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 +

.

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