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DI-003

Artificial Intelligence (AI) for Process Optimization

Intensive 5-day course · English, Arabic & European languages

Process engineers and maintenance teams examine artificial intelligence (AI) for process modelling and optimisation over five days. The course covers machine learning (ML), data preparation and model deployment, using case studies to explore how information held in ERP, SCADA and CMMS systems can support maintenance decisions.

Classroom

Public courses in our cities

  • 26–30 Oct 2026Riyadh£4,200 + VAT
  • 9–13 Nov 2026Barcelona£3,080 + VAT
  • 23–27 Nov 2026Doha£4,200 + VAT
  • 14–18 Dec 2026Paris£3,600 + VAT
  • 28 Dec – 1 Jan 2026Abu Dhabi£4,200 + VAT
  • 4–8 Jan 2027Barcelona£3,080 + VAT
Show 23 more dates
  • 18–22 Jan 2027Manama£3,080 + VAT
  • 1–5 Feb 2027London£3,360 + VAT
  • 15–19 Feb 2027Amman£3,400 + VAT
  • 8–12 Mar 2027Munich£3,400 + VAT
  • 22–26 Mar 2027Madrid£3,400 + VAT
  • 12–16 Apr 2027Barcelona£3,080 + VAT
  • 26–30 Apr 2027Berlin£3,400 + VAT
  • 10–14 May 2027Paris£3,600 + VAT
  • 24–28 May 2027Milan£3,400 + VAT
  • 7–11 Jun 2027Barcelona£3,080 + VAT
  • 21–25 Jun 2027Malaga£3,080 + VAT
  • 5–9 Jul 2027London£3,360 + VAT
  • 19–23 Jul 2027Sarajevo£3,400 + VAT
  • 9–13 Aug 2027Munich£3,400 + VAT
  • 23–27 Aug 2027Dubai£4,200 + VAT
  • 13–17 Sep 2027Barcelona£3,080 + VAT
  • 27 Sep – 1 Oct 2027Riyadh£4,200 + VAT
  • 11–15 Oct 2027Paris£3,600 + VAT
  • 25–29 Oct 2027Doha£4,200 + VAT
  • 1–5 Nov 2027Barcelona£3,080 + VAT
  • 15–19 Nov 2027Geneva£4,200 + VAT
  • 6–10 Dec 2027London£3,360 + VAT
  • 20–24 Dec 2027Abu Dhabi£4,200 + VAT

Group discount: 2 delegates 20% · 3 delegates 35% · 5 or more delegates 50%

Can't make these dates? We can run this course for your team on dates that suit you. Ask for custom dates · In-house options

Online (live)

With a trainer, on dates to suit your team

This course can be taught live online by a trainer, in English, Arabic or any European language, for one person or a whole team.

£1,850 + VAT

Group discount: 2 delegates 20% · 3 delegates 35% · 5 or more delegates 50%

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Artificial Intelligence (AI) for Process Optimization

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Course overview

Maintenance decisions depend on usable process data. Delegates examine traditional maintenance and AI applications, including artificial neural networks (ANN), fuzzy logic control (FLC) and classification models, before considering how these approaches inform maintenance policies. Case studies use information already held in enterprise resource planning (ERP) and computerised maintenance management systems (CMMS).

Process modelling begins with data acquisition and preparation. The syllabus moves through supervised learning, clustering and advanced optimisation algorithms to deployment alongside supervisory control and data acquisition (SCADA) systems. Learning from failures is part of the discussion. Delegates consider model monitoring, ethical questions and the costs and threats associated with adopting AI.

The organisational discussion connects optimisation with profitability, operational efficiency and competitive position within digital transformation. Exercises develop critical thinking and predictive problem-solving, with attention to decision analysis and the use of existing data. Learning and innovation are considered alongside future developments, including digital twins and autonomous systems.

Course objectives

By the end of the course, delegates will be able to:

  1. Understand modern process optimisation techniques
  2. Explain the concepts and trends behind machine learning and AI
  3. Design and develop AI methods for maintenance work
  4. Analyse data to select maintenance policies and actions
  5. Apply AI to process optimisation and explain its role

Who should attend

This course suits:

  • Process and operations engineering staff
  • Control room supervisors
  • Maintenance and reliability supervisors
  • Business leaders, directors, and managers
  • Data analysts and IT professionals

Training methodology

  • Tools and techniques are explained through practical examples.
  • Case studies and activities in small groups support the application of learning.

Daily agenda

  1. Day 1

    Introduction to Process Optimisation and Fundamentals

    • Overview of process optimisation
    • Defining core process improvement concepts
    • Efficiency, lower costs and better quality as optimisation objectives
    • Traditional optimisation methods and AI applications
    • Foundations of AI, machine learning (ML) and deep learning (DL)
    • Supervised, unsupervised, and reinforcement learning
    • Optimisation algorithms including genetic algorithms and simulated annealing
    • AI for real-time decisions and dynamic process optimisation
  2. Day 2

    Data Collection, Preprocessing, and Analysis

    • Data acquisition for process optimisation
    • Sensor readings, operational logs and performance measures
    • Information from IoT devices, ERP and MES systems, and historical process records
    • Storing and integrating data through cloud services, edge computing and databases
    • Preprocessing and exploratory data analysis (EDA)
    • Feature extraction and dimensionality reduction
    • Correlation heatmaps and time-series plots for exploring data
    • Statistical identification of patterns, trends and anomalies
  3. Day 3

    Machine Learning Models for Optimisation

    • Supervised learning for process improvement
    • SVM, k-NN and random forests for classifying defects and process anomalies
    • Key performance indicators and objective functions
    • Model assessment through metrics, cross-validation and the bias-variance trade-off
    • Unsupervised learning and its use in clustering
    • Applying k-means and hierarchical clustering to processes
    • Anomaly detection using Isolation Forest and DBSCAN
    • Case study: Using cluster analysis to find inefficiencies
  4. Day 4

    Advanced Techniques for Process Optimisation

    • Reinforcement learning (RL) for dynamic environments
    • Agents, environments and rewards in reinforcement learning
    • RL applications in supply chains, scheduling and energy systems
    • Evolutionary and swarm algorithms
    • Crossover, mutation and selection in genetic algorithms
    • Simulated annealing for global optimisation
    • Particle swarm optimisation (PSO) for detailed tasks
    • Case study: Solving a multi-variable problem
  5. Day 5

    Deployment, Monitoring, and Future Trends

    • Deploying models into real-world processes
    • Connecting models to SCADA, ERP and MES control and monitoring systems
    • Live monitoring, feedback loops and updates to models
    • Industrial scaling with cloud and edge deployment
    • Challenges and future directions for AI in process optimisation
    • Ethics, data privacy, and security considerations
    • Data quality, model interpretability and implementation costs
    • Industry 4.0, digital twins and autonomous systems in future process optimisation

Certificate

Every delegate who completes the course receives an ARUK Training Certificate of Attendance.

Frequently asked questions

How do I register?

Choose a date and city in the schedule and press Register, or send us an enquiry. Our team confirms your place and sends an invoice.

Are fees shown with VAT?

Fees are shown per delegate and exclude VAT, which is added to the invoice where applicable.

Can this course be delivered for our team only?

Yes. Any ARUK course can be delivered in-house at your premises, in a meeting room we book for you, or 1-to-1.

Is this course available online?

Yes. We run it live online with a trainer, on dates that suit your team, for £1,850 + VAT per delegate.

Is there a group discount?

Yes. 20% for 2 delegates, 35% for 3 delegates and 50% for 5 or more delegates on the same course.

Can the course be delivered in Arabic or another language?

Yes. Our courses can be delivered in English, Arabic or any European language, by a trainer who teaches in that language. Mention your preferred language when you register or enquire.

What certificate will I receive?

An ARUK Training Certificate of Attendance.

Related courses

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Artificial Intelligence (AI) for Process Optimization

Key booking terms (read before you register)
  • The full fee is paid in advance, before the course starts.
  • Cancel in writing 30 or more days before the start date (45 for in-house) for a refund. Later than that, the full fee is payable.
  • No refund for non-attendance or partial attendance.
  • Places are for the named delegate only; another person cannot attend instead.
  • Moving to another date or city is only possible if ARUK agrees in writing.
  • Travel, accommodation and visas are your responsibility. A refused visa does not change the cancellation terms.

booking terms and conditions

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Artificial Intelligence (AI) for Process Optimization