitogorsk Iron & Steel Works
Optimisation of raw material use: other cases
Animal feed production Chocolate production Gold extraction
Optimisation of animal feed production— Complex technological process managed by an operator
— Strict requirements on chemical composition and amount of moisture content
— Goals:
〉To optimise the consumption of raw material, electricity, gas, water, gas, etc.〉To decrease the variability of the
process
Animal feed production process
Raw materials measurements
Milling Preconditioning Extrusion Drying
Process data
Spraying Cooling
Extruder operator Dryer operator
Final product measurements
Server
Optimisation of gold extraction process
〉20-40% is the share of cyanide costs in ore processing
〉To define the optimal amounts of cyanide to be added and its concentration
〉In order to decrease overall cyanide costs while maintaining the levels of gold recovery
Optimisation of chocolate conching process
〉A lot of uncertainties in the process and fluctuations in quality of raw materials
〉To recommend the optimal amount of cocoa butter to be added
〉In order to decrease the consumption of cocoa butter while keeping up with final product quality
│ Timely reaction for │ optimal decisions
Quality prediction
Determining optimal production routes
Route 1
Route 2
Rules based on statistics/
guidelines
Action choice
Production process
Determining optimal production routes
PredictionsProduction
process
Predictions
Route 1
Route 2
Action choice
│Analysed data on │17,000 slabs
│48% of defect slabs │predicted in first │10% of all slabs
Slab quality prediction
│ It’s hard to manage manually │ with precision due to a │ multitude of factors that │ change dynamically
Optimisation of process parameters
Optimisation of moisture content in tobacco〉Use of different additives,
fluctuations in raw materials and time gap after drying affect the outcome
〉Goal: to predict required moisture levels in order to manage speed and temperature of the drying machine
〉Result: 44% decrease in the average error as compared to existing model
Optimisation of diffusion process
〉A certain portion of sugar is lost during its extraction from sliced sugar beets
〉Its amount depends on the operational parameters of the diffuser unit and the ability to adjust them on time
〉Goal: to increase throughput (sugar recovery) of diffuser unit
Optimisation of gas fractionation
〉Some parameters should be adjusted before the chemical composition of stream is known
〉Changing the operating mode too fast may lead to disruptions
〉Some mistakes of raw processing cannot be fixed later
〉Goal: to improve energy efficiency while maintaining high throughput
How AI is used by other process manufacturers
Production efficiency optimisation: Hershey saved $500,000 (on one machine)
Anomaly detection in beer fermentation process: Deschutes Brewery Inc.
Automatic classification of nutritional deficiencies in coffee plant (using computer vision)
How AI is used by other process manufacturers
〉Calving prediction from activity, lying, and ruminating behaviors in dairy cattle
〉Prediction of insemination outcomes in Holstein dairy cattle
Other cases in dairy production:
Practical issues of AI implementation
Level 2 Process Control (DCS / SCADA / APC)
How AI solutions are integrated
Operator interface
Control execution (Level 1)
Production process
Sensors, real-time process data
Existing process control environment
Controlled KPIs
Manipulated variables, commands
Level 2 Process Control (DCS / SCADA / APC)
How AI solutions are integrated
Operator interface
Control execution (Level 1)
Production process
Sensors, real-time process data
Existing process control environment
Controlled KPIs
Manipulated variables, commands
AI-based model (no interface)
Prescriptions
Recommendations
Model KPIs
Why you should use artificial intelligence
No capital investments
No disruption of existing process
3-6 months to implement
Immediate ROI
Capital investments
Process redesign
Lengthy deployment
ROI in 5-10 years
How to get started? Project plan
Stage Scope Timeframe
Preliminary phase
– Confirmation of the details of the technological process (input - output parameters)– Data transfer– Preliminary data analysis– Preparation of the individual project plan
1 month
Service development and integration
– Development and optimisation of the machine learning model– Service integration with existing customer software
2 months
Pilot– Experimental testing of the service– Measurement of the economic effect 1 month
Commercial use – Regular support and quality monitoring, including model quality updates
1 year +
.
