AI and machine learning: Using Machine Learning to Control Single-Pass Tangential Flow Filtration in Biopharmaceutical Processing
Bastian Oetomo, University of Melbourne, Australia

Bastian will explore the use of machine learning and AI to optimise single-pass tangential flow filtration (SPTFF) in ultrafiltration/diafiltration, aiming to meet stringent quality standards and support the shift towards continuous bioprocessing through greater automation and intelligence.

AI and machine learning: A Hybrid Machine Learning Assisted Modelling Framework Across the Scales for Heat Transfer Systems 
Nima Nazemzadeh, Hexxcell, UK

This study demonstrates hybrid modelling frameworks that combine data-driven methods with first-principles to improve prediction accuracy and reliability for complex heat transfer challenges, specifically condensation in microfin tubes and fouling in industrial-scale heat exchangers.​

Digital twin technology: Hybrid Modelling and Optimisation Framework for Plant-Wide Real-Time Applications
Patrick Thorpe, Spiro, UK

Patrick will introduce a hybrid modelling and optimisation framework combining simplified physical and AI-based unit models for real-time plant-wide optimisation in complex facilities, illustrated through a refinery case study and practical deployment insights.

Digital twin technology: Bioprocessing 4.0: Minimising Cost of mRNA Vaccine Manufacturing
Kesler Isoko, University College London / University of Sheffield, UK

Kesla will showcase the first end-to-end Bioprocessing 4.0 solution for mRNA vaccine production, featuring a physics-informed soft sensor and dynamic model integrated into an IIoT platform, achieving significant reagent and cost reductions while maintaining product quality.

Advanced process automation: Showcase of Advanced Regulatory Control in British Sugar
James Caws, British Sugar, UK

Discover how British Sugar is solving complex process challenges by enhancing its existing DCS with advanced regulatory control strategies. James presents compelling case studies showing how these traditional approaches continue to drive significant industrial value – even amid growing interest in AI technologies.

Advanced process automation: Introducing ORTO – A Novel Approach Real-time Optimisation
Paul Oram, Ortomation, UK

Hear from Paul as he outlines Ortomation Ltd’s novel agent-based real-time optimisation (RTO) approach, designed to overcome the cost, complexity, and resource challenges of traditional RTO methods and enable widespread industrial adoption.

Digital twin technology: Achieving Heat Transfer Equipment Reliability in the Process Industry Through Digital Transformation with HTRIconnect and SmartPM 
Edward Ishiyama, HTRI, UK

Hear how HTRI’s digital platforms – HTRIconnect™ and SmartPM™ – leverage validated digital twins and machine learning to provide real-time monitoring, predictive analytics, and optimisation of heat exchanger performance, enhancing productivity, safety, and sustainability across diverse industries.

Digital twin technology: Transforming Process Operations in NLNG through Digital Twin - Enabled Predictive Intelligence 
Amin Muhammed Muhammed, Nigeria LNG (NLNG), Nigeria

Find out how NLNG utilises Digital Twin technology to optimise operations and enhance efficiency by integrating visualisation, information management, and predictive analytics, enabling proactive maintenance and smarter decision-making through advanced machine learning and full plant digitalisation.

Advanced process automation: Replacing Missing Instrumentation with Data Science: Calculating Virtual Flow Rates for Chemical Injection
Murray Calander, Eigen, UK

Murray introduces an innovative digital twin solution that estimates chemical flow and dosage in real time using existing tank level and fluid rate data. Ideal for mature plants without flow meters, it enhances dosing accuracy, minimises chemical waste, and reduces risk – all without adding new instrumentation.

Advanced process automation: Digital Twins and Process Control with Software-defined Process Automation
Raghav Tripathi, Siemens, UK

Discover how Siemens is revolutionising process control through software-defined automation, blending real and digital worlds to achieve IT and OT convergence, with a focus on their web-based distributed control system delivering high availability, flexible deployment, and enhanced connectivity to optimise operations and drive sustainable growth.

AI and machine learning: Machine Learning and Hybrid Modelling of Particle Breakage in a Jet Mill
Carl Jackson, Johnson Matthey, UK

Showcasing a novel hybrid modelling strategy, Carl explores how combining machine learning with population balance modelling enhances particle size reduction processes in jet mills – driving performance in demanding pharmaceutical and fine chemical applications.

AI and machine learning: Digitalising Experimental Design: Automating Process R&D Using Machine Learning
Tom Whitehead, Intellegens, UK

By applying machine learning through Adaptive Experimental Design (AED), Tom demonstrates how experimental effort in chemical process development can be reduced by 50–80%. The benefits are explored via a case study focused on optimising oligonucleotide synthesis.

Data management and integration: The Process Industry Neurology Project
Chris Hamlin, HancockHamlin, UK

Despite significant investments and efforts, digital transformation in the process industries remains elusive, with many AI initiatives failing to deliver. This presentation examines these challenges through Stafford Beer’s Viable Systems Model, providing valuable insights into organisational dynamics and proposing more effective approaches.

Data management and integration: Enhancing Data Management, Integration, and Digital Risk Strategies
Gavin Statham, adi Life Sciences, UK

Discover how process-industry leaders are replacing outdated paper workflows with integrated digital ecosystems. This session highlights key technologies—including smart forms, IoT data capture, and digital twins – that improve compliance, efficiency, and cybersecurity. A case study and actionable insights will equip delegates with strategies for managing digital risk and enabling scalable, future-proof transformation.

Data management and integration: Bringing Consequence Modelling to the Cloud: Unlocking Real-Time Risk Insights with Phast APIs
James Pickles, DNV, UK

Hear from James as he outlines how cloud-based access to DNV’s Phast consequence models via secure APIs enables seamless integration with digital twins and IoT platforms, supporting real-time, risk-based decision-making and advancing digital risk management throughout the asset lifecycle.

Data management and integration: Digitalisation Practice Applied to Tablet Packing
Anthony Margetts, Factorytalk, UK

Find out how the Manufacturing Technology Centre in Liverpool has developed an automatic tablet packing line, showcasing the use of digitalisation to address practical challenges in medicines packaging.

Advanced process automation: Monitoring and Evaluation of the In-situ Consolidation of Prepreg Layers During Manufacturing Stage to Improve Interlaminar Bond Strength by Optimisation of Parameters
Obinna Okolie, Robert Gordon University, UK

Introducing a sensor-integrated in-situ consolidation technique, this study employs real-time data and optimisation to enhance bond quality and reduce defects in thermoplastic composites – paving the way for intelligent, data-driven manufacturing.

Advanced process automation: Optimisation of Mixed Refrigerant Loop in LNG Plant Facilities Using Aspen Hysys Software
Goulmane Samir, Oran, Algeria

Using Aspen HYSYS, this study optimises the mixed refrigerant cycle with propane pre-cooling in LNG production, resulting in lower compressor energy demand and more efficient refrigerant use – delivering estimated annual savings of $250K per LNG train.

Advanced process automation: Optimising PID Control with Proven Tuning and Monitoring Solutions
Damien Munroe, Control Station, Ireland

Explore Control Station’s cutting-edge PID optimisation and analytics solutions – PlantESP™ for advanced process monitoring and Loop-Pro™ for precise controller tuning – that drive enhanced performance, efficiency, and sustainability in process manufacturing.

Digital risk management strategies: Towards Continuous Risk Assessment in Industrial Operations: Leveraging Digital Transformation to Overcome Human and Temporal Limitations
Moataz Nabil Hashem, Yara, Netherlands

An introduction to a Continuous Risk Assessment model that uses real-time sensor and control system data to dynamically identify and evaluate risks, offering a scalable alternative to static assessments and enabling improved safety outcomes, reduced TRI rates, and more informed decision-making in industrial operations.

How to make the most out of your heat exchangers with Hybrid-AI Digital Twins
Delivered by Hexxcell

Hexxcell will lead a workshop on unlocking the full potential of heat exchangers with Hybrid-AI Digital Twins. Drawing on decades of experience in developing and deploying digital technology for industrial thermal systems, the session will provide practical technical and organisational recommendations that participants can take back to their plants to successfully implement digital projects that boost performance, cut costs, reduce emissions, and extend equipment life.

AI and machine learning: Smart Reactor Temperature Predictor for Diesel Hydrotreating (DHT) Unit
Rowin Kumeresen, PETRONAS, Malaysia

To improve efficiency and consistency in ultra-low sulphur diesel production, this presentation introduces an advanced analytics model that dynamically recommends optimal reactor temperatures in hydrotreating units. By adapting to varying feedstocks in real time, the model reduces operator burden, minimises off-spec product and quality giveaways, boosts diesel yield, lowers fuel gas consumption, and cuts carbon emissions.

AI and machine learning: AI, Ethics and Workforce Transformation: Preparing Engineers for Digital Manufacturing Challenges
Mo Zandi, University of Sheffield, UK

Join Mo as he explores the ethical and workforce implications of AI adoption in the chemical process industry, highlighting challenges such as job displacement and training gaps. Drawing on insights from global experts, he proposes a framework to guide fair and transparent AI deployment and informs the redesign of engineering education to prepare professionals for ethical decision-making in digital manufacturing.

AI and machine learning: Understanding How AI is Used by Industry in HSE Regulated Sectors
Freya Anderson, Health and Safety Executive, UK

Drawing on a scoping study of 247 AI use cases spanning 14 sectors, this HSE Science Division presentation identifies major health and safety risks and assurance techniques, providing actionable guidance to help industries build robust AI governance frameworks.

Digital risk management strategies: Towards an Agentic AI Assistant for Safety Barrier Risk Management
Joel Chacon, Eigen, UK

Discover an AI chatbot built with Retrieval Augmented Generation and a graph-based Safety Critical Elements model, delivering real-time, conversational insights into process safety barrier status. This presentation highlights how Large Language Models enable smarter, safer decisions in hazardous environments.

Digital twin technologies: NLNG's Digital Twin Achieving Process Digitalisation Excellence
Emmanuel Nwachukwu, Nigeria LNG (NLNG), Nigeria

NLNG’s journey to digitalisation excellence, powered by the Aveva Digital Twin, achieved major operational and environmental milestones – $6 million in downtime savings and a 15% cut in CO₂ emissions. This presentation provides actionable insights on delivering value and evolving towards AI-integrated optimisation.

Digital twin technologies: Evaluating the Impact of Local Policy and Techno-Economic Drivers on Decision Making Using Digital Twins
Monica Tirapelle, Hexxcell, UK

This work demonstrates how Hybrid-AI Digital Twins can improve refinery decision-making by integrating technical, economic, and policy data to optimise preheat train cleaning strategies. Through regional comparisons, it shows how local factors like fuel cost, CO₂ policy, and profit margins influence maintenance planning, enabling more informed, cost-effective, and sustainable operations.