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Artificial Intelligence (AI) for Process Optimization

Use of AI for Data Analysis in Maintenance Management

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Course Schedule

15-19 Dec 2025

Barcelona

$5,950

02-06 Feb 2026

Dubai

$5,950

04-08 May 2026

London

$5,950

10-14 Aug 2026

Amsterdam

$5,950

05-09 Oct 2026

Dubai

$5,950

14-18 Dec 2026

Barcelona

$5,950

Course Overview

This training course on Artificial Intelligence (AI) for Process Optimization covers essential aspects of process modelling and optimization and applications of AI techniques. It covers an overview of process optimization and fundamentals of Artificial Intelligence (AI). This includes data acquisition for process optimization, supervised learning for optimization, ERP and SCADA systems. It also includes introduction to machine learning (ML) and AI techniques such as Artificial Neural Networks (ANN), Fuzzy Logic Control (FLC), classification models and random forests..

Practical case studies will be presented to demonstrate the use of AI related techniques in order to utilize the data that exists in Enterprise Resource Planning (ERP), and Computerized Maintenance Management Systems (CMMS) and provide decision support for selection of the most appropriate maintenance strategies.

This GLOMACS Artificial Intelligence (AI) for Process Optimization training course will highlight:

Traditional maintenance vs. process optimization
The role of AI in process optimization
Integration with existing maintenance systems (CMMS, ERP)
Learning from Failures.
Challenges, Ethical Considerations, and Future Trends

What are the Goals?

At the end of this training course, you will learn to:

Understand process optimization techniques.
Learn about ML and AI concepts and trends.
Design and develop methods of utilizing AI in the maintenance domain.
Analyze data and determine most appropriate maintenance polices and actions.
Apply and explain role of AI in process optimization.

Who is this Training Course for?

The training course on AI for Process Optimization is ideal for professionals seeking to leverage AI for reliability engineering and maintenance management skills.

This GLOMACS Artificial Intelligence (AI) for Process Optimization training course is suitable to a wide range of professionals but will greatly benefit:

Process and Operations Engineering
Control Room Supervisors
Maintenance and Reliability Supervisors
Business leaders, directors and managers.
Data analysts, IT professionals

How will this Training Course be Presented?

This training course on Artificial Intelligence (AI) for Process Optimization will utilize various proven learning methodologies to ensure full understanding of comprehension, retention, and ability to apply the knowledge presented. This includes tools and techniques, examples, case studies, and small groups activities.

Course Outline

Day 1

Overview of Process Optimization
What is process optimization?
Key optimization objectives: efficiency, cost reduction, quality improvement
Traditional vs. AI-powered process optimization
Introduction to AI, machine learning (ML), and deep learning (DL)
Overview of supervised, unsupervised, and reinforcement learning
Key AI algorithms for optimization problems (e.g., genetic algorithms, simulated annealing)
AI's role in real-time decision-making and dynamic process optimization

Day 2

Data Acquisition for Process Optimization
Types of data in process optimization (sensor data, operational logs, performance metrics, etc.)
Data sources: IoT devices, enterprise systems (ERP, MES), historical process data
Data storage and integration (cloud, edge computing, databases)
Data Preprocessing and Exploratory Data Analysis (EDA)
Techniques for feature extraction and dimensionality reduction
Visualizing data for insights (correlation heatmaps, time-series plots)
Statistical methods to identify trends, anomalies, and patterns in process data

Day 3

Supervised Learning for Optimization
Classification models for defect detection and process anomalies (SVM, k-NN, random forests)
Key performance indicators (KPIs) and objective functions in optimization problems
Model evaluation: metrics, cross-validation, and bias-variance tradeoff
Unsupervised Learning and Clustering
Clustering methods for process optimization (k-means, hierarchical clustering)
Anomaly detection in unsupervised settings (Isolation Forest, DBSCAN)
Case study: Cluster analysis to identify process inefficiencies

Day 4

Reinforcement Learning for Dynamic Optimization
Introduction to reinforcement learning (RL) and its components (agents, environments, rewards)
Applications of RL in dynamic process optimization (supply chain, scheduling, energy systems)
Evolutionary and Swarm Algorithms
Genetic algorithms for optimization (crossover, mutation, selection)
Simulated annealing for solving global optimization problems
Particle swarm optimization (PSO) for complex process optimization tasks
Case study: Solving a complex multi-variable optimization problem using these algorithms

Day 5

Deploying AI Models in real-world processes
Integration with existing process control and monitoring systems (SCADA, ERP, MES)
Real-time monitoring, feedback loops, and model updates
Scaling AI for industrial applications (cloud vs. edge deployment)
Challenges, ethics, and the future of AI in Process Optimization
Data privacy, security, and ethical considerations in process optimization
Common challenges: data quality, model interpretability, and implementation cost
The future of AI in process optimization: Industry 4.0, digital twins, and autonomous systems

Accreditation

GLOMACS is registered with NASBA as a sponsor of Continuing Professional Education (CPE) on the National Registry of CPE Sponsors. NASBA have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.learningmarket.org.

All Training Seminars delivered by GLOMACS by default are eligible for CPE Credit.

Would you like to take this course as a team?

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